Upload 65 files

.env-example

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+# API 提供商 (openai SDK)
+API_PROVIDER=openai #使用openai格式的api即可，无需修改
+
+# OpenAI API 配置 使用openai格式的api
+OPENAI_API_URL=https://free.v36.cm/v1
+OPENAI_API_KEY=sk-tLB1LCAGfBVMW1mt54F1A5026dD246E582809454Ea93E430
+OPENAI_API_MODEL=gpt-4o-mini
+NEWS_MODEL=your_open_news_model
+EMBEDDING_MODEL=text-embedding-3-small
+
+
+# 其他新闻获取接口 Serp 和 tavily（免费1000次/月） 二选一即可，如果都选，都会使用
+SERP_API_KEY=e8956fd01e58d7a067b996a9af460bddf4ddd272 # Serp key申请地址：https://serper.dev/api-key
+TAVILY_API_KEY=your_tavily_api_key # tavily key申请地址：https://app.tavily.com/playground
+FINNHUB_API_KEY=your_finnhub_api_key # Finnhub key申请地址: https://finnhub.io/dashboard
+
+FUNCTION_CALL_MODEL=your_function_call_model
+
+# QA上下文数量
+MAX_QA=10
+
+# 安全配置
+# API_KEY=sk-tLB1LCAGfBVMW1mt54F1A5026dD246E582809454Ea93E430
+# HMAC_SECRET=your_hmac_secret_key_for_webhook_verification
+# ALLOWED_ORIGINS=http://localhost:8888,https://your-domain.com
+
+# Redis缓存设置(可选)
+# REDIS_URL=redis://redis:6379  #docker配置
+REDIS_URL=redis://localhost:6379
+USE_REDIS_CACHE=False
+
+# 数据库设置(可选)
+# DATABASE_URL=sqlite:///app/data/stock_analyzer.db  #docker配置
+DATABASE_URL=sqlite:///data/stock_analyzer.db
+USE_DATABASE=False
+
+# 日志配置
+# 可选的日志级别: DEBUG, INFO, WARNING, ERROR, CRITICAL
+LOG_LEVEL=INFO
+LOG_FILE=logs/stock_analyzer.log

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+app/web/static/favicon.ico filter=lfs diff=lfs merge=lfs -text
+images/1.png filter=lfs diff=lfs merge=lfs -text
+images/2.png filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Python
+__pycache__/
+**/__pycache__/
+*/__pycache__/*
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+# Virtual Environment
+venv/
+env/
+ENV/
+myenv/
+myenv2/
+.env
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+.DS_Store
+
+# Logs
+*.log
+logs/
+log/
+
+# Database
+*.db
+*.sqlite3
+*.sqlite
+data/
+
+# Docker
+.docker/
+
+# Test
+.coverage
+htmlcov/
+.pytest_cache/
+.tox/
+
+# Distribution
+*.tar.gz
+*.zip
+
+# Cache
+.cache/
+.pytest_cache/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*.ipynb
+
+# Local development settings
+local_settings.py
+
+# Redis
+dump.rdb
+
+# Other
+*.bak
+*.tmp
+*.temp
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local 
+
+# md
+CLAUDE.md
+akshare.md
+.server.pid
+
+eval_results/

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/StockAnal.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="HtmlUnknownAttribute" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="myValues">
+        <value>
+          <list size="1">
+            <item index="0" class="java.lang.String" itemvalue="market_type" />
+          </list>
+        </value>
+      </option>
+      <option name="myCustomValuesEnabled" value="true" />
+    </inspection_tool>
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="147">
+            <item index="0" class="java.lang.String" itemvalue="chardet" />
+            <item index="1" class="java.lang.String" itemvalue="openai" />
+            <item index="2" class="java.lang.String" itemvalue="linkai" />
+            <item index="3" class="java.lang.String" itemvalue="qrcode" />
+            <item index="4" class="java.lang.String" itemvalue="pre-commit" />
+            <item index="5" class="java.lang.String" itemvalue="HTMLParser" />
+            <item index="6" class="java.lang.String" itemvalue="PyQRCode" />
+            <item index="7" class="java.lang.String" itemvalue="web.py" />
+            <item index="8" class="java.lang.String" itemvalue="requests" />
+            <item index="9" class="java.lang.String" itemvalue="Pillow" />
+            <item index="10" class="java.lang.String" itemvalue="zhipuai" />
+            <item index="11" class="java.lang.String" itemvalue="cprint" />
+            <item index="12" class="java.lang.String" itemvalue="pillow" />
+            <item index="13" class="java.lang.String" itemvalue="pyymal" />
+            <item index="14" class="java.lang.String" itemvalue="qianfan" />
+            <item index="15" class="java.lang.String" itemvalue="pydub" />
+            <item index="16" class="java.lang.String" itemvalue="flask" />
+            <item index="17" class="java.lang.String" itemvalue="pilk" />
+            <item index="18" class="java.lang.String" itemvalue="httpx" />
+            <item index="19" class="java.lang.String" itemvalue="yarg" />
+            <item index="20" class="java.lang.String" itemvalue="PyYAML" />
+            <item index="21" class="java.lang.String" itemvalue="pickleshare" />
+            <item index="22" class="java.lang.String" itemvalue="defusedxml" />
+            <item index="23" class="java.lang.String" itemvalue="executing" />
+            <item index="24" class="java.lang.String" itemvalue="Pygments" />
+            <item index="25" class="java.lang.String" itemvalue="bleach" />
+            <item index="26" class="java.lang.String" itemvalue="soupsieve" />
+            <item index="27" class="java.lang.String" itemvalue="comtypes" />
+            <item index="28" class="java.lang.String" itemvalue="jsonschema" />
+            <item index="29" class="java.lang.String" itemvalue="pywin32" />
+            <item index="30" class="java.lang.String" itemvalue="pydantic" />
+            <item index="31" class="java.lang.String" itemvalue="asgiref" />
+            <item index="32" class="java.lang.String" itemvalue="click" />
+            <item index="33" class="java.lang.String" itemvalue="nbconvert" />
+            <item index="34" class="java.lang.String" itemvalue="attrs" />
+            <item index="35" class="java.lang.String" itemvalue="jedi" />
+            <item index="36" class="java.lang.String" itemvalue="pure-eval" />
+            <item index="37" class="java.lang.String" itemvalue="regex" />
+            <item index="38" class="java.lang.String" itemvalue="jupyterlab_pygments" />
+            <item index="39" class="java.lang.String" itemvalue="pydantic_core" />
+            <item index="40" class="java.lang.String" itemvalue="asttokens" />
+            <item index="41" class="java.lang.String" itemvalue="google-ai-generativelanguage" />
+            <item index="42" class="java.lang.String" itemvalue="platformdirs" />
+            <item index="43" class="java.lang.String" itemvalue="gTTS" />
+            <item index="44" class="java.lang.String" itemvalue="httpcore" />
+            <item index="45" class="java.lang.String" itemvalue="idna" />
+            <item index="46" class="java.lang.String" itemvalue="referencing" />
+            <item index="47" class="java.lang.String" itemvalue="rsa" />
+            <item index="48" class="java.lang.String" itemvalue="decorator" />
+            <item index="49" class="java.lang.String" itemvalue="ephem" />
+            <item index="50" class="java.lang.String" itemvalue="pandocfilters" />
+            <item index="51" class="java.lang.String" itemvalue="pyttsx3" />
+            <item index="52" class="java.lang.String" itemvalue="pkgutil_resolve_name" />
+            <item index="53" class="java.lang.String" itemvalue="pyasn1" />
+            <item index="54" class="java.lang.String" itemvalue="sniffio" />
+            <item index="55" class="java.lang.String" itemvalue="websocket-client" />
+            <item index="56" class="java.lang.String" itemvalue="exceptiongroup" />
+            <item index="57" class="java.lang.String" itemvalue="stack-data" />
+            <item index="58" class="java.lang.String" itemvalue="zipp" />
+            <item index="59" class="java.lang.String" itemvalue="ntchat" />
+            <item index="60" class="java.lang.String" itemvalue="grpcio-status" />
+            <item index="61" class="java.lang.String" itemvalue="pyee" />
+            <item index="62" class="java.lang.String" itemvalue="annotated-types" />
+            <item index="63" class="java.lang.String" itemvalue="importlib_metadata" />
+            <item index="64" class="java.lang.String" itemvalue="tornado" />
+            <item index="65" class="java.lang.String" itemvalue="pyasn1_modules" />
+            <item index="66" class="java.lang.String" itemvalue="et-xmlfile" />
+            <item index="67" class="java.lang.String" itemvalue="mistune" />
+            <item index="68" class="java.lang.String" itemvalue="Django" />
+            <item index="69" class="java.lang.String" itemvalue="typing_extensions" />
+            <item index="70" class="java.lang.String" itemvalue="cachetools" />
+            <item index="71" class="java.lang.String" itemvalue="multidict" />
+            <item index="72" class="java.lang.String" itemvalue="yarl" />
+            <item index="73" class="java.lang.String" itemvalue="pytz" />
+            <item index="74" class="java.lang.String" itemvalue="webencodings" />
+            <item index="75" class="java.lang.String" itemvalue="tiktoken" />
+            <item index="76" class="java.lang.String" itemvalue="traitlets" />
+            <item index="77" class="java.lang.String" itemvalue="protobuf" />
+            <item index="78" class="java.lang.String" itemvalue="arrow" />
+            <item index="79" class="java.lang.String" itemvalue="googleapis-common-protos" />
+            <item index="80" class="java.lang.String" itemvalue="huggingface-hub" />
+            <item index="81" class="java.lang.String" itemvalue="jiter" />
+            <item index="82" class="java.lang.String" itemvalue="python-dateutil" />
+            <item index="83" class="java.lang.String" itemvalue="baidu-aip" />
+            <item index="84" class="java.lang.String" itemvalue="h11" />
+            <item index="85" class="java.lang.String" itemvalue="nbclient" />
+            <item index="86" class="java.lang.String" itemvalue="MarkupSafe" />
+            <item index="87" class="java.lang.String" itemvalue="tinycss2" />
+            <item index="88" class="java.lang.String" itemvalue="frozenlist" />
+            <item index="89" class="java.lang.String" itemvalue="fsspec" />
+            <item index="90" class="java.lang.String" itemvalue="docopt" />
+            <item index="91" class="java.lang.String" itemvalue="filelock" />
+            <item index="92" class="java.lang.String" itemvalue="backports.zoneinfo" />
+            <item index="93" class="java.lang.String" itemvalue="pyzmq" />
+            <item index="94" class="java.lang.String" itemvalue="certifi" />
+            <item index="95" class="java.lang.String" itemvalue="anthropic" />
+            <item index="96" class="java.lang.String" itemvalue="anyio" />
+            <item index="97" class="java.lang.String" itemvalue="google-api-core" />
+            <item index="98" class="java.lang.String" itemvalue="beautifulsoup4" />
+            <item index="99" class="java.lang.String" itemvalue="tokenizers" />
+            <item index="100" class="java.lang.String" itemvalue="image" />
+            <item index="101" class="java.lang.String" itemvalue="nodeenv" />
+            <item index="102" class="java.lang.String" itemvalue="jupyter_client" />
+            <item index="103" class="java.lang.String" itemvalue="backcall" />
+            <item index="104" class="java.lang.String" itemvalue="virtualenv" />
+            <item index="105" class="java.lang.String" itemvalue="charset-normalizer" />
+            <item index="106" class="java.lang.String" itemvalue="distlib" />
+            <item index="107" class="java.lang.String" itemvalue="pypiwin32" />
+            <item index="108" class="java.lang.String" itemvalue="cfgv" />
+            <item index="109" class="java.lang.String" itemvalue="distro" />
+            <item index="110" class="java.lang.String" itemvalue="matplotlib-inline" />
+            <item index="111" class="java.lang.String" itemvalue="async-timeout" />
+            <item index="112" class="java.lang.String" itemvalue="pysilk-mod" />
+            <item index="113" class="java.lang.String" itemvalue="wcwidth" />
+            <item index="114" class="java.lang.String" itemvalue="jupyter_core" />
+            <item index="115" class="java.lang.String" itemvalue="Jinja2" />
+            <item index="116" class="java.lang.String" itemvalue="types-python-dateutil" />
+            <item index="117" class="java.lang.String" itemvalue="sqlparse" />
+            <item index="118" class="java.lang.String" itemvalue="jsonschema-specifications" />
+            <item index="119" class="java.lang.String" itemvalue="google-generativeai" />
+            <item index="120" class="java.lang.String" itemvalue="rpds-py" />
+            <item index="121" class="java.lang.String" itemvalue="LunarCalendar" />
+            <item index="122" class="java.lang.String" itemvalue="urllib3" />
+            <item index="123" class="java.lang.String" itemvalue="croniter" />
+            <item index="124" class="java.lang.String" itemvalue="six" />
+            <item index="125" class="java.lang.String" itemvalue="identify" />
+            <item index="126" class="java.lang.String" itemvalue="importlib_resources" />
+            <item index="127" class="java.lang.String" itemvalue="prompt_toolkit" />
+            <item index="128" class="java.lang.String" itemvalue="parso" />
+            <item index="129" class="java.lang.String" itemvalue="nbformat" />
+            <item index="130" class="java.lang.String" itemvalue="tzdata" />
+            <item index="131" class="java.lang.String" itemvalue="ipython" />
+            <item index="132" class="java.lang.String" itemvalue="packaging" />
+            <item index="133" class="java.lang.String" itemvalue="pipreqs" />
+            <item index="134" class="java.lang.String" itemvalue="fastjsonschema" />
+            <item index="135" class="java.lang.String" itemvalue="tqdm" />
+            <item index="136" class="java.lang.String" itemvalue="colorama" />
+            <item index="137" class="java.lang.String" itemvalue="proto-plus" />
+            <item index="138" class="java.lang.String" itemvalue="aiohttp" />
+            <item index="139" class="java.lang.String" itemvalue="grpcio" />
+            <item index="140" class="java.lang.String" itemvalue="aiosignal" />
+            <item index="141" class="java.lang.String" itemvalue="google-auth" />
+            <item index="142" class="java.lang.String" itemvalue="openpyxl" />
+            <item index="143" class="java.lang.String" itemvalue="bs4" />
+            <item index="144" class="java.lang.String" itemvalue="lxml" />
+            <item index="145" class="java.lang.String" itemvalue="uvicorn" />
+            <item index="146" class="java.lang.String" itemvalue="gunicorn" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N801" />
+        </list>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="time.error" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.11" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11" project-jdk-type="Python SDK" />
+  <component name="PyCharmProfessionalAdvertiser">
+    <option name="shown" value="true" />
+  </component>
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/StockAnal.iml" filepath="$PROJECT_DIR$/.idea/StockAnal.iml" />
+    </modules>
+  </component>
+</project>

Dockerfile

@@ -0,0 +1,40 @@
+# 使用Python 3.11基础镜像（因为你的依赖包兼容性更好）
+FROM python:3.11-slim
+
+# 设置工作目录
+WORKDIR /app
+
+# 创建数据和日志目录
+RUN mkdir -p /app/data /app/logs
+
+# 设置环境变量
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+# 更新源列表并更换为阿里云源
+RUN echo 'deb http://mirrors.aliyun.com/debian/ bookworm main' > /etc/apt/sources.list && \
+    echo 'deb-src http://mirrors.aliyun.com/debian/ bookworm main' >> /etc/apt/sources.list && \
+    echo 'deb http://mirrors.aliyun.com/debian/ bookworm-updates main' >> /etc/apt/sources.list && \
+    echo 'deb-src http://mirrors.aliyun.com/debian/ bookworm-updates main' >> /etc/apt/sources.list && \
+    echo 'deb http://mirrors.aliyun.com/debian-security bookworm-security main' >> /etc/apt/sources.list && \
+    echo 'deb-src http://mirrors.aliyun.com/debian-security bookworm-security main' >> /etc/apt/sources.list
+
+# 安装系统依赖
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# 复制requirements.txt
+COPY requirements.txt .
+
+# 安装Python依赖
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 复制应用代码
+COPY . .
+
+# 暴露端口（假设Flask应用运行在5000端口）
+EXPOSE 8888
+
+# 使用gunicorn启动应用
+CMD ["gunicorn", "--bind", "0.0.0.0:8888", "--workers", "4", "web_server:app"] 

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Cookpro
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,12 +1,316 @@
----
-title: Stock Any
-emoji: 📈
-colorFrom: indigo
-colorTo: purple
-sdk: docker
-pinned: false
-license: other
-short_description: stock_any
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 智能分析系统
+
+![版本](https://img.shields.io/badge/版本-2.1.0-blue.svg)
+![Python](https://img.shields.io/badge/Python-3.7+-green.svg)
+![Flask](https://img.shields.io/badge/Flask-2.0+-red.svg)
+![AKShare](https://img.shields.io/badge/AKShare-1.0.0+-orange.svg)
+![AI](https://img.shields.io/badge/AI_API-集成-blueviolet.svg)
+
+![系统首页截图](./images/1.png)
+
+## 📝 项目概述
+
+智能分析系统是一个基于Python和Flask的Web应用，整合了多维度股票分析能力和人工智能辅助决策功能。系统通过AKShare获取股票数据，结合技术分析、基本面分析和资金面分析，为投资者提供全方位的投资决策支持。
+
+## ✨ 核心功能
+
+### 多维度股票分析
+
+- **技术面分析**：趋势识别、支撑压力位、技术指标（RSI、MACD、KDJ等）
+- **基本面分析**：估值分析、财务健康、成长前景
+- **资金面分析**：主力资金流向、北向资金、机构持仓
+- **智能评分**：100分制综合评分，40-40-20权重分配
+
+### 智能化功能
+
+- **AI增强分析**：通过AI API提供专业投资建议
+- **支撑压力位自动识别**：智能识别关键价格区域
+- **情景预测**：生成乐观、中性、悲观多种市场情景，优化预测精度和可视化效果
+- **智能问答**：支持联网搜索实时信息和多轮对话，回答关于个股的专业问题
+
+### 市场分析工具
+
+- **市场扫描**：筛选高评分股票，发现投资机会
+- **投资组合分析**：评估投资组合表现，提供调整建议
+- **风险监控**：多维度风险预警系统
+- **指数和行业分析**：支持沪深300、中证500等指数和主要行业成分股分析
+
+### 可视化界面
+
+- **交互式图表**：K线图、技术指标、多维度评分雷达图
+- **直观数据展示**：支撑压力位、评分、投资建议等清晰呈现
+- **响应式设计**：适配桌面和移动设备的界面
+- **财经门户主页**：三栏式财经门户风格布局，左侧功能导航、中间实时财经要闻、右侧舆情热点，底部显示全球主要市场状态
+
+### 实时数据更新
+
+- **实时财经要闻**：时间线形式展示最新财经新闻，自动高亮上涨/下跌相关内容
+- **舆情热点监控**：自动识别和展示市场舆情热点，包括人工智能等前沿领域
+- **全球市场状态**：实时显示亚太、欧非中东、美洲等全球主要证券市场的开闭市状态
+- **自动刷新机制**：系统每10分钟自动刷新，确保数据实时性
+
+## 🔧 系统架构
+
+```
+智能分析系统/
+│
+├── web_server.py            # Web服务器和路由控制
+├── stock_analyzer.py        # 股票分析核心引擎
+├── us_stock_service.py      # 美股服务（可选）
+├── start.sh                 # 服务管理脚本
+├── news_fetcher.py          # 新闻获取与缓存
+├── stock_qa.py              # 智能问答功能，支持联网搜索
+│
+├── templates/               # HTML模板
+│   ├── layout.html          # 基础布局模板
+│   ├── index.html           # 首页（财经门户风格）
+│   ├── dashboard.html       # 智能仪表盘
+│   ├── stock_detail.html    # 股票详情页
+│   ├── market_scan.html     # 市场扫描页面
+│   ├── portfolio.html       # 投资组合页面
+│   └── error.html           # 错误页面
+│   └── *********            # 不一一列举了
+│
+├── static/                  # 静态资源
+│   ├── favicon.ico          # favicon.ico
+│   └── swagger.json         # API文档
+│
+├── data/                    # 数据存储目录
+│   └── news/                # 新闻缓存目录
+│
+└── .env                     # 环境变量配置文件
+```
+
+### 技术栈
+
+- **后端**：Python, Flask, AKShare, AI API
+- **前端**：HTML5, CSS3, JavaScript, Bootstrap 5, ApexCharts
+- **数据分析**：Pandas, NumPy
+- **AI**：各种AI模型集成
+
+## 📦 安装指南
+
+### 环境要求
+
+- Python 3.7+
+- pip包管理器
+- 网络连接（用于获取股票数据和访问AI API）
+
+### 安装步骤
+
+1. **克隆或下载代码库**
+
+```bash
+git clone https://github.com/LargeCupPanda/StockAnal_Sys.git
+cd StockAnal_Sys
+```
+
+2. **安装依赖**
+
+```bash
+pip install -r requirements.txt
+```
+
+或手动安装主要依赖：
+
+```bash
+pip install flask pandas numpy akshare requests matplotlib python-dotenv flask-cors flask-caching
+```
+
+3. **创建并配置环境变量**
+
+将`.env-example`复制为`.env`，并设置您的API密钥：
+
+```
+# API 提供商 (OpenAI SDK )
+API_PROVIDER=openai
+
+# OpenAI API 配置
+OPENAI_API_URL=***
+OPENAI_API_KEY=your_api_key
+OPENAI_API_MODEL=gpt-4o
+NEWS_MODEL=你的可联网模型
+```
+
+## ⚙️ 配置说明
+
+### 环境变量
+
+| 变量名 | 说明 | 默认值 |
+|-------|------|-------|
+| `API_PROVIDER` | API提供商选择 | `openai` |
+| `OPENAI_API_KEY` | OpenAI API密钥 | 无，必须提供 |
+| `OPENAI_API_URL` | OpenAI API端点URL | `https://api.openai.com/v1` |
+| `OPENAI_API_MODEL` | 使用的OpenAI模型 | `gpt-4o` |
+| `PORT` | Web服务器端口 | `8888` |
+
+### 技术指标参数
+
+可在`stock_analyzer.py`中的`__init__`方法中调整以下参数：
+
+- `ma_periods`: 移动平均线周期设置
+- `rsi_period`: RSI指标周期
+- `bollinger_period`: 布林带周期
+- `bollinger_std`: 布林带标准差
+- `volume_ma_period`: 成交量均线周期
+- `atr_period`: ATR周期
+
+### 缓存机制
+
+系统实现了智能缓存策略，包括：
+
+- **股票数据缓存**：减少重复API调用
+- **分析结果缓存**：避免重复计算
+- **任务结果缓存**：保存已完成任务的结果
+- **新闻数据缓存**：按天存储新闻数据，避免重复内容
+- **自动缓存清理**：每天收盘时间(16:30左右)自动清理所有缓存，确保数据实时性
+
+## 🚀 使用指南
+
+### 启动系统
+
+使用提供的启动脚本：
+
+```bash
+bash start.sh start
+```
+
+启动后，访问 `http://localhost:8888` 打开系统。
+
+### 其他管理命令
+
+```bash
+bash start.sh stop       # 停止服务
+bash start.sh restart    # 重启服务
+bash start.sh status     # 查看服务状态
+bash start.sh monitor    # 以监控模式运行（自动重启）
+bash start.sh logs       # 查看日志
+```
+
+### Docker启动
+
+```bash
+docker-compose up -d
+```
+可以挂载sqlite_data，在env文件中设置USE_DATABASE=True
+可以使用redis缓存，在env文件中设置USE_REDIS_CACHE=True
+挂载.env文件到本地
+
+### 主要功能页面
+
+1. **首页** (`/`)
+   - 三栏式财经门户风格界面
+   - 左侧功能导航、中间实时财经要闻、右侧舆情热点
+   - 底部显示全球主要市场状态，10分钟自动刷新
+
+2. **智能仪表盘** (`/dashboard`)
+   - 输入股票代码，开始分析
+   - 查看多维度分析结果和AI建议
+
+3. **股票详情** (`/stock_detail/<stock_code>`)
+   - 查看单只股票的详细分析
+   - 支持技术图表、支撑压力位和AI分析
+
+4. **市场扫描** (`/market_scan`)
+   - 扫描指数成分股或行业股票
+   - 筛选高评分股票，发现投资机会
+
+5. **投资组合** (`/portfolio`)
+   - 创建和管理个人投资组合
+   - 分析组合表现，获取优化建议
+
+6. **基本面分析** (`/fundamental`)
+   - 查看股票财务指标和估值分析
+   - 分析股票成长性和财务健康状况
+
+7. **资金流向** (`/capital_flow`)
+   - 跟踪主力资金和北向资金动向
+   - 分析机构持仓变化
+
+8. **情景预测** (`/scenario_predict`)
+   - 预测股票未来走势的多种情景
+   - 提供乐观、中性、悲观三种预测
+
+9. **风险监控** (`/risk_monitor`)
+   - 分析股票和投资组合风险
+   - 提供风险预警和应对建议
+
+10. **智能问答** (`/qa`)
+    - 通过AI回答关于股票的专业问题
+    - 支持联网搜索实时信息和多轮对话
+
+11. **行业分析** (`/industry_analysis`)
+    - 分析行业整体表现和资金流向
+    - 对比不同行业投资机会
+
+### 常用操作
+
+- **分析股票**：在智能仪表盘输入股票代码，点击"分析"
+- **查看股票详情**：点击股票代码或搜索股票进入详情页
+- **扫描市场**：在市场扫描页面选择指数或行业，设置最低评分，点击"扫描"
+- **管理投资组合**：在投资组合页面添加/删除股票，查看组合分析
+- **智能问答**：选择股票后，提问关于该股票的问题，获取AI回答
+- **查看实时财经要闻**：在首页浏览最新财经新闻和舆情热点
+
+## 📚 API文档
+
+系统提供了完整的REST API，可通过Swagger文档查看：`/api/docs`
+
+主要API包括：
+
+- 股票分析API：`/api/enhanced_analysis`
+- 市场扫描API：`/api/start_market_scan`
+- 指数成分股API：`/api/index_stocks`
+- 智能问答API：`/api/qa`
+- 风险分析API：`/api/risk_analysis`
+- 情景预测API：`/api/scenario_predict`
+- 行业分析API：`/api/industry_analysis`
+- 最新新闻API：`/api/latest_news`
+
+## 📋 版本历史
+
+### v2.1.0 (当前版本)
+- 优化缓存机制，增加市场收盘时自动清理缓存
+- 增强错误处理和系统稳定性
+- 新增智能问答功能，支持联网搜索实时信息和多轮对话
+- 优化情景预测模块，提高预测精度和可视化效果
+- 新增行业分析功能
+- 改进首页为财经门户风格，实时显示财经要闻与舆情热点
+- 增加全球主要市场状态实时监控
+- 优化服务器超时处理
+- 改进UI交互体验
+
+### v2.0.0
+- 增加多维度分析能力
+- 整合AI API实现AI增强分析
+- 新增投资组合管理功能
+- 重构用户界面，添加交互式图表
+- 优化技术���析和评分系统
+
+### v1.0.0 (初始版本)
+- 基础股票分析功能
+- 技术指标计算
+- 简单评分系统
+- 基础Web界面
+
+## 🔄 扩展开发
+
+系统设计采用模块化架构，便于扩展开发。主要扩展点包括：
+
+- 添加新的技术指标
+- 集成其他数据源
+- 开发新的分析模块
+- 扩展用户界面功能
+
+## ⚠️ 注意
+
+**当前版本为先驱探索版，旨在学习人工智能在指令分析方面的研究学习。AI生成的内容有很多错误，请勿当成投资建议，若由此造成的一切损失，本项目不负责！**
+
+## 💡 联系与支持
+
+如有问题或建议，请pr：
+
+- 项目有很多问题，基础功能可以运行起来，扩充项目代码全由AI开发，所以进展比较缓慢，请谅解。
+- 如你有好的想法或修复，欢迎提交GitHub Issue
+
+感谢使用智能分析系统！

app/analysis/capital_flow_analyzer.py

@@ -0,0 +1,588 @@
+# capital_flow_analyzer.py
+import logging
+import traceback
+import akshare as ak
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+
+class CapitalFlowAnalyzer:
+    def __init__(self):
+        self.data_cache = {}
+
+        # 设置日志记录
+        logging.basicConfig(level=logging.INFO,
+                            format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger(__name__)
+
+    def get_concept_fund_flow(self, period="10日排行"):
+        """获取概念/行业资金流向数据"""
+        try:
+            self.logger.info(f"Getting concept fund flow for period: {period}")
+
+            # 检查缓存
+            cache_key = f"concept_fund_flow_{period}"
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果在最近一小时内有缓存数据，则返回缓存数据
+                if (datetime.now() - cache_time).total_seconds() < 3600:
+                    return cached_data
+
+            # 从akshare获取数据
+            concept_data = ak.stock_fund_flow_concept(symbol=period)
+
+            # 处理数据
+            result = []
+            for _, row in concept_data.iterrows():
+                try:
+                    # 列名可能有所不同，所以我们使用灵活的方法
+                    item = {
+                        "rank": int(row.get("序号", 0)),
+                        "sector": row.get("行业", ""),
+                        "company_count": int(row.get("公司家数", 0)),
+                        "sector_index": float(row.get("行业指数", 0)),
+                        "change_percent": self._parse_percent(row.get("阶段涨跌幅", "0%")),
+                        "inflow": float(row.get("流入资金", 0)),
+                        "outflow": float(row.get("流出资金", 0)),
+                        "net_flow": float(row.get("净额", 0))
+                    }
+                    result.append(item)
+                except Exception as e:
+                    # self.logger.warning(f"Error processing row in concept fund flow: {str(e)}")
+                    continue
+
+            # 缓存结果
+            self.data_cache[cache_key] = (datetime.now(), result)
+
+            return result
+        except Exception as e:
+            self.logger.error(f"Error getting concept fund flow: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            # 如果API调用失败则返回模拟数据
+            return self._generate_mock_concept_fund_flow(period)
+
+    def get_individual_fund_flow_rank(self, period="10日"):
+        """获取个股资金流向排名"""
+        try:
+            self.logger.info(f"Getting individual fund flow ranking for period: {period}")
+
+            # 检查缓存
+            cache_key = f"individual_fund_flow_rank_{period}"
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果在最近一小时内有缓存数据，则返回缓存数据
+                if (datetime.now() - cache_time).total_seconds() < 3600:
+                    return cached_data
+
+            # 从akshare获取数据
+            stock_data = ak.stock_individual_fund_flow_rank(indicator=period)
+
+            # 处理数据
+            result = []
+            for _, row in stock_data.iterrows():
+                try:
+                    # 根据不同时间段设置列名前缀
+                    period_prefix = "" if period == "今日" else f"{period}"
+
+                    item = {
+                        "rank": int(row.get("序号", 0)),
+                        "code": row.get("代码", ""),
+                        "name": row.get("名称", ""),
+                        "price": float(row.get("最新价", 0)),
+                        "change_percent": float(row.get(f"{period_prefix}涨跌幅", 0)),
+                        "main_net_inflow": float(row.get(f"{period_prefix}主力净流入-净额", 0)),
+                        "main_net_inflow_percent": float(row.get(f"{period_prefix}主力净流入-净占比", 0)),
+                        "super_large_net_inflow": float(row.get(f"{period_prefix}超大单净流入-净额", 0)),
+                        "super_large_net_inflow_percent": float(row.get(f"{period_prefix}超大单净流入-净占比", 0)),
+                        "large_net_inflow": float(row.get(f"{period_prefix}大单净流入-净额", 0)),
+                        "large_net_inflow_percent": float(row.get(f"{period_prefix}大单净流入-净占比", 0)),
+                        "medium_net_inflow": float(row.get(f"{period_prefix}中单净流入-净额", 0)),
+                        "medium_net_inflow_percent": float(row.get(f"{period_prefix}中单净流入-净占比", 0)),
+                        "small_net_inflow": float(row.get(f"{period_prefix}小单净流入-净额", 0)),
+                        "small_net_inflow_percent": float(row.get(f"{period_prefix}小单净流入-净占比", 0))
+                    }
+                    result.append(item)
+                except Exception as e:
+                    self.logger.warning(f"Error processing row in individual fund flow rank: {str(e)}")
+                    continue
+
+            # 缓存结果
+            self.data_cache[cache_key] = (datetime.now(), result)
+
+            return result
+        except Exception as e:
+            self.logger.error(f"Error getting individual fund flow ranking: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            # 如果API调用失败则返回模拟数据
+            return self._generate_mock_individual_fund_flow_rank(period)
+
+    def get_individual_fund_flow(self, stock_code, market_type="", re_date="10日"):
+        """获取个股资金流向数据"""
+        try:
+            self.logger.info(f"Getting fund flow for stock: {stock_code}, market: {market_type}")
+
+            # 检查缓存
+            cache_key = f"individual_fund_flow_{stock_code}_{market_type}"
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果在一小时内有缓存数据，则返回缓存数据
+                if (datetime.now() - cache_time).total_seconds() < 3600:
+                    return cached_data
+
+            # 如果未提供市场类型，则根据股票代码判断
+            if not market_type:
+                if stock_code.startswith('6'):
+                    market_type = "sh"
+                elif stock_code.startswith('0') or stock_code.startswith('3'):
+                    market_type = "sz"
+                else:
+                    market_type = "sh"  # Default to Shanghai
+
+            # 从akshare获取数据
+            flow_data = ak.stock_individual_fund_flow(stock=stock_code, market=market_type)
+
+            # 处理数据
+            result = {
+                "stock_code": stock_code,
+                "data": []
+            }
+
+            for _, row in flow_data.iterrows():
+                try:
+                    item = {
+                        "date": row.get("日期", ""),
+                        "price": float(row.get("收盘价", 0)),
+                        "change_percent": float(row.get("涨跌幅", 0)),
+                        "main_net_inflow": float(row.get("主力净流入-净额", 0)),
+                        "main_net_inflow_percent": float(row.get("主力净流入-净占比", 0)),
+                        "super_large_net_inflow": float(row.get("超大单净流入-净额", 0)),
+                        "super_large_net_inflow_percent": float(row.get("超大单净流入-净占比", 0)),
+                        "large_net_inflow": float(row.get("大单净流入-净额", 0)),
+                        "large_net_inflow_percent": float(row.get("大单净流入-净占比", 0)),
+                        "medium_net_inflow": float(row.get("中单净流入-净额", 0)),
+                        "medium_net_inflow_percent": float(row.get("中单净流入-净占比", 0)),
+                        "small_net_inflow": float(row.get("小单净流入-净额", 0)),
+                        "small_net_inflow_percent": float(row.get("小单净流入-净占比", 0))
+                    }
+                    result["data"].append(item)
+                except Exception as e:
+                    self.logger.warning(f"Error processing row in individual fund flow: {str(e)}")
+                    continue
+
+            # 计算汇总统计数据
+            if result["data"]:
+                # 最近数据 (最近10天)
+                recent_data = result["data"][:min(10, len(result["data"]))]
+
+                result["summary"] = {
+                    "recent_days": len(recent_data),
+                    "total_main_net_inflow": sum(item["main_net_inflow"] for item in recent_data),
+                    "avg_main_net_inflow_percent": np.mean(
+                        [item["main_net_inflow_percent"] for item in recent_data]),
+                    "positive_days": sum(1 for item in recent_data if item["main_net_inflow"] > 0),
+                    "negative_days": sum(1 for item in recent_data if item["main_net_inflow"] <= 0)
+                }
+
+            # Cache the result
+            self.data_cache[cache_key] = (datetime.now(), result)
+
+            return result
+        except Exception as e:
+            self.logger.error(f"Error getting individual fund flow: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            # 如果API调用失败则返回模拟数据
+            return self._generate_mock_individual_fund_flow(stock_code, market_type)
+
+    def get_sector_stocks(self, sector):
+        """获取特定行业的股票"""
+        try:
+            self.logger.info(f"Getting stocks for sector: {sector}")
+
+            # 检查缓存
+            cache_key = f"sector_stocks_{sector}"
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果在一小时内有缓存数据，则返回缓存数据
+                if (datetime.now() - cache_time).total_seconds() < 3600:
+                    return cached_data
+
+            # 尝试从akshare获取数据
+            try:
+                # For industry sectors (using 东方财富 interface)
+                stocks = ak.stock_board_industry_cons_em(symbol=sector)
+
+                # 提取股票列表
+                if not stocks.empty and '代码' in stocks.columns:
+                    result = []
+                    for _, row in stocks.iterrows():
+                        try:
+                            item = {
+                                "code": row.get("代码", ""),
+                                "name": row.get("名称", ""),
+                                "price": float(row.get("最新价", 0)),
+                                "change_percent": float(row.get("涨跌幅", 0)) if "涨跌幅" in row else 0,
+                                "main_net_inflow": 0,  # We'll get this data separately if needed
+                                "main_net_inflow_percent": 0  # We'll get this data separately if needed
+                            }
+                            result.append(item)
+                        except Exception as e:
+                            # self.logger.warning(f"Error processing row in sector stocks: {str(e)}")
+                            continue
+
+                    # 缓存结果
+                    self.data_cache[cache_key] = (datetime.now(), result)
+                    return result
+            except Exception as e:
+                self.logger.warning(f"Failed to get sector stocks from API: {str(e)}")
+                # 降级到模拟数据
+
+            # 如果到达这里，说明无法从API获取数据，返回模拟数据
+            result = self._generate_mock_sector_stocks(sector)
+            self.data_cache[cache_key] = (datetime.now(), result)
+            return result
+
+        except Exception as e:
+            self.logger.error(f"Error getting sector stocks: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            # 如果API调用失败则返回模拟数据
+            return self._generate_mock_sector_stocks(sector)
+
+    def calculate_capital_flow_score(self, stock_code, market_type=""):
+        """计算股票资金流向评分"""
+        try:
+            self.logger.info(f"Calculating capital flow score for stock: {stock_code}")
+
+            # 获取个股资金流向数据
+            fund_flow = self.get_individual_fund_flow(stock_code, market_type)
+
+            if not fund_flow or not fund_flow.get("data") or not fund_flow.get("summary"):
+                return {
+                    "total": 0,
+                    "main_force": 0,
+                    "large_order": 0,
+                    "small_order": 0,
+                    "details": {}
+                }
+
+            # Extract summary statistics
+            summary = fund_flow["summary"]
+            recent_days = summary["recent_days"]
+            total_main_net_inflow = summary["total_main_net_inflow"]
+            avg_main_net_inflow_percent = summary["avg_main_net_inflow_percent"]
+            positive_days = summary["positive_days"]
+
+            # Calculate main force score (0-40)
+            main_force_score = 0
+
+            # 基于净流入百分比的评分
+            if avg_main_net_inflow_percent > 3:
+                main_force_score += 20
+            elif avg_main_net_inflow_percent > 1:
+                main_force_score += 15
+            elif avg_main_net_inflow_percent > 0:
+                main_force_score += 10
+
+            # 基于上涨天数的评分
+            positive_ratio = positive_days / recent_days if recent_days > 0 else 0
+            if positive_ratio > 0.7:
+                main_force_score += 20
+            elif positive_ratio > 0.5:
+                main_force_score += 15
+            elif positive_ratio > 0.3:
+                main_force_score += 10
+
+            # 计算大单评分（0-30分）
+            large_order_score = 0
+
+            # 分析超大单和大单交易
+            recent_super_large = [item["super_large_net_inflow"] for item in
+                                   fund_flow["data"][:recent_days]]
+            recent_large = [item["large_net_inflow"] for item in fund_flow["data"][:recent_days]]
+
+            super_large_positive = sum(1 for x in recent_super_large if x > 0)
+            large_positive = sum(1 for x in recent_large if x > 0)
+
+            # 基于超大单的评分
+            super_large_ratio = super_large_positive / recent_days if recent_days > 0 else 0
+            if super_large_ratio > 0.7:
+                large_order_score += 15
+            elif super_large_ratio > 0.5:
+                large_order_score += 10
+            elif super_large_ratio > 0.3:
+                large_order_score += 5
+
+            # 基于大单的评分
+            large_ratio = large_positive / recent_days if recent_days > 0 else 0
+            if large_ratio > 0.7:
+                large_order_score += 15
+            elif large_ratio > 0.5:
+                large_order_score += 10
+            elif large_ratio > 0.3:
+                large_order_score += 5
+
+            # 计算小单评分（0-30分）
+            small_order_score = 0
+
+            # 分析中单和小单交易
+            recent_medium = [item["medium_net_inflow"] for item in fund_flow["data"][:recent_days]]
+            recent_small = [item["small_net_inflow"] for item in fund_flow["data"][:recent_days]]
+
+            medium_positive = sum(1 for x in recent_medium if x > 0)
+            small_positive = sum(1 for x in recent_small if x > 0)
+
+            # 基于中单的评分
+            medium_ratio = medium_positive / recent_days if recent_days > 0 else 0
+            if medium_ratio > 0.7:
+                small_order_score += 15
+            elif medium_ratio > 0.5:
+                small_order_score += 10
+            elif medium_ratio > 0.3:
+                small_order_score += 5
+
+            # 基于小单的评分
+            small_ratio = small_positive / recent_days if recent_days > 0 else 0
+            if small_ratio > 0.7:
+                small_order_score += 15
+            elif small_ratio > 0.5:
+                small_order_score += 10
+            elif small_ratio > 0.3:
+                small_order_score += 5
+
+            # 计算总评分
+            total_score = main_force_score + large_order_score + small_order_score
+
+            return {
+                "total": total_score,
+                "main_force": main_force_score,
+                "large_order": large_order_score,
+                "small_order": small_order_score,
+                "details": fund_flow
+            }
+        except Exception as e:
+            self.logger.error(f"Error calculating capital flow score: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            return {
+                "total": 0,
+                "main_force": 0,
+                "large_order": 0,
+                "small_order": 0,
+                "details": {},
+                "error": str(e)
+            }
+
+    def _parse_percent(self, percent_str):
+        """将百分比字符串转换为浮点数"""
+        try:
+            if isinstance(percent_str, str) and '%' in percent_str:
+                return float(percent_str.replace('%', ''))
+            return float(percent_str)
+        except (ValueError, TypeError):
+            return 0.0
+
+    def _generate_mock_concept_fund_flow(self, period):
+        """生成模拟概念资金流向数据"""
+        # self.logger.warning(f"Generating mock concept fund flow data for period: {period}")
+
+        sectors = [
+            "新能源", "医药", "半导体", "芯片", "人工智能", "大数据", "云计算", "5G",
+            "汽车", "消费", "金融", "互联网", "游戏", "农业", "化工", "建筑", "军工",
+            "钢铁", "有色金属", "煤炭", "石油"
+        ]
+
+        result = []
+        for i, sector in enumerate(sectors):
+            # 随机数据 - 前半部分为正，后半部分为负
+            is_positive = i < len(sectors) // 2
+
+            inflow = round(np.random.uniform(10, 50), 2) if is_positive else round(
+                np.random.uniform(5, 20), 2)
+            outflow = round(np.random.uniform(5, 20), 2) if is_positive else round(
+                np.random.uniform(10, 50), 2)
+            net_flow = round(inflow - outflow, 2)
+
+            change_percent = round(np.random.uniform(0, 5), 2) if is_positive else round(
+                np.random.uniform(-5, 0), 2)
+
+            item = {
+                "rank": i + 1,
+                "sector": sector,
+                "company_count": np.random.randint(10, 100),
+                "sector_index": round(np.random.uniform(1000, 5000), 2),
+                "change_percent": change_percent,
+                "inflow": inflow,
+                "outflow": outflow,
+                "net_flow": net_flow
+            }
+            result.append(item)
+
+        # 按净流入降序排序
+        return sorted(result, key=lambda x: x["net_flow"], reverse=True)
+
+    def _generate_mock_individual_fund_flow_rank(self, period):
+        """生成模拟个股资金流向排名数据"""
+        # self.logger.warning(f"Generating mock individual fund flow ranking data for period: {period}")
+
+        # Sample stock data
+        stocks = [
+            {"code": "600000", "name": "浦发银行"}, {"code": "600036", "name": "招商银行"},
+            {"code": "601318", "name": "中国平安"}, {"code": "600519", "name": "贵州茅台"},
+            {"code": "000858", "name": "五粮液"}, {"code": "000333", "name": "美的集团"},
+            {"code": "600276", "name": "恒瑞医药"}, {"code": "601888", "name": "中国中免"},
+            {"code": "600030", "name": "中信证券"}, {"code": "601166", "name": "兴业银行"},
+            {"code": "600887", "name": "伊利股份"}, {"code": "601398", "name": "工商银行"},
+            {"code": "600028", "name": "中国石化"}, {"code": "601988", "name": "中国银行"},
+            {"code": "601857", "name": "中国石油"}, {"code": "600019", "name": "宝钢股份"},
+            {"code": "600050", "name": "中国联通"}, {"code": "601328", "name": "交通银行"},
+            {"code": "601668", "name": "中国建筑"}, {"code": "601288", "name": "农业银行"}
+        ]
+
+        result = []
+        for i, stock in enumerate(stocks):
+            # 随机数据 - 前半部分为正，后半部分为负
+            is_positive = i < len(stocks) // 2
+
+            main_net_inflow = round(np.random.uniform(1e6, 5e7), 2) if is_positive else round(
+                np.random.uniform(-5e7, -1e6), 2)
+            main_net_inflow_percent = round(np.random.uniform(1, 10), 2) if is_positive else round(
+                np.random.uniform(-10, -1), 2)
+
+            super_large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
+            super_large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
+
+            large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
+            large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
+
+            medium_net_inflow = round(np.random.uniform(-1e6, 1e6), 2)
+            medium_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
+
+            small_net_inflow = round(np.random.uniform(-1e6, 1e6), 2)
+            small_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
+
+            change_percent = round(np.random.uniform(0, 5), 2) if is_positive else round(np.random.uniform(-5, 0), 2)
+
+            item = {
+                "rank": i + 1,
+                "code": stock["code"],
+                "name": stock["name"],
+                "price": round(np.random.uniform(10, 100), 2),
+                "change_percent": change_percent,
+                "main_net_inflow": main_net_inflow,
+                "main_net_inflow_percent": main_net_inflow_percent,
+                "super_large_net_inflow": super_large_net_inflow,
+                "super_large_net_inflow_percent": super_large_net_inflow_percent,
+                "large_net_inflow": large_net_inflow,
+                "large_net_inflow_percent": large_net_inflow_percent,
+                "medium_net_inflow": medium_net_inflow,
+                "medium_net_inflow_percent": medium_net_inflow_percent,
+                "small_net_inflow": small_net_inflow,
+                "small_net_inflow_percent": small_net_inflow_percent
+            }
+            result.append(item)
+
+        # 按主力净流入降序排序
+        return sorted(result, key=lambda x: x["main_net_inflow"], reverse=True)
+
+    def _generate_mock_individual_fund_flow(self, stock_code, market_type):
+        """生成模拟个股资金流向数据"""
+        # self.logger.warning(f"Generating mock individual fund flow data for stock: {stock_code}")
+
+        # 生成30天的模拟数据
+        end_date = datetime.now()
+
+        result = {
+            "stock_code": stock_code,
+            "data": []
+        }
+
+        # 创建模拟价格趋势（使用合理的随机游走）
+        base_price = np.random.uniform(10, 100)
+        current_price = base_price
+
+        for i in range(30):
+            date = (end_date - timedelta(days=i)).strftime('%Y-%m-%d')
+
+            # 随机价格变化（-2%到+2%）
+            change_percent = np.random.uniform(-2, 2)
+            price = round(current_price * (1 + change_percent / 100), 2)
+            current_price = price
+
+            # 随机资金流向数据，与价格变化有一定相关性
+            is_positive = change_percent > 0
+
+            main_net_inflow = round(np.random.uniform(1e5, 5e6), 2) if is_positive else round(
+                np.random.uniform(-5e6, -1e5), 2)
+            main_net_inflow_percent = round(np.random.uniform(1, 5), 2) if is_positive else round(
+                np.random.uniform(-5, -1), 2)
+
+            super_large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
+            super_large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
+
+            large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
+            large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
+
+            medium_net_inflow = round(np.random.uniform(-1e5, 1e5), 2)
+            medium_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
+
+            small_net_inflow = round(np.random.uniform(-1e5, 1e5), 2)
+            small_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
+
+            item = {
+                "date": date,
+                "price": price,
+                "change_percent": round(change_percent, 2),
+                "main_net_inflow": main_net_inflow,
+                "main_net_inflow_percent": main_net_inflow_percent,
+                "super_large_net_inflow": super_large_net_inflow,
+                "super_large_net_inflow_percent": super_large_net_inflow_percent,
+                "large_net_inflow": large_net_inflow,
+                "large_net_inflow_percent": large_net_inflow_percent,
+                "medium_net_inflow": medium_net_inflow,
+                "medium_net_inflow_percent": medium_net_inflow_percent,
+                "small_net_inflow": small_net_inflow,
+                "small_net_inflow_percent": small_net_inflow_percent
+            }
+            result["data"].append(item)
+
+        # 按日期降序排序（最新的在前）
+        result["data"].sort(key=lambda x: x["date"], reverse=True)
+
+        # 计算汇总统计数据
+        recent_data = result["data"][:10]
+
+        result["summary"] = {
+            "recent_days": len(recent_data),
+            "total_main_net_inflow": sum(item["main_net_inflow"] for item in recent_data),
+            "avg_main_net_inflow_percent": np.mean([item["main_net_inflow_percent"] for item in recent_data]),
+            "positive_days": sum(1 for item in recent_data if item["main_net_inflow"] > 0),
+            "negative_days": sum(1 for item in recent_data if item["main_net_inflow"] <= 0)
+        }
+
+        return result
+
+    def _generate_mock_sector_stocks(self, sector):
+        """生成模拟行业股票数据"""
+        # self.logger.warning(f"Generating mock sector stocks for: {sector}")
+
+        # 要生成的股票数量
+        num_stocks = np.random.randint(20, 50)
+
+        result = []
+        for i in range(num_stocks):
+            prefix = "6" if np.random.random() > 0.5 else "0"
+            stock_code = prefix + str(100000 + i).zfill(5)[-5:]
+
+            change_percent = round(np.random.uniform(-5, 5), 2)
+
+            item = {
+                "code": stock_code,
+                "name": f"{sector}股票{i + 1}",
+                "price": round(np.random.uniform(10, 100), 2),
+                "change_percent": change_percent,
+                "main_net_inflow": round(np.random.uniform(-1e6, 1e6), 2),
+                "main_net_inflow_percent": round(np.random.uniform(-5, 5), 2)
+            }
+            result.append(item)
+
+        # 按主力净流入降序排序
+        return sorted(result, key=lambda x: x["main_net_inflow"], reverse=True)

app/analysis/etf_analyzer.py

@@ -0,0 +1,494 @@
+
+import akshare as ak
+import pandas as pd
+from datetime import datetime, timedelta
+import numpy as np
+from stockstats import StockDataFrame
+
+class EtfAnalyzer:
+    def __init__(self, etf_code, stock_analyzer_instance):
+        self.etf_code = etf_code
+        self.analysis_result = {}
+        self.hist_df = None # 用于存储历史数据以供后续分析使用
+        self.stock_analyzer = stock_analyzer_instance # 复用StockAnalyzer实例
+
+    def run_analysis(self):
+        """
+        运行所有分析步骤并返回结果
+        """
+        self.get_basic_info()
+        self.analyze_market_performance()
+        self.analyze_fund_flow()
+        self.analyze_risk_and_tracking()
+        self.analyze_holdings()
+        self.analyze_sector()
+        self.get_ai_summary()
+        
+        return self.analysis_result
+
+    def get_basic_info(self):
+        """
+        1. 基本信息分析
+        """
+        print("开始获取基本信息...")
+        try:
+            # 使用akshare获取ETF基金概况
+            fund_info_df = ak.fund_etf_fund_info_em(fund=self.etf_code)
+            
+            if fund_info_df.empty:
+                info_dict = {"error": "未能获取到该ETF的基本信息，请检查代码是否正确。"}
+            else:
+                # 将返回的DataFrame转换成字典，假设第一列是键，第二列是值
+                info_dict = {}
+                for _, row in fund_info_df.iterrows():
+                    if len(row) >= 2:
+                        info_dict[row.iloc[0]] = row.iloc[1]
+
+            self.analysis_result['basic_info'] = info_dict
+            print("基本信息获取完成。")
+
+        except Exception as e:
+            print(f"获取ETF基本信息时出错: {e}")
+            self.analysis_result['basic_info'] = {"error": f"获取基本信息失败: {e}"}
+
+    def analyze_market_performance(self):
+        """
+        2. 市场表现与技术分析 (第一部分：回报率与流动性)
+        """
+        print("开始分析市场表现...")
+        try:
+            # 获取近一年的历史数据
+            end_date = datetime.now()
+            start_date = end_date - timedelta(days=365)
+            
+            end_date_str = end_date.strftime('%Y%m%d')
+            start_date_str = start_date.strftime('%Y%m%d')
+
+            # 使用后复权数据
+            hist_df = ak.fund_etf_hist_em(symbol=self.etf_code, start_date=start_date_str, end_date=end_date_str, adjust="hfq")
+
+            if hist_df.empty:
+                self.analysis_result['market_performance'] = {"error": "未能获取到该ETF的历史行情数据。"}
+                print("未能获取历史行情数据。")
+                return
+
+            # --- 数据准备 ---
+            hist_df['日期'] = pd.to_datetime(hist_df['日期'])
+            hist_df.set_index('日期', inplace=True)
+            hist_df['收盘'] = pd.to_numeric(hist_df['收盘'])
+            hist_df['成交额'] = pd.to_numeric(hist_df['成交额'], errors='coerce').fillna(0)
+            hist_df['换手率'] = pd.to_numeric(hist_df['换手率'], errors='coerce').fillna(0)
+            
+            # 存储历史数据以供其他方法使用
+            self.hist_df = hist_df
+
+            # --- 回报率计算 ---
+            returns = {}
+            if not hist_df.empty:
+                latest_price = hist_df['收盘'].iloc[-1]
+                
+                periods = {
+                    '近1周': 5,
+                    '近1个月': 21,
+                    '近3个月': 63,
+                    '近1年': 252
+                }
+                
+                for name, days in periods.items():
+                    if len(hist_df) > days:
+                        old_price = hist_df['收盘'].iloc[-days-1]
+                        returns[name] = ((latest_price / old_price) - 1) * 100 if old_price != 0 else 0
+                    else:
+                        # 如果数据不足，则从第一天开始算
+                        old_price = hist_df['收盘'].iloc[0]
+                        returns[name] = ((latest_price / old_price) - 1) * 100 if old_price != 0 else 0
+
+                # 计算年初至今回报率
+                ytd_df = hist_df[hist_df.index.year == end_date.year]
+                if not ytd_df.empty:
+                    ytd_start_price = ytd_df['收盘'].iloc[0]
+                    returns['年初至今'] = ((latest_price / ytd_start_price) - 1) * 100 if ytd_start_price != 0 else 0
+                else:
+                    # 如果当年没有数据，则计算从开始到现在的总回报
+                    start_price = hist_df['收盘'].iloc[0]
+                    returns['年初至今'] = ((latest_price / start_price) - 1) * 100 if start_price != 0 else 0
+
+            # --- 流动性分析 ---
+            liquidity = {}
+            last_month_df = hist_df.tail(21)
+            if not last_month_df.empty:
+                liquidity['日均成交额(近一月)'] = last_month_df['成交额'].mean()
+                liquidity['日均换手率(近一月)'] = last_month_df['换手率'].mean()
+            else:
+                liquidity['日均成交额(近一月)'] = None
+                liquidity['日均换手率(近一月)'] = None
+
+            # --- 技术指标计算 ---
+            tech_indicators = {}
+            if self.hist_df is not None and not self.hist_df.empty:
+                # stockstats需要特定的列名，创建一个副本进行操作
+                stock_df_for_ta = self.hist_df.copy()
+                stock_df_for_ta.rename(columns={'收盘': 'close', '开盘': 'open', '最高': 'high', '最低': 'low', '成交量': 'volume'}, inplace=True)
+                
+                # 转换为StockDataFrame
+                sdf = StockDataFrame.retype(stock_df_for_ta)
+                
+                # 计算指标
+                sdf[['macd', 'macds', 'macdh']] # MACD
+                sdf['rsi_14'] # RSI
+                sdf['close_20_sma'] # 20日均线
+                sdf['close_60_sma'] # 60日均线
+
+                # 获取最新的指标值
+                latest_indicators = sdf.iloc[-1]
+                tech_indicators = {
+                    'MA20': latest_indicators.get('close_20_sma'),
+                    'MA60': latest_indicators.get('close_60_sma'),
+                    'MACD': latest_indicators.get('macd'),
+                    'MACD_Signal': latest_indicators.get('macds'),
+                    'MACD_Hist': latest_indicators.get('macdh'),
+                    'RSI_14': latest_indicators.get('rsi_14')
+                }
+                
+                # 注意：不再覆盖 self.hist_df
+
+            self.analysis_result['market_performance'] = {
+                "returns": returns,
+                "liquidity": liquidity,
+                "tech_indicators": tech_indicators,
+                "message": "回报率、流动性和技术指标分析完成。"
+            }
+            print("市场表现分析(回报率、流动性、技术指标)完成。")
+
+            # --- 与基准对比 ---
+            benchmark_code = 'sh000300' # 默认使用沪深300作为基准
+            print(f"开始与基准 {benchmark_code} 进行对比...")
+            
+            benchmark_df = ak.stock_zh_index_daily(symbol=benchmark_code)
+            benchmark_df['date'] = pd.to_datetime(benchmark_df['date'])
+            benchmark_df.set_index('date', inplace=True)
+            
+            # 截取与ETF相同的时间段
+            benchmark_df = benchmark_df.loc[self.hist_df.index[0]:self.hist_df.index[-1]]
+            
+            benchmark_returns = {}
+            alpha = {}
+
+            if not benchmark_df.empty:
+                benchmark_latest_price = benchmark_df['close'].iloc[-1]
+                
+                for name, days in periods.items():
+                    if len(benchmark_df) > days:
+                        old_price = benchmark_df['close'].iloc[-days-1]
+                        benchmark_returns[name] = ((benchmark_latest_price / old_price) - 1) * 100 if old_price != 0 else 0
+                    else:
+                        old_price = benchmark_df['close'].iloc[0]
+                        benchmark_returns[name] = ((benchmark_latest_price / old_price) - 1) * 100 if old_price != 0 else 0
+                    
+                    # 计算超额收益
+                    alpha[name] = returns.get(name, 0) - benchmark_returns.get(name, 0)
+
+                # 年初至今
+                benchmark_ytd_df = benchmark_df[benchmark_df.index.year == end_date.year]
+                if not benchmark_ytd_df.empty:
+                    ytd_start_price = benchmark_ytd_df['close'].iloc[0]
+                    benchmark_returns['年初至今'] = ((benchmark_latest_price / ytd_start_price) - 1) * 100 if ytd_start_price != 0 else 0
+                else:
+                    start_price = benchmark_df['close'].iloc[0]
+                    benchmark_returns['年初至今'] = ((benchmark_latest_price / start_price) - 1) * 100 if start_price != 0 else 0
+                
+                alpha['年初至今'] = returns.get('年初至今', 0) - benchmark_returns.get('年初至今', 0)
+
+            # 更新结果
+            self.analysis_result['market_performance']['benchmark_returns'] = benchmark_returns
+            self.analysis_result['market_performance']['alpha'] = alpha
+            self.analysis_result['market_performance']['message'] = "完整的市场表现分析已完成。"
+            print("与基准对比分析完成。")
+
+        except Exception as e:
+            print(f"分析市场表现时出错: {e}")
+            self.analysis_result['market_performance'] = {"error": f"分析市场表现失败: {e}"}
+
+    def analyze_fund_flow(self):
+        """
+        3. 资金流向分析
+        """
+        print("开始分析资金流向...")
+        try:
+            # 复用已获取的历史数据
+            if self.hist_df is None or self.hist_df.empty:
+                self.analysis_result['fund_flow'] = {"error": "历史数据缺失，无法进行资金流向分析。"}
+                print("历史数据缺失，跳过资金流向分析。")
+                return
+
+            df = self.hist_df.copy()
+            
+            # akshare返回的hist_df中没有直接的份额列，需要重新请求或者寻找其他接口
+            # 这里我们假设'成交量'可以作为份额变化的代理指标进行估算，这是一个简化处理
+            # 更好的方法是找到直接提供份额历史的接口
+            
+            # 计算份额变化 (此处使用成交量作为代理)
+            # 注意：这是一个估算，并非精确值。真实份额变化需专门接口。
+            df['份额变化'] = df['成交量'].diff().fillna(0)
+            
+            # 估算资金净流入/流出 = 份额变化 * 当日收盘价
+            # 正值表示流入，负值表示流出
+            df['资金净流入估算'] = df['份额变化'] * df['收盘']
+
+            # --- 汇总统计 ---
+            flow_summary = {}
+            periods = {
+                '近1周': 5,
+                '近1个月': 21,
+                '近3个月': 63
+            }
+
+            for name, days in periods.items():
+                if len(df) >= days:
+                    flow_summary[name] = df['资金净流入估算'].tail(days).sum()
+                else:
+                    flow_summary[name] = df['资金净流入估算'].sum()
+            
+            # 获取最近的资金流数据以供图表使用 (例如最近60天)
+            recent_flow_data = df.tail(60)[['资金净流入估算']].copy()
+            recent_flow_data.index = recent_flow_data.index.strftime('%Y-%m-%d')
+            recent_flow_data['资金净流入估算'] = (recent_flow_data['资金净流入估算'] / 1e8).round(4) # 转换为亿元
+
+            # 准备图表数据，格式为 [ [date_string, value], ... ]
+            chart_data_df = recent_flow_data.reset_index()
+            chart_data_list = chart_data_df.values.tolist()
+
+            self.analysis_result['fund_flow'] = {
+                "summary": flow_summary,
+                "daily_flow_chart_data": {"data": chart_data_list},
+                "message": "资金流向分析完成 (基于成交量估算)。"
+            }
+            print("资金流向分析完成。")
+
+        except Exception as e:
+            print(f"分析资金流向时出错: {e}")
+            self.analysis_result['fund_flow'] = {"error": f"分析资金流向失败: {e}"}
+
+    def analyze_risk_and_tracking(self):
+        """
+        4. 风险与跟踪能力分析
+        """
+        print("开始分析风险与跟踪能力...")
+        try:
+            if self.hist_df is None or self.hist_df.empty:
+                self.analysis_result['risk_and_tracking'] = {"error": "历史数据缺失，无法进行风险分析。"}
+                print("历史数据缺失，跳过风险分析。")
+                return
+
+            df = self.hist_df.copy()
+            # 确保返回的是pct_change，而不是整个Series
+            df['etf_return'] = df['收盘'].pct_change().fillna(0)
+
+            # 1. 波动率 (年化)
+            annualized_volatility = df['etf_return'].std() * np.sqrt(252)
+
+            # 2. 与基准比较 (Beta, 跟踪误差, 夏普比率)
+            benchmark_code = 'sh000300'
+            benchmark_df = ak.stock_zh_index_daily(symbol=benchmark_code)
+            benchmark_df['date'] = pd.to_datetime(benchmark_df['date'])
+            benchmark_df.set_index('date', inplace=True)
+            benchmark_df = benchmark_df.loc[df.index.min():df.index.max()]
+            benchmark_df['benchmark_return'] = benchmark_df['close'].pct_change().fillna(0)
+
+            # 合并数据
+            merged_df = pd.merge(df[['etf_return']], benchmark_df[['benchmark_return']], left_index=True, right_index=True, how='inner')
+
+            # 计算 Beta
+            covariance = merged_df['etf_return'].cov(merged_df['benchmark_return'])
+            variance = merged_df['benchmark_return'].var()
+            beta = covariance / variance if variance != 0 else None
+
+            # 计算 跟踪误差 (年化)
+            merged_df['difference'] = merged_df['etf_return'] - merged_df['benchmark_return']
+            tracking_error = merged_df['difference'].std() * np.sqrt(252)
+            
+            # 计算 夏普比率 (年化)
+            risk_free_rate_daily = (1.02 ** (1/252)) - 1 # 假设年化无风险利率为2%
+            avg_daily_return = merged_df['etf_return'].mean()
+            std_daily_return = merged_df['etf_return'].std()
+            sharpe_ratio = ((avg_daily_return - risk_free_rate_daily) * 252) / (std_daily_return * np.sqrt(252)) if std_daily_return != 0 else None
+            
+            # 3. 溢价/折价率 (近一个月平均)
+            avg_premium_discount = None
+            df_for_premium = self.hist_df.copy()
+            if '单位净值' in df_for_premium.columns and not df_for_premium['单位净值'].isnull().all():
+                df_for_premium['单位净值'] = pd.to_numeric(df_for_premium['单位净值'], errors='coerce')
+                df_for_premium.dropna(subset=['单位净值'], inplace=True)
+                df_for_premium = df_for_premium[df_for_premium['单位净值'] != 0]
+                
+                if not df_for_premium.empty:
+                    df_for_premium['premium_discount'] = ((df_for_premium['收盘'] / df_for_premium['单位净值']) - 1) * 100
+                    avg_premium_discount = df_for_premium.tail(21)['premium_discount'].mean()
+
+            risk_metrics = {
+                "annualized_volatility": annualized_volatility,
+                "beta": beta,
+                "tracking_error": tracking_error,
+                "sharpe_ratio": sharpe_ratio,
+                "avg_premium_discount_monthly": avg_premium_discount
+            }
+            
+            self.analysis_result['risk_and_tracking'] = risk_metrics
+            print("风险与跟踪能力分析完成。")
+
+        except Exception as e:
+            print(f"分析风险与跟踪能力时出错: {e}")
+            self.analysis_result['risk_and_tracking'] = {"error": f"分析风险与跟踪能力失败: {e}"}
+
+    def analyze_holdings(self):
+        """
+        5. 持仓分析
+        """
+        print("开始分析持仓...")
+        try:
+            # 获取ETF持仓明细
+            holdings_df = ak.fund_portfolio_hold_em(symbol=self.etf_code, date=datetime.now().strftime("%Y"))
+            
+            if holdings_df.empty or '股票代码' not in holdings_df.columns:
+                 self.analysis_result['holdings'] = {"error": "未能获取到该ETF的持仓数据。"}
+                 print("未能获取持仓数据。")
+                 return
+            
+            # 提取前十大持仓
+            top_10_holdings = holdings_df.head(10)[['股票代码', '股票名称', '持仓市值', '占净值比例']].copy()
+            top_10_holdings.rename(columns={'占净值比例': '占净值比例(%)'}, inplace=True)
+            top_10_holdings['占净值比例(%)'] = pd.to_numeric(top_10_holdings['占净值比例(%)'], errors='coerce')
+            
+            # 计算前十大持仓集中度
+            concentration = top_10_holdings['占净值比例(%)'].sum()
+
+            holdings_data = {
+                "top_10_holdings": top_10_holdings.to_dict('records'),
+                "concentration": concentration
+            }
+            
+            self.analysis_result['holdings'] = holdings_data
+            print("持仓分析完成。")
+
+        except Exception as e:
+            print(f"分析持仓时出错: {e}")
+            self.analysis_result['holdings'] = {"error": f"分析持仓失败: {e}"}
+
+    def analyze_sector(self):
+        """
+        6. 板块深度分析
+        """
+        print("开始进行板块深度分析...")
+        try:
+            # 1. 识别板块/行业
+            basic_info = self.analysis_result.get('basic_info', {})
+            tracking_index = basic_info.get('跟踪标的', '')
+            
+            if not tracking_index or '指数' not in tracking_index:
+                self.analysis_result['sector_analysis'] = {"error": "无法从基本信息中确定ETF跟踪的板块或行业指数。"}
+                print("无法识别板块，跳过板块分析。")
+                return
+
+            # 简化处理：假设跟踪标的名称与akshare中的板块名称直接对应
+            # 例如 "中证白酒指数" -> 我们需要找到对应的板块名称 "白酒"
+            # 这是一个复杂的映射，这里我们先做一个简化假设，后续可以优化
+            sector_name = tracking_index.replace('指数', '').replace('中证', '').replace('国证', '')
+            
+            # 尝试获取行业板块数据，如果失败，则可能是概念板块
+            try:
+                sector_df = ak.stock_board_industry_hist_em(symbol=sector_name)
+            except Exception:
+                try:
+                    sector_df = ak.stock_board_concept_hist_em(symbol=sector_name)
+                except Exception as e:
+                    self.analysis_result['sector_analysis'] = {"error": f"无法获取板块 '{sector_name}' 的行情数据: {e}"}
+                    return
+
+            sector_df['日期'] = pd.to_datetime(sector_df['日期'])
+            sector_df.set_index('日期', inplace=True)
+
+            # 2. 板块回报率 (景气度)
+            sector_returns = {}
+            latest_price = sector_df['收盘'].iloc[-1]
+            periods = {'近1周': 5, '近1个月': 21, '近3个月': 63, '近1年': 252}
+            for name, days in periods.items():
+                if len(sector_df) > days:
+                    old_price = sector_df['收盘'].iloc[-days-1]
+                    sector_returns[name] = ((latest_price / old_price) - 1) * 100 if old_price != 0 else 0
+            
+            # 3. 板块估值 (PE百分位)
+            pe_df = ak.stock_board_industry_pe_em(symbol=sector_name)
+            latest_pe = pe_df.iloc[-1]['滚动市盈率']
+            pe_percentile = (pe_df['滚动市盈率'] < latest_pe).mean() * 100 if not pe_df.empty else None
+
+            sector_data = {
+                "sector_name": sector_name,
+                "returns": sector_returns,
+                "valuation": {
+                    "current_pe": latest_pe,
+                    "pe_percentile": pe_percentile
+                }
+            }
+            
+            self.analysis_result['sector_analysis'] = sector_data
+            print("板块深度分析完成。")
+
+        except Exception as e:
+            print(f"分析板块时出错: {e}")
+            self.analysis_result['sector_analysis'] = {"error": f"分析板块失败: {e}"}
+
+    def get_ai_summary(self):
+        """
+        7. AI 综合诊断
+        """
+        print("开始生成AI综合诊断...")
+        try:
+            # 1. 整合所有分析结果
+            prompt_data = f"请为ETF代码 {self.etf_code} 生成一份全面的投资分析报告。请严格根据以下数据进行分析，不要使用外部知识：\n\n"
+            
+            # 基本信息
+            basic_info = self.analysis_result.get('basic_info', {})
+            prompt_data += f"**基本信息**: 跟踪指数: {basic_info.get('跟踪标的', 'N/A')}, 规模: {basic_info.get('基金规模', 'N/A')}, 管理人: {basic_info.get('基金管理人', 'N/A')}.\n"
+
+            # 市场表现
+            perf = self.analysis_result.get('market_performance', {})
+            returns = perf.get('returns', {})
+            alpha = perf.get('alpha', {})
+            prompt_data += f"**市场表现**: 近1个月回报率: {returns.get('近1个月', 0):.2f}%, 同期超额收益(相对沪深300): {alpha.get('近1个月', 0):.2f}%. 年初至今回报率: {returns.get('年初至今', 0):.2f}%, 同期超额收益: {alpha.get('年初至今', 0):.2f}%.\n"
+
+            # 风险
+            risk = self.analysis_result.get('risk_and_tracking', {})
+            prompt_data += f"**风险指标**: 年化波动率: {risk.get('annualized_volatility', 0):.2%}, Beta值: {risk.get('beta', 0):.2f}, 年化跟踪误差: {risk.get('tracking_error', 0):.2%}.\n"
+
+            # 持仓
+            holdings = self.analysis_result.get('holdings', {})
+            prompt_data += f"**持仓结构**: 前十大持仓集中度: {holdings.get('concentration', 0):.2f}%.\n"
+
+            # 板块
+            sector = self.analysis_result.get('sector_analysis', {})
+            sector_val = sector.get('valuation', {})
+            prompt_data += f"**板块分析**: 所属板块: {sector.get('sector_name', 'N/A')}, 当前滚动PE находится at {sector_val.get('pe_percentile', 0):.2f}% 历史分位点.\n\n"
+
+            # 2. 构建Prompt
+            prompt_data += "请根据以上数据，从以下三个方面进行分析，并给出一个最终总结：\n1. **核心优势**: 这只ETF最主要的投资亮点是什么？\n2. **潜在风险**: 投资者需要注意哪些潜在风险？\n3. **板块前景**: 结合板块估值和前景，对该ETF的赛道进行评价。\n4. **最终结论**: 给出一个不超过50字的简明投资结论。"
+            
+            # 3. 调用AI模型
+            if self.stock_analyzer:
+                ai_summary = self.stock_analyzer.get_ai_analysis_from_prompt(prompt_data)
+            else:
+                ai_summary = "AI分析器未初始化，无法生成摘要。"
+
+            self.analysis_result['ai_summary'] = {"message": ai_summary}
+            print("AI综合诊断生成完成。")
+
+        except Exception as e:
+            print(f"生成AI摘要时出错: {e}")
+            self.analysis_result['ai_summary'] = {"error": f"生成AI摘要失败: {e}"}
+
+if __name__ == '__main__':
+    # For testing purposes
+    test_etf = '510300'  # 沪深300 ETF
+    analyzer = EtfAnalyzer(test_etf)
+    results = analyzer.run_analysis()
+    import json
+    print(json.dumps(results, indent=4, ensure_ascii=False))

app/analysis/fundamental_analyzer.py

@@ -0,0 +1,199 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# fundamental_analyzer.py
+import akshare as ak
+import pandas as pd
+import numpy as np
+
+
+class FundamentalAnalyzer:
+    def __init__(self):
+        """初始化基础分析类"""
+        self.data_cache = {}
+
+    def get_financial_indicators(self, stock_code, progress_callback=None):
+        """获取财务指标数据"""
+        if progress_callback:
+            progress_callback(5, "正在获取财务指标...")
+        try:
+            # 获取基本财务指标
+            financial_data = ak.stock_financial_analysis_indicator(symbol=stock_code,start_year="2022")
+
+            # 获取最新估值指标
+            valuation = ak.stock_value_em(symbol=stock_code)
+
+            # 整合数据
+            indicators = {
+                'pe_ttm': float(valuation['PE(TTM)'].iloc[0]),
+                'pb': float(valuation['市净率'].iloc[0]),
+                'ps_ttm': float(valuation['市销率'].iloc[0]),
+                'roe': float(financial_data['加权净资产收益率(%)'].iloc[0]),
+                'gross_margin': float(financial_data['销售毛利率(%)'].iloc[0]),
+                'net_profit_margin': float(financial_data['总资产净利润率(%)'].iloc[0]),
+                'debt_ratio': float(financial_data['资产负债率(%)'].iloc[0])
+            }
+            if progress_callback:
+                progress_callback(10, "财务指标获取成功")
+            return indicators
+        except Exception as e:
+            print(f"获取财务指标出错: {str(e)}")
+            if progress_callback:
+                progress_callback(10, f"财务指标获取失败: {e}")
+            return {}
+
+    def get_growth_data(self, stock_code, progress_callback=None):
+        """获取成长性数据"""
+        if progress_callback:
+            progress_callback(15, "正在获取成长性数据...")
+        try:
+            # 获取历年财务数据
+            financial_data = ak.stock_financial_abstract(symbol=stock_code)
+
+            # --- 修复：兼容不同的财务字段名 ---
+            # 查找营业收入列
+            revenue_col = None
+            if '营业总收入' in financial_data.columns:
+                revenue_col = '营业总收入'
+            elif '营业收入' in financial_data.columns:
+                revenue_col = '营业收入'
+            
+            # 查找净利润列
+            profit_col = None
+            if '归属母公司股东的净利润' in financial_data.columns:
+                profit_col = '归属母公司股东的净利润'
+            elif '净利润' in financial_data.columns:
+                profit_col = '净利润'
+
+            growth = {}
+            # 仅在找到列时计算
+            if revenue_col:
+                revenue = financial_data[revenue_col].astype(float)
+                growth['revenue_growth_3y'] = self._calculate_cagr(revenue, 3)
+                growth['revenue_growth_5y'] = self._calculate_cagr(revenue, 5)
+            else:
+                print(f"警告: 股票 {stock_code} 未找到 '营业总收入' 或 '营业收入' 列")
+
+            if profit_col:
+                net_profit = financial_data[profit_col].astype(float)
+                growth['profit_growth_3y'] = self._calculate_cagr(net_profit, 3)
+                growth['profit_growth_5y'] = self._calculate_cagr(net_profit, 5)
+            else:
+                print(f"警告: 股票 {stock_code} 未找到 '归属母公司股东的净利润' 或 '净利润' 列")
+            # --- 修复结束 ---
+
+            if progress_callback:
+                progress_callback(20, "成长性数据获取成功")
+            return growth
+        except Exception as e:
+            # 保持现有的异常捕获，以防akshare调用本身失败
+            print(f"获取成长数据出错: {str(e)}")
+            if progress_callback:
+                progress_callback(20, f"成长性数据获取失败: {e}")
+            return {}
+
+    def _calculate_cagr(self, series, years):
+        """计算复合年增长率"""
+        if len(series) < years:
+            return None
+
+        latest = series.iloc[0]
+        earlier = series.iloc[min(years, len(series) - 1)]
+
+        if earlier <= 0:
+            return None
+
+        return ((latest / earlier) ** (1 / years) - 1) * 100
+
+    def calculate_fundamental_score(self, stock_code, progress_callback=None):
+        """计算基本面综合评分"""
+        if progress_callback:
+            progress_callback(0, "启动基本面分析模块...")
+
+        indicators = self.get_financial_indicators(stock_code, progress_callback=progress_callback)
+        growth = self.get_growth_data(stock_code, progress_callback=progress_callback)
+
+        if progress_callback:
+            progress_callback(25, "计算基本面综合评分...")
+
+        # 估值评分 (30分)
+        valuation_score = 0
+        if 'pe_ttm' in indicators and indicators['pe_ttm'] > 0:
+            pe = indicators['pe_ttm']
+            if pe < 15:
+                valuation_score += 25
+            elif pe < 25:
+                valuation_score += 20
+            elif pe < 35:
+                valuation_score += 15
+            elif pe < 50:
+                valuation_score += 10
+            else:
+                valuation_score += 5
+
+        # 财务健康评分 (40分)
+        financial_score = 0
+        if 'roe' in indicators:
+            roe = indicators['roe']
+            if roe > 20:
+                financial_score += 15
+            elif roe > 15:
+                financial_score += 12
+            elif roe > 10:
+                financial_score += 8
+            elif roe > 5:
+                financial_score += 4
+
+        if 'debt_ratio' in indicators:
+            debt_ratio = indicators['debt_ratio']
+            if debt_ratio < 30:
+                financial_score += 15
+            elif debt_ratio < 50:
+                financial_score += 10
+            elif debt_ratio < 70:
+                financial_score += 5
+
+        # 成长性评分 (30分)
+        growth_score = 0
+        if 'revenue_growth_3y' in growth and growth['revenue_growth_3y']:
+            rev_growth = growth['revenue_growth_3y']
+            if rev_growth > 30:
+                growth_score += 15
+            elif rev_growth > 20:
+                growth_score += 12
+            elif rev_growth > 10:
+                growth_score += 8
+            elif rev_growth > 0:
+                growth_score += 4
+
+        if 'profit_growth_3y' in growth and growth['profit_growth_3y']:
+            profit_growth = growth['profit_growth_3y']
+            if profit_growth > 30:
+                growth_score += 15
+            elif profit_growth > 20:
+                growth_score += 12
+            elif profit_growth > 10:
+                growth_score += 8
+            elif profit_growth > 0:
+                growth_score += 4
+
+        # 计算总分
+        total_score = valuation_score + financial_score + growth_score
+        
+        if progress_callback:
+            progress_callback(30, "基本面分析完成")
+
+        return {
+            'total': total_score,
+            'valuation': valuation_score,
+            'financial_health': financial_score,
+            'growth': growth_score,
+            'details': {
+                'indicators': indicators,
+                'growth': growth
+            }
+        }

app/analysis/index_industry_analyzer.py

@@ -0,0 +1,275 @@
+# index_industry_analyzer.py
+import akshare as ak
+import pandas as pd
+import numpy as np
+import threading
+
+
+class IndexIndustryAnalyzer:
+    def __init__(self, analyzer):
+        self.analyzer = analyzer
+        self.data_cache = {}
+
+    def analyze_index(self, index_code, limit=30):
+        """分析指数整体情况"""
+        try:
+            cache_key = f"index_{index_code}"
+            if cache_key in self.data_cache:
+                cache_time, cached_result = self.data_cache[cache_key]
+                # 如果缓存时间在1小时内，直接返回
+                if (pd.Timestamp.now() - cache_time).total_seconds() < 3600:
+                    return cached_result
+
+            # 获取指数成分股
+            if index_code == '000300':
+                # 沪深300成分股
+                stocks = ak.index_stock_cons_weight_csindex(symbol="000300")
+                index_name = "沪深300"
+            elif index_code == '000905':
+                # 中证500成分股
+                stocks = ak.index_stock_cons_weight_csindex(symbol="000905")
+                index_name = "中证500"
+            elif index_code == '000852':
+                # 中证1000成分股
+                stocks = ak.index_stock_cons_weight_csindex(symbol="000852")
+                index_name = "中证1000"
+            elif index_code == '000001':
+                # 上证指数
+                stocks = ak.index_stock_cons_weight_csindex(symbol="000001")
+                index_name = "上证指数"
+            else:
+                return {"error": "不支持的指数代码"}
+
+            # 提取股票代码列表和权重
+            stock_list = []
+            if '成分券代码' in stocks.columns:
+                stock_list = stocks['成分券代码'].tolist()
+                weights = stocks['权重(%)'].tolist() if '权重(%)' in stocks.columns else [1] * len(stock_list)
+            else:
+                return {"error": "获取指数成分股失败"}
+
+            # 限制分析的股票数量以提高性能
+            if limit and len(stock_list) > limit:
+                # 按权重排序，取前limit只权重最大的股票
+                stock_weights = list(zip(stock_list, weights))
+                stock_weights.sort(key=lambda x: x[1], reverse=True)
+                stock_list = [s[0] for s in stock_weights[:limit]]
+                weights = [s[1] for s in stock_weights[:limit]]
+
+            # 多线程分析股票
+            results = []
+            threads = []
+            results_lock = threading.Lock()
+
+            def analyze_stock(stock_code, weight):
+                try:
+                    # 分析股票
+                    result = self.analyzer.quick_analyze_stock(stock_code)
+                    result['weight'] = weight
+
+                    with results_lock:
+                        results.append(result)
+                except Exception as e:
+                    print(f"分析股票 {stock_code} 时出错: {str(e)}")
+
+            # 创建并启动线程
+            for i, stock_code in enumerate(stock_list):
+                weight = weights[i] if i < len(weights) else 1
+                thread = threading.Thread(target=analyze_stock, args=(stock_code, weight))
+                threads.append(thread)
+                thread.start()
+
+            # 等待所有线程完成
+            for thread in threads:
+                thread.join()
+
+            # 计算指数整体情况
+            total_weight = sum([r.get('weight', 1) for r in results])
+
+            # 计算加权评分
+            index_score = 0
+            if total_weight > 0:
+                index_score = sum([r.get('score', 0) * r.get('weight', 1) for r in results]) / total_weight
+
+            # 计算其他指标
+            up_count = sum(1 for r in results if r.get('price_change', 0) > 0)
+            down_count = sum(1 for r in results if r.get('price_change', 0) < 0)
+            flat_count = len(results) - up_count - down_count
+
+            # 计算涨跌股比例
+            up_ratio = up_count / len(results) if len(results) > 0 else 0
+
+            # 计算加权平均涨跌幅
+            weighted_change = 0
+            if total_weight > 0:
+                weighted_change = sum([r.get('price_change', 0) * r.get('weight', 1) for r in results]) / total_weight
+
+            # 按评分对股票排序
+            results.sort(key=lambda x: x.get('score', 0), reverse=True)
+
+            # 整理结果
+            index_analysis = {
+                "index_code": index_code,
+                "index_name": index_name,
+                "score": round(index_score, 2),
+                "stock_count": len(results),
+                "up_count": up_count,
+                "down_count": down_count,
+                "flat_count": flat_count,
+                "up_ratio": up_ratio,
+                "weighted_change": weighted_change,
+                "top_stocks": results[:5] if len(results) >= 5 else results,
+                "results": results
+            }
+
+            # 缓存结果
+            self.data_cache[cache_key] = (pd.Timestamp.now(), index_analysis)
+
+            return index_analysis
+
+        except Exception as e:
+            print(f"分析指数整体情况时出错: {str(e)}")
+            return {"error": f"分析指数时出错: {str(e)}"}
+
+    def analyze_industry(self, industry, limit=30):
+        """分析行业整体情况"""
+        try:
+            cache_key = f"industry_{industry}"
+            if cache_key in self.data_cache:
+                cache_time, cached_result = self.data_cache[cache_key]
+                # 如果缓存时间在1小时内，直接返回
+                if (pd.Timestamp.now() - cache_time).total_seconds() < 3600:
+                    return cached_result
+
+            # 获取行业成分股
+            stocks = ak.stock_board_industry_cons_em(symbol=industry)
+
+            # 提取股票代码列表
+            stock_list = stocks['代码'].tolist() if '代码' in stocks.columns else []
+
+            if not stock_list:
+                return {"error": "获取行业成分股失败"}
+
+            # 限制分析的股票数量以提高性能
+            if limit and len(stock_list) > limit:
+                stock_list = stock_list[:limit]
+
+            # 多线程分析股票
+            results = []
+            threads = []
+            results_lock = threading.Lock()
+
+            def analyze_stock(stock_code):
+                try:
+                    # 分析股票
+                    result = self.analyzer.quick_analyze_stock(stock_code)
+
+                    with results_lock:
+                        results.append(result)
+                except Exception as e:
+                    print(f"分析股票 {stock_code} 时出错: {str(e)}")
+
+            # 创建并启动线程
+            for stock_code in stock_list:
+                thread = threading.Thread(target=analyze_stock, args=(stock_code,))
+                threads.append(thread)
+                thread.start()
+
+            # 等待所有线程完成
+            for thread in threads:
+                thread.join()
+
+            # 计算行业整体情况
+            if not results:
+                return {"error": "分析行业股票失败"}
+
+            # 计算平均评分
+            industry_score = sum([r.get('score', 0) for r in results]) / len(results)
+
+            # 计算其他指标
+            up_count = sum(1 for r in results if r.get('price_change', 0) > 0)
+            down_count = sum(1 for r in results if r.get('price_change', 0) < 0)
+            flat_count = len(results) - up_count - down_count
+
+            # 计算涨跌股比例
+            up_ratio = up_count / len(results)
+
+            # 计算平均涨跌幅
+            avg_change = sum([r.get('price_change', 0) for r in results]) / len(results)
+
+            # 按评分对股票排序
+            results.sort(key=lambda x: x.get('score', 0), reverse=True)
+
+            # 整理结果
+            industry_analysis = {
+                "industry": industry,
+                "score": round(industry_score, 2),
+                "stock_count": len(results),
+                "up_count": up_count,
+                "down_count": down_count,
+                "flat_count": flat_count,
+                "up_ratio": up_ratio,
+                "avg_change": avg_change,
+                "top_stocks": results[:5] if len(results) >= 5 else results,
+                "results": results
+            }
+
+            # 缓存结果
+            self.data_cache[cache_key] = (pd.Timestamp.now(), industry_analysis)
+
+            return industry_analysis
+
+        except Exception as e:
+            print(f"分析行业整体情况时出错: {str(e)}")
+            return {"error": f"分析行业时出错: {str(e)}"}
+
+    def compare_industries(self, limit=10):
+        """比较不同行业的表现"""
+        try:
+            # 获取行业板块数据
+            industry_data = ak.stock_board_industry_name_em()
+
+            # 提取行业名称列表
+            industries = industry_data['板块名称'].tolist() if '板块名称' in industry_data.columns else []
+
+            if not industries:
+                return {"error": "获取行业列表失败"}
+
+            # 限制分析的行业数量
+            industries = industries[:limit] if limit else industries
+
+            # 分析各行业情况
+            industry_results = []
+
+            for industry in industries:
+                try:
+                    # 简化分析，只获取基本指标
+                    industry_info = ak.stock_board_industry_hist_em(symbol=industry, period="3m")
+
+                    # 计算行业涨跌幅
+                    if not industry_info.empty:
+                        latest = industry_info.iloc[0]
+                        change = latest['涨跌幅'] if '涨跌幅' in latest.index else 0
+
+                        industry_results.append({
+                            "industry": industry,
+                            "change": change,
+                            "volume": latest['成交量'] if '成交量' in latest.index else 0,
+                            "turnover": latest['成交额'] if '成交额' in latest.index else 0
+                        })
+                except Exception as e:
+                    print(f"分析行业 {industry} 时出错: {str(e)}")
+
+            # 按涨跌幅排序
+            industry_results.sort(key=lambda x: x.get('change', 0), reverse=True)
+
+            return {
+                "count": len(industry_results),
+                "top_industries": industry_results[:5] if len(industry_results) >= 5 else industry_results,
+                "bottom_industries": industry_results[-5:] if len(industry_results) >= 5 else [],
+                "results": industry_results
+            }
+
+        except Exception as e:
+            print(f"比较行业表现时出错: {str(e)}")
+            return {"error": f"比较行业表现时出错: {str(e)}"}

app/analysis/industry_analyzer.py

@@ -0,0 +1,533 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# industry_analyzer.py
+import logging
+import random
+import akshare as ak
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+
+class IndustryAnalyzer:
+    def __init__(self):
+        """初始化行业分析类"""
+        self.data_cache = {}
+        self.industry_code_map = {}  # 缓存行业名称到代码的映射
+
+        # 设置日志记录
+        logging.basicConfig(level=logging.INFO,
+                            format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger(__name__)
+
+    def get_industry_fund_flow(self, symbol="即时"):
+        """获取行业资金流向数据"""
+        try:
+            # 缓存键
+            cache_key = f"industry_fund_flow_{symbol}"
+
+            # 检查缓存
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果缓存时间在30分钟内，直接返回
+                if (datetime.now() - cache_time).total_seconds() < 1800:
+                    self.logger.info(f"从缓存获取行业资金流向数据: {symbol}")
+                    return cached_data
+
+            # 获取行业资金流向数据
+            self.logger.info(f"从API获取行业资金流向数据: {symbol}")
+            fund_flow_data = ak.stock_fund_flow_industry(symbol=symbol)
+
+            # 打印列名以便调试
+            self.logger.info(f"行业资金流向数据列名: {fund_flow_data.columns.tolist()}")
+
+            # 转换为字典列表
+            result = []
+
+            if symbol == "即时":
+                for _, row in fund_flow_data.iterrows():
+                    try:
+                        # 安全地将值转换为对应的类型
+                        item = {
+                            "rank": self._safe_int(row["序号"]),
+                            "industry": str(row["行业"]),
+                            "index": self._safe_float(row["行业指数"]),
+                            "change": self._safe_percent(row["行业-涨跌幅"]),
+                            "inflow": self._safe_float(row["流入资金"]),
+                            "outflow": self._safe_float(row["流出资金"]),
+                            "netFlow": self._safe_float(row["净额"]),
+                            "companyCount": self._safe_int(row["公司家数"])
+                        }
+
+                        # 添加领涨股相关数据，如果存在
+                        if "领涨股" in row:
+                            item["leadingStock"] = str(row["领涨股"])
+                        if "领涨股-涨跌幅" in row:
+                            item["leadingStockChange"] = self._safe_percent(row["领涨股-涨跌幅"])
+                        if "当前价" in row:
+                            item["leadingStockPrice"] = self._safe_float(row["当前价"])
+
+                        result.append(item)
+                    except Exception as e:
+                        self.logger.warning(f"处理行业资金流向数据行时出错: {str(e)}")
+                        continue
+            else:
+                for _, row in fund_flow_data.iterrows():
+                    try:
+                        item = {
+                            "rank": self._safe_int(row["序号"]),
+                            "industry": str(row["行业"]),
+                            "companyCount": self._safe_int(row["公司家数"]),
+                            "index": self._safe_float(row["行业指数"]),
+                            "change": self._safe_percent(row["阶段涨跌幅"]),
+                            "inflow": self._safe_float(row["流入资金"]),
+                            "outflow": self._safe_float(row["流出资金"]),
+                            "netFlow": self._safe_float(row["净额"])
+                        }
+                        result.append(item)
+                    except Exception as e:
+                        self.logger.warning(f"处理行业资金流向数据行时出错: {str(e)}")
+                        continue
+
+            # 缓存结果
+            self.data_cache[cache_key] = (datetime.now(), result)
+
+            return result
+
+        except Exception as e:
+            self.logger.error(f"获取行业资金流向数据失败: {str(e)}")
+            # 返回更详细的错误信息，包括堆栈跟踪
+            import traceback
+            self.logger.error(traceback.format_exc())
+            return []
+
+    def _safe_float(self, value):
+        """安全地将值转换为浮点数"""
+        try:
+            if pd.isna(value):
+                return 0.0
+            return float(value)
+        except:
+            return 0.0
+
+    def _safe_int(self, value):
+        """安全地将值转换为整数"""
+        try:
+            if pd.isna(value):
+                return 0
+            return int(value)
+        except:
+            return 0
+
+    def _safe_percent(self, value):
+        """安全地将百分比值转换为字符串格式"""
+        try:
+            if pd.isna(value):
+                return "0.00"
+
+            # 如果是字符串并包含%，移除%符号
+            if isinstance(value, str) and "%" in value:
+                return value.replace("%", "")
+
+            # 如果是数值，直接转换成字符串
+            return str(float(value))
+        except:
+            return "0.00"
+
+    def _get_industry_code(self, industry_name):
+        """获取行业名称对应的板块代码"""
+        try:
+            # 如果已经缓存了行业代码映射，直接使用
+            if not self.industry_code_map:
+                # 获取东方财富行业板块名称及代码
+                industry_list = ak.stock_board_industry_name_em()
+
+                # 创建行业名称到代码的映射
+                for _, row in industry_list.iterrows():
+                    if '板块名称' in industry_list.columns and '板块代码' in industry_list.columns:
+                        name = row['板块名称']
+                        code = row['板块代码']
+                        self.industry_code_map[name] = code
+
+                self.logger.info(f"成功获取到 {len(self.industry_code_map)} 个行业代码映射")
+
+            # 尝试精确匹配
+            if industry_name in self.industry_code_map:
+                return self.industry_code_map[industry_name]
+
+            # 尝试模糊匹配
+            for name, code in self.industry_code_map.items():
+                if industry_name in name or name in industry_name:
+                    self.logger.info(f"行业名称 '{industry_name}' 模糊匹配到 '{name}'，代码: {code}")
+                    return code
+
+            # 如果找不到匹配项，则返回None
+            self.logger.warning(f"未找到行业 '{industry_name}' 对应的代码")
+            return None
+
+        except Exception as e:
+            self.logger.error(f"获取行业代码时出错: {str(e)}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+            return None
+
+    def get_industry_stocks(self, industry):
+        """获取行业成分股"""
+        try:
+            # 缓存键
+            cache_key = f"industry_stocks_{industry}"
+
+            # 检查缓存
+            if cache_key in self.data_cache:
+                cache_time, cached_data = self.data_cache[cache_key]
+                # 如果缓存时间在1小时内，直接返回
+                if (datetime.now() - cache_time).total_seconds() < 3600:
+                    self.logger.info(f"从缓存获取行业成分股: {industry}")
+                    return cached_data
+
+            # 获取行业成分股
+            self.logger.info(f"获取 {industry} 行业成分股")
+
+            result = []
+            try:
+                # 1. 首先尝试直接使用行业名称
+                try:
+                    stocks = ak.stock_board_industry_cons_em(symbol=industry)
+                    self.logger.info(f"使用行业名称 '{industry}' 成功获取成分股")
+                except Exception as direct_error:
+                    self.logger.warning(f"使用行业名称获取成分股失败: {str(direct_error)}")
+                    # 2. 尝试使用行业代码
+                    industry_code = self._get_industry_code(industry)
+                    if industry_code:
+                        self.logger.info(f"尝试使用行业代码 {industry_code} 获取成分股")
+                        stocks = ak.stock_board_industry_cons_em(symbol=industry_code)
+                    else:
+                        # 如果无法获取行业代码，抛出异常，进入模拟数据生成
+                        raise ValueError(f"无法找到行业 '{industry}' 对应的代码")
+
+                # 打印列名以便调试
+                self.logger.info(f"行业成分股数据列名: {stocks.columns.tolist()}")
+
+                # 转换为字典列表
+                if not stocks.empty:
+                    for _, row in stocks.iterrows():
+                        try:
+                            item = {
+                                "code": str(row["代码"]),
+                                "name": str(row["名称"]),
+                                "price": self._safe_float(row["最新价"]),
+                                "change": self._safe_float(row["涨跌幅"]),
+                                "change_amount": self._safe_float(row["涨跌额"]) if "涨跌额" in row else 0.0,
+                                "volume": self._safe_float(row["成交量"]) if "成交量" in row else 0.0,
+                                "turnover": self._safe_float(row["成交额"]) if "成交额" in row else 0.0,
+                                "amplitude": self._safe_float(row["振幅"]) if "振幅" in row else 0.0,
+                                "turnover_rate": self._safe_float(row["换手率"]) if "换手率" in row else 0.0
+                            }
+                            result.append(item)
+                        except Exception as e:
+                            self.logger.warning(f"处理行业成分股数据行时出错: {str(e)}")
+                            continue
+
+            except Exception as e:
+                # 3. 如果上述方法都失败，生成模拟数据
+                self.logger.warning(f"无法通过API获取行业成分股，使用模拟数据: {str(e)}")
+                result = self._generate_mock_industry_stocks(industry)
+
+            # 缓存结果
+            self.data_cache[cache_key] = (datetime.now(), result)
+
+            return result
+
+        except Exception as e:
+            self.logger.error(f"获取行业成分股失败: {str(e)}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+            return []
+
+    def _generate_mock_industry_stocks(self, industry):
+        """生成模拟的行业成分股数据"""
+        self.logger.info(f"生成行业 {industry} 的模拟成分股数据")
+
+        # 使用来自资金流向的行业数据获取该行业的基本信息
+        fund_flow_data = self.get_industry_fund_flow("即时")
+        industry_data = next((item for item in fund_flow_data if item["industry"] == industry), None)
+
+        company_count = 20  # 默认值
+        if industry_data and "companyCount" in industry_data:
+            company_count = min(industry_data["companyCount"], 30)  # 限制最多30只股票
+
+        # 生成模拟股票
+        result = []
+        for i in range(company_count):
+            # 生成6位数字的股票代码，确保前缀是0或6
+            prefix = "6" if i % 2 == 0 else "0"
+            code = prefix + str(100000 + i).zfill(5)[-5:]
+
+            # 生成股票价格和涨跌幅
+            price = round(random.uniform(10, 100), 2)
+            change = round(random.uniform(-5, 5), 2)
+
+            # 生成成交量和成交额
+            volume = round(random.uniform(100000, 10000000))
+            turnover = round(volume * price / 10000, 2)  # 转换为万元
+
+            # 生成换手率和振幅
+            turnover_rate = round(random.uniform(0.5, 5), 2)
+            amplitude = round(random.uniform(1, 10), 2)
+
+            item = {
+                "code": code,
+                "name": f"{industry}股{i + 1}",
+                "price": price,
+                "change": change,
+                "change_amount": round(price * change / 100, 2),
+                "volume": volume,
+                "turnover": turnover,
+                "amplitude": amplitude,
+                "turnover_rate": turnover_rate
+            }
+            result.append(item)
+
+        # 按涨跌幅排序
+        result.sort(key=lambda x: x["change"], reverse=True)
+
+        return result
+
+    def get_industry_detail(self, industry):
+        """获取行业详细信息"""
+        try:
+            # 获取行业资金流向数据
+            fund_flow_data = self.get_industry_fund_flow("即时")
+            industry_data = next((item for item in fund_flow_data if item["industry"] == industry), None)
+
+            if not industry_data:
+                return None
+
+            # 获取历史资金流向数据
+            history_data = []
+
+            for period in ["3日排行", "5日排行", "10日排行", "20日排行"]:
+                period_data = self.get_industry_fund_flow(period)
+                industry_period_data = next((item for item in period_data if item["industry"] == industry), None)
+
+                if industry_period_data:
+                    days = int(period.replace("日排行", ""))
+                    date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
+
+                    history_data.append({
+                        "date": date,
+                        "inflow": industry_period_data["inflow"],
+                        "outflow": industry_period_data["outflow"],
+                        "netFlow": industry_period_data["netFlow"],
+                        "change": industry_period_data["change"]
+                    })
+
+            # 添加即时数据
+            history_data.append({
+                "date": datetime.now().strftime("%Y-%m-%d"),
+                "inflow": industry_data["inflow"],
+                "outflow": industry_data["outflow"],
+                "netFlow": industry_data["netFlow"],
+                "change": industry_data["change"]
+            })
+
+            # 按日期排序
+            history_data.sort(key=lambda x: x["date"])
+
+            # 计算行业评分
+            score = self.calculate_industry_score(industry_data, history_data)
+
+            # 生成投资建议
+            recommendation = self.generate_industry_recommendation(score, industry_data, history_data)
+
+            # 构建结果
+            result = {
+                "industry": industry,
+                "index": industry_data["index"],
+                "change": industry_data["change"],
+                "companyCount": industry_data["companyCount"],
+                "inflow": industry_data["inflow"],
+                "outflow": industry_data["outflow"],
+                "netFlow": industry_data["netFlow"],
+                "leadingStock": industry_data.get("leadingStock", ""),
+                "leadingStockChange": industry_data.get("leadingStockChange", ""),
+                "leadingStockPrice": industry_data.get("leadingStockPrice", 0),
+                "score": score,
+                "recommendation": recommendation,
+                "flowHistory": history_data
+            }
+
+            return result
+
+        except Exception as e:
+            self.logger.error(f"获取行业详细信息失败: {str(e)}")
+            import traceback
+            self.logger.error(traceback.format_exc())
+            return None
+
+    def calculate_industry_score(self, industry_data, history_data):
+        """计算行业评分"""
+        try:
+            # 基础分数为50分
+            score = 50
+
+            # 根据涨跌幅增减分数（-10到+10）
+            change = float(industry_data["change"])
+            if change > 3:
+                score += 10
+            elif change > 1:
+                score += 5
+            elif change < -3:
+                score -= 10
+            elif change < -1:
+                score -= 5
+
+            # 根据资金流向增减分数（-20到+20）
+            netFlow = float(industry_data["netFlow"])
+
+            if netFlow > 5:
+                score += 20
+            elif netFlow > 2:
+                score += 15
+            elif netFlow > 0:
+                score += 10
+            elif netFlow < -5:
+                score -= 20
+            elif netFlow < -2:
+                score -= 15
+            elif netFlow < 0:
+                score -= 10
+
+            # 根据历史资金流向趋势增减分数（-10到+10）
+            if len(history_data) >= 2:
+                net_flow_trend = 0
+                for i in range(1, len(history_data)):
+                    if float(history_data[i]["netFlow"]) > float(history_data[i - 1]["netFlow"]):
+                        net_flow_trend += 1
+                    else:
+                        net_flow_trend -= 1
+
+                if net_flow_trend > 0:
+                    score += 10
+                elif net_flow_trend < 0:
+                    score -= 10
+
+            # 限制分数在0-100之间
+            score = max(0, min(100, score))
+
+            return round(score)
+
+        except Exception as e:
+            self.logger.error(f"计算行业评分时出错: {str(e)}")
+            return 50
+
+    def generate_industry_recommendation(self, score, industry_data, history_data):
+        """生成行业投资建议"""
+        try:
+            if score >= 80:
+                return "行业景气度高，资金持续流入，建议积极配置"
+            elif score >= 60:
+                return "行业表现良好，建议适当加仓"
+            elif score >= 40:
+                return "行业表现一般，建议谨慎参与"
+            else:
+                return "行业下行趋势明显，建议减持规避风险"
+
+        except Exception as e:
+            self.logger.error(f"生成行业投资建议时出错: {str(e)}")
+            return "无法生成投资建议"
+
+    def compare_industries(self, limit=10):
+        """比较不同行业的表现"""
+        try:
+            # 获取行业板块数据
+            industry_data = ak.stock_board_industry_name_em()
+
+            # 提取行业名称列表
+            industries = industry_data['板块名称'].tolist() if '板块名称' in industry_data.columns else []
+
+            if not industries:
+                return {"error": "获取行业列表失败"}
+
+            # 限制分析的行业数量
+            industries = industries[:limit] if limit else industries
+
+            # 分析各行业情况
+            industry_results = []
+
+            for industry in industries:
+                try:
+                    # 尝试获取行业板块代码
+                    industry_code = None
+                    for _, row in industry_data.iterrows():
+                        if row['板块名称'] == industry:
+                            industry_code = row['板块代码']
+                            break
+
+                    if not industry_code:
+                        self.logger.warning(f"未找到行业 {industry} 的板块代码")
+                        continue
+
+                    # 尝试使用不同的参数来获取行业数据 - 不使用"3m"
+                    try:
+                        # 尝试不使用period参数
+                        industry_info = ak.stock_board_industry_hist_em(symbol=industry_code)
+                    except Exception as e1:
+                        try:
+                            # 尝试使用daily参数
+                            industry_info = ak.stock_board_industry_hist_em(symbol=industry_code, period="daily")
+                        except Exception as e2:
+                            self.logger.warning(f"分析行业 {industry} 历史数据失败: {str(e1)}, {str(e2)}")
+                            continue
+
+                    # 计算行业涨跌幅
+                    if not industry_info.empty:
+                        latest = industry_info.iloc[0]
+
+                        # 尝试获取涨跌幅，列名可能有变化
+                        change = 0.0
+                        if '涨跌幅' in latest.index:
+                            change = latest['涨跌幅']
+                        elif '涨跌幅' in industry_info.columns:
+                            change = latest['涨跌幅']
+
+                        # 尝试获取成交量和成交额
+                        volume = 0.0
+                        turnover = 0.0
+                        if '成交量' in latest.index:
+                            volume = latest['成交量']
+                        elif '成交量' in industry_info.columns:
+                            volume = latest['成交量']
+
+                        if '成交额' in latest.index:
+                            turnover = latest['成交额']
+                        elif '成交额' in industry_info.columns:
+                            turnover = latest['成交额']
+
+                        industry_results.append({
+                            "industry": industry,
+                            "change": float(change) if change else 0.0,
+                            "volume": float(volume) if volume else 0.0,
+                            "turnover": float(turnover) if turnover else 0.0
+                        })
+                except Exception as e:
+                    self.logger.error(f"分析行业 {industry} 时出错: {str(e)}")
+
+            # 按涨跌幅排序
+            industry_results.sort(key=lambda x: x.get('change', 0), reverse=True)
+
+            return {
+                "count": len(industry_results),
+                "top_industries": industry_results[:5] if len(industry_results) >= 5 else industry_results,
+                "bottom_industries": industry_results[-5:] if len(industry_results) >= 5 else [],
+                "results": industry_results
+            }
+
+        except Exception as e:
+            self.logger.error(f"比较行业表现时出错: {str(e)}")
+            return {"error": f"比较行业表现时出错: {str(e)}"}

app/analysis/news_fetcher.py

@@ -0,0 +1,282 @@
+# news_fetcher.py
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 新闻数据获取模块
+功能: 获取财联社电报新闻数据并缓存到本地，避免重复内容
+"""
+
+import os
+import json
+import logging
+import time
+import hashlib
+from datetime import datetime, timedelta, date
+import akshare as ak
+import pandas as pd
+
+# 设置日志
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger('news_fetcher')
+
+# 自定义JSON编码器，处理日期类型
+class DateEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, (datetime, date)):
+            return obj.isoformat()
+        if pd.isna(obj):  # 处理pandas中的NaN
+            return None
+        return super(DateEncoder, self).default(obj)
+
+class NewsFetcher:
+    def __init__(self, save_dir="data/news"):
+        """初始化新闻获取器"""
+        self.save_dir = save_dir
+        # 确保保存目录存在
+        os.makedirs(self.save_dir, exist_ok=True)
+        self.last_fetch_time = None
+
+        # 哈希集合用于快速判断新闻是否已存在
+        self.news_hashes = set()
+        # 加载已有的新闻哈希
+        self._load_existing_hashes()
+
+    def _load_existing_hashes(self):
+        """加载已有文件中的新闻哈希值"""
+        try:
+            # 获取最近3天的文件来加载哈希值
+            today = datetime.now()
+            for i in range(3):  # 检查今天和前两天的数据
+                date = today - timedelta(days=i)
+                filename = self.get_news_filename(date)
+
+                if os.path.exists(filename):
+                    with open(filename, 'r', encoding='utf-8') as f:
+                        try:
+                            news_data = json.load(f)
+                            for item in news_data:
+                                # 如果有哈希字段就直接使用，否则计算新的哈希
+                                if 'hash' in item:
+                                    self.news_hashes.add(item['hash'])
+                                else:
+                                    content_hash = self._calculate_hash(item['content'])
+                                    self.news_hashes.add(content_hash)
+                        except json.JSONDecodeError:
+                            logger.warning(f"文件 {filename} 格式错误，跳过加载哈希值")
+
+            logger.info(f"已加载 {len(self.news_hashes)} 条新闻哈希值")
+        except Exception as e:
+            logger.error(f"加载现有新闻哈希值时出错: {str(e)}")
+            # 出错时清空哈希集合，保证程序可以继续运行
+            self.news_hashes = set()
+
+    def _calculate_hash(self, content):
+        """计算新闻内容的哈希值"""
+        # 使用MD5哈希算法计算内容的哈希值
+        # 对于财经新闻，内容通常是唯一的标识，所以只对内容计算哈希
+        return hashlib.md5(str(content).encode('utf-8')).hexdigest()
+
+    def get_news_filename(self, date=None):
+        """获取指定日期的新闻文件名"""
+        if date is None:
+            date = datetime.now().strftime('%Y%m%d')
+        else:
+            date = date.strftime('%Y%m%d')
+        return os.path.join(self.save_dir, f"news_{date}.json")
+
+    def fetch_and_save(self):
+        """获取新闻并保存到JSON文件，避免重复内容"""
+        try:
+            # 获取当前时间
+            now = datetime.now()
+
+            # 调用AKShare API获取财联社电报数据
+            logger.info("开始获取财联社电报数据")
+            stock_info_global_cls_df = ak.stock_info_global_cls(symbol="全部")
+
+            if stock_info_global_cls_df.empty:
+                logger.warning("获取的财联社电报数据为空")
+                return False
+
+            # 打印DataFrame的信息和类型，帮助调试
+            logger.info(f"获取的数据形状: {stock_info_global_cls_df.shape}")
+            logger.info(f"数据列: {stock_info_global_cls_df.columns.tolist()}")
+            logger.info(f"数据类型: \n{stock_info_global_cls_df.dtypes}")
+
+            # 计数器
+            total_count = 0
+            new_count = 0
+
+            # 转换为列表字典格式并添加哈希值
+            news_list = []
+            for _, row in stock_info_global_cls_df.iterrows():
+                total_count += 1
+
+                # 安全获取内容，确保为字符串
+                content = str(row.get("内容", ""))
+
+                # 计算内容哈希值
+                content_hash = self._calculate_hash(content)
+
+                # 检查是否已存在相同内容的新闻
+                if content_hash in self.news_hashes:
+                    continue  # 跳过已存在的新闻
+
+                # 添加新的哈希值到集合
+                self.news_hashes.add(content_hash)
+                new_count += 1
+
+                # 安全获取日期和时间，确保为字符串格式
+                pub_date = row.get("发布日期", "")
+                if isinstance(pub_date, (datetime, date)):
+                    pub_date = pub_date.isoformat()
+                else:
+                    pub_date = str(pub_date)
+
+                pub_time = row.get("发布时间", "")
+                if isinstance(pub_time, (datetime, date)):
+                    pub_time = pub_time.isoformat()
+                else:
+                    pub_time = str(pub_time)
+
+                # 创建新闻项并添加哈希值
+                news_item = {
+                    "title": str(row.get("标题", "")),
+                    "content": content,
+                    "date": pub_date,
+                    "time": pub_time,
+                    "datetime": f"{pub_date} {pub_time}",
+                    "fetch_time": now.strftime('%Y-%m-%d %H:%M:%S'),
+                    "hash": content_hash  # 保存哈希值以便后续使用
+                }
+                news_list.append(news_item)
+
+            # 如果没有新的新闻，直接返回
+            if not news_list:
+                logger.info(f"没有新的新闻数据需要保存 (共检查 {total_count} 条)")
+                return True
+
+            # 获取文件名
+            filename = self.get_news_filename()
+
+            # 如果文件已存在，则合并新旧数据
+            if os.path.exists(filename):
+                with open(filename, 'r', encoding='utf-8') as f:
+                    try:
+                        existing_data = json.load(f)
+                        # 合并数据，已经确保news_list中的内容都是新的
+                        merged_news = existing_data + news_list
+                        # 按时间排序
+                        merged_news.sort(key=lambda x: x['datetime'], reverse=True)
+                    except json.JSONDecodeError:
+                        logger.warning(f"文件 {filename} 格式错误，使用新数据替换")
+                        merged_news = sorted(news_list, key=lambda x: x['datetime'], reverse=True)
+            else:
+                # 如果文件不存在，直接使用新数据
+                merged_news = sorted(news_list, key=lambda x: x['datetime'], reverse=True)
+
+            # 保存合并后的数据，使用自定义编码器处理日期
+            with open(filename, 'w', encoding='utf-8') as f:
+                json.dump(merged_news, f, ensure_ascii=False, indent=2, cls=DateEncoder)
+
+            logger.info(f"成功保存 {new_count} 条新闻数据 (共检查 {total_count} 条，过滤重复 {total_count - new_count} 条)")
+            self.last_fetch_time = now
+            return True
+
+        except Exception as e:
+            logger.error(f"获取或保存新闻数据时出错: {str(e)}")
+            import traceback
+            logger.error(traceback.format_exc())  # 打印完整的堆栈跟踪，便于调试
+            return False
+
+    def get_latest_news(self, days=1, limit=50):
+        """获取最近几天的新闻数据，并去除重复项"""
+        news_data = []
+        today = datetime.now()
+        # 记录已处理的日期，便于日志
+        processed_dates = []
+
+        # 获取指定天数内的所有新闻
+        for i in range(days):
+            date = today - timedelta(days=i)
+            date_str = date.strftime('%Y%m%d')
+            filename = self.get_news_filename(date)
+
+            if os.path.exists(filename):
+                try:
+                    with open(filename, 'r', encoding='utf-8') as f:
+                        data = json.load(f)
+                        news_data.extend(data)
+                        processed_dates.append(date_str)
+                        logger.info(f"已加载 {date_str} 新闻数据 {len(data)} 条")
+                except Exception as e:
+                    logger.error(f"读取文件 {filename} 时出错: {str(e)}")
+            else:
+                logger.warning(f"日期 {date_str} 的新闻文件不存在: {filename}")
+
+        # 排序前记录总数
+        total_before_sort = len(news_data)
+
+        # 去除重复项
+        # 使用内容哈希或已有的哈希字段作为唯一标识
+        unique_news = {}
+        duplicate_count = 0
+
+        for item in news_data:
+            # 优先使用已有的哈希值，如果没有则计算内容哈希
+            item_hash = item.get('hash')
+            if not item_hash and 'content' in item:
+                item_hash = self._calculate_hash(item['content'])
+
+            # 如果是新的哈希值，则添加到结果中
+            if item_hash and item_hash not in unique_news:
+                unique_news[item_hash] = item
+            else:
+                duplicate_count += 1
+
+        # 转换回列表并按时间排序
+        deduplicated_news = list(unique_news.values())
+        deduplicated_news.sort(key=lambda x: x.get('datetime', ''), reverse=True)
+
+        # 限制返回条数
+        result = deduplicated_news[:limit]
+
+        logger.info(f"获取最近 {days} 天新闻(处理日期:{','.join(processed_dates)}), "
+                    f"共 {total_before_sort} 条, 去重后 {len(deduplicated_news)} 条, "
+                    f"移除重复 {duplicate_count} 条, 返回最新 {len(result)} 条")
+
+        return result
+
+# 单例模式的新闻获取器
+news_fetcher = NewsFetcher()
+
+def fetch_news_task():
+    """执行新闻获取任务"""
+    logger.info("开始执行新闻获取任务")
+    news_fetcher.fetch_and_save()
+    logger.info("新闻获取任务完成")
+
+def start_news_scheduler():
+    """启动新闻获取定时任务"""
+    import threading
+    import time
+
+    def _run_scheduler():
+        while True:
+            try:
+                fetch_news_task()
+                # 等待10分钟
+                time.sleep(600)
+            except Exception as e:
+                logger.error(f"定时任务执行出错: {str(e)}")
+                time.sleep(60)  # 出错后等待1分钟再试
+
+    # 创建并启动定时任务线程
+    scheduler_thread = threading.Thread(target=_run_scheduler)
+    scheduler_thread.daemon = True
+    scheduler_thread.start()
+    logger.info("新闻获取定时任务已启动")
+
+# 初始获取一次数据
+if __name__ == "__main__":
+    fetch_news_task()

app/analysis/risk_monitor.py

@@ -0,0 +1,375 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# risk_monitor.py
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+class RiskMonitor:
+    def __init__(self, analyzer):
+        self.analyzer = analyzer
+
+    def analyze_stock_risk(self, stock_code, market_type='A'):
+        """分析单只股票的风险"""
+        try:
+            # 获取股票数据和技术指标
+            df = self.analyzer.get_stock_data(stock_code, market_type)
+            df = self.analyzer.calculate_indicators(df)
+
+            # 计算各类风险指标
+            volatility_risk = self._analyze_volatility_risk(df)
+            trend_risk = self._analyze_trend_risk(df)
+            reversal_risk = self._analyze_reversal_risk(df)
+            volume_risk = self._analyze_volume_risk(df)
+
+            # 综合评估总体风险
+            total_risk_score = (
+                    volatility_risk['score'] * 0.3 +
+                    trend_risk['score'] * 0.3 +
+                    reversal_risk['score'] * 0.25 +
+                    volume_risk['score'] * 0.15
+            )
+
+            # 确定风险等级
+            if total_risk_score >= 80:
+                risk_level = "极高"
+            elif total_risk_score >= 60:
+                risk_level = "高"
+            elif total_risk_score >= 40:
+                risk_level = "中等"
+            elif total_risk_score >= 20:
+                risk_level = "低"
+            else:
+                risk_level = "极低"
+
+            # 生成风险警报
+            alerts = []
+
+            if volatility_risk['score'] >= 70:
+                alerts.append({
+                    "type": "volatility",
+                    "level": "高",
+                    "message": f"波动率风险较高 ({volatility_risk['value']:.2f}%)，可能面临大幅波动"
+                })
+
+            if trend_risk['score'] >= 70:
+                alerts.append({
+                    "type": "trend",
+                    "level": "高",
+                    "message": f"趋势风险较高，当前处于{trend_risk['trend']}趋势，可能面临加速下跌"
+                })
+
+            if reversal_risk['score'] >= 70:
+                alerts.append({
+                    "type": "reversal",
+                    "level": "高",
+                    "message": f"趋势反转风险较高，技术指标显示可能{reversal_risk['direction']}反转"
+                })
+
+            if volume_risk['score'] >= 70:
+                alerts.append({
+                    "type": "volume",
+                    "level": "高",
+                    "message": f"成交量异常，{volume_risk['pattern']}，可能预示价格波动"
+                })
+
+            return {
+                "total_risk_score": total_risk_score,
+                "risk_level": risk_level,
+                "volatility_risk": volatility_risk,
+                "trend_risk": trend_risk,
+                "reversal_risk": reversal_risk,
+                "volume_risk": volume_risk,
+                "alerts": alerts
+            }
+
+        except Exception as e:
+            print(f"分析股票风险出错: {str(e)}")
+            return {
+                "error": f"分析风险时出错: {str(e)}"
+            }
+
+    def _analyze_volatility_risk(self, df):
+        """分析波动率风险"""
+        # 计算近期波动率
+        recent_volatility = df.iloc[-1]['Volatility']
+
+        # 计算波动率变化
+        avg_volatility = df['Volatility'].mean()
+        volatility_change = recent_volatility / avg_volatility - 1
+
+        # 评估风险分数
+        if recent_volatility > 5 and volatility_change > 0.5:
+            score = 90  # 极高风险
+        elif recent_volatility > 4 and volatility_change > 0.3:
+            score = 75  # 高风险
+        elif recent_volatility > 3 and volatility_change > 0.1:
+            score = 60  # 中高风险
+        elif recent_volatility > 2:
+            score = 40  # 中等风险
+        elif recent_volatility > 1:
+            score = 20  # 低风险
+        else:
+            score = 0  # 极低风险
+
+        return {
+            "score": score,
+            "value": recent_volatility,
+            "change": volatility_change * 100,
+            "risk_level": "高" if score >= 60 else "中" if score >= 30 else "低"
+        }
+
+    def _analyze_trend_risk(self, df):
+        """分析趋势风险"""
+        # 获取均线数据
+        ma5 = df.iloc[-1]['MA5']
+        ma20 = df.iloc[-1]['MA20']
+        ma60 = df.iloc[-1]['MA60']
+
+        # 判断当前趋势
+        if ma5 < ma20 < ma60:
+            trend = "下降"
+
+            # 判断下跌加速程度
+            ma5_ma20_gap = (ma20 - ma5) / ma20 * 100
+
+            if ma5_ma20_gap > 5:
+                score = 90  # 极高风险
+            elif ma5_ma20_gap > 3:
+                score = 75  # 高风险
+            elif ma5_ma20_gap > 1:
+                score = 60  # 中高风险
+            else:
+                score = 50  # 中等风险
+
+        elif ma5 > ma20 > ma60:
+            trend = "上升"
+            score = 20  # 低风险
+        else:
+            trend = "盘整"
+            score = 40  # 中等风险
+
+        return {
+            "score": score,
+            "trend": trend,
+            "risk_level": "高" if score >= 60 else "中" if score >= 30 else "低"
+        }
+
+    def _analyze_reversal_risk(self, df):
+        """分析趋势反转风险"""
+        # 获取最新指标
+        rsi = df.iloc[-1]['RSI']
+        macd = df.iloc[-1]['MACD']
+        signal = df.iloc[-1]['Signal']
+        price = df.iloc[-1]['close']
+        ma20 = df.iloc[-1]['MA20']
+
+        # 判断潜在趋势反转信号
+        reversal_signals = 0
+
+        # RSI超买/超卖
+        if rsi > 75:
+            reversal_signals += 1
+            direction = "向下"
+        elif rsi < 25:
+            reversal_signals += 1
+            direction = "向上"
+        else:
+            direction = "无明确方向"
+
+        # MACD死叉/金叉
+        if macd > signal and df.iloc[-2]['MACD'] <= df.iloc[-2]['Signal']:
+            reversal_signals += 1
+            direction = "向上"
+        elif macd < signal and df.iloc[-2]['MACD'] >= df.iloc[-2]['Signal']:
+            reversal_signals += 1
+            direction = "向下"
+
+        # 价格与均线关系
+        if price > ma20 * 1.1:
+            reversal_signals += 1
+            direction = "向下"
+        elif price < ma20 * 0.9:
+            reversal_signals += 1
+            direction = "向上"
+
+        # 评估风险分数
+        if reversal_signals >= 3:
+            score = 90  # 极高风险
+        elif reversal_signals == 2:
+            score = 70  # 高风险
+        elif reversal_signals == 1:
+            score = 40  # 中等风险
+        else:
+            score = 10  # 低风险
+
+        return {
+            "score": score,
+            "reversal_signals": reversal_signals,
+            "direction": direction,
+            "risk_level": "高" if score >= 60 else "中" if score >= 30 else "低"
+        }
+
+    def _analyze_volume_risk(self, df):
+        """分析成交量风险"""
+        # 计算成交量变化
+        recent_volume = df.iloc[-1]['volume']
+        avg_volume = df['volume'].rolling(window=20).mean().iloc[-1]
+        volume_ratio = recent_volume / avg_volume
+
+        # 判断成交量模式
+        if volume_ratio > 3:
+            pattern = "成交量暴增"
+            score = 90  # 极高风险
+        elif volume_ratio > 2:
+            pattern = "成交量显著放大"
+            score = 70  # 高风险
+        elif volume_ratio > 1.5:
+            pattern = "成交量温和放大"
+            score = 50  # 中等风险
+        elif volume_ratio < 0.5:
+            pattern = "成交量萎缩"
+            score = 40  # 中低风险
+        else:
+            pattern = "成交量正常"
+            score = 20  # 低风险
+
+        # 价格与成交量背离分析
+        price_change = (df.iloc[-1]['close'] - df.iloc[-5]['close']) / df.iloc[-5]['close']
+        volume_change = (recent_volume - df.iloc[-5]['volume']) / df.iloc[-5]['volume']
+
+        if price_change > 0.05 and volume_change < -0.3:
+            pattern = "价量背离(价格上涨但量能萎缩)"
+            score = max(score, 80)  # 提高风险评分
+        elif price_change < -0.05 and volume_change < -0.3:
+            pattern = "价量同向(价格下跌且量能萎缩)"
+            score = max(score, 70)  # 提高风险评分
+        elif price_change < -0.05 and volume_change > 0.5:
+            pattern = "价量同向(价格下跌且量能放大)"
+            score = max(score, 85)  # 提高风险评分
+
+        return {
+            "score": score,
+            "volume_ratio": volume_ratio,
+            "pattern": pattern,
+            "risk_level": "高" if score >= 60 else "中" if score >= 30 else "低"
+        }
+
+    def analyze_portfolio_risk(self, portfolio):
+        """分析投资组合整体风险"""
+        try:
+            if not portfolio or len(portfolio) == 0:
+                return {"error": "投资组合为空"}
+
+            # 分析每只股票的风险
+            stock_risks = {}
+            total_weight = 0
+            weighted_risk_score = 0
+
+            for stock in portfolio:
+                stock_code = stock.get('stock_code')
+                weight = stock.get('weight', 1)
+                market_type = stock.get('market_type', 'A')
+
+                if not stock_code:
+                    continue
+
+                # 分析股票风险
+                risk = self.analyze_stock_risk(stock_code, market_type)
+                stock_risks[stock_code] = risk
+
+                # 计算加权风险分数
+                total_weight += weight
+                weighted_risk_score += risk.get('total_risk_score', 50) * weight
+
+            # 计算组��总风险分数
+            if total_weight > 0:
+                portfolio_risk_score = weighted_risk_score / total_weight
+            else:
+                portfolio_risk_score = 0
+
+            # 确定风险等级
+            if portfolio_risk_score >= 80:
+                risk_level = "极高"
+            elif portfolio_risk_score >= 60:
+                risk_level = "高"
+            elif portfolio_risk_score >= 40:
+                risk_level = "中等"
+            elif portfolio_risk_score >= 20:
+                risk_level = "低"
+            else:
+                risk_level = "极低"
+
+            # 收集高风险股票
+            high_risk_stocks = [
+                {
+                    "stock_code": code,
+                    "risk_score": risk.get('total_risk_score', 0),
+                    "risk_level": risk.get('risk_level', '未知')
+                }
+                for code, risk in stock_risks.items()
+                if risk.get('total_risk_score', 0) >= 60
+            ]
+
+            # 收集所有风险警报
+            all_alerts = []
+            for code, risk in stock_risks.items():
+                for alert in risk.get('alerts', []):
+                    all_alerts.append({
+                        "stock_code": code,
+                        **alert
+                    })
+
+            # 分析风险集中度
+            risk_concentration = self._analyze_risk_concentration(portfolio, stock_risks)
+
+            return {
+                "portfolio_risk_score": portfolio_risk_score,
+                "risk_level": risk_level,
+                "high_risk_stocks": high_risk_stocks,
+                "alerts": all_alerts,
+                "risk_concentration": risk_concentration,
+                "stock_risks": stock_risks
+            }
+
+        except Exception as e:
+            print(f"分析投资组合风险出错: {str(e)}")
+            return {
+                "error": f"分析投资组合风险时出错: {str(e)}"
+            }
+
+    def _analyze_risk_concentration(self, portfolio, stock_risks):
+        """分析风险集中度"""
+        # 分析行业集中度
+        industries = {}
+        for stock in portfolio:
+            stock_code = stock.get('stock_code')
+            stock_info = self.analyzer.get_stock_info(stock_code)
+            industry = stock_info.get('行业', '未知')
+            weight = stock.get('weight', 1)
+
+            if industry in industries:
+                industries[industry] += weight
+            else:
+                industries[industry] = weight
+
+        # 找出权重最大的行业
+        max_industry = max(industries.items(), key=lambda x: x[1]) if industries else ('未知', 0)
+
+        # 计算高风险股票总权重
+        high_risk_weight = 0
+        for stock in portfolio:
+            stock_code = stock.get('stock_code')
+            if stock_code in stock_risks and stock_risks[stock_code].get('total_risk_score', 0) >= 60:
+                high_risk_weight += stock.get('weight', 1)
+
+        return {
+            "max_industry": max_industry[0],
+            "max_industry_weight": max_industry[1],
+            "high_risk_weight": high_risk_weight
+        }

app/analysis/scenario_predictor.py

@@ -0,0 +1,254 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# scenario_predictor.py
+import os
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+import openai
+from openai import OpenAI
+import logging
+from logging.handlers import RotatingFileHandler
+"""
+
+"""
+
+# 配置日志
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(levelname)s - %(message)s')
+
+class ScenarioPredictor:
+    def __init__(self, analyzer, openai_api_key=None, openai_model=None):
+        self.analyzer = analyzer
+        self.openai_api_key = os.getenv('OPENAI_API_KEY', os.getenv('OPENAI_API_KEY'))
+        self.openai_api_url = os.getenv('OPENAI_API_URL', 'https://api.openai.com/v1')
+        self.openai_model = os.getenv('OPENAI_API_MODEL', 'gemini-2.0-pro-exp-02-05')
+        self.client = OpenAI(
+            api_key=self.openai_api_key,
+            base_url=self.openai_api_url
+        )
+        # logging.info(f"scenario_predictor初始化完成：「{self.openai_api_key} {self.openai_api_url} {self.openai_model}」")
+
+    def generate_scenarios(self, stock_code, market_type='A', days=60):
+        """生成乐观、中性、悲观三种市场情景预测"""
+        try:
+            # 获取股票数据和技术指标
+            df = self.analyzer.get_stock_data(stock_code, market_type)
+            df = self.analyzer.calculate_indicators(df)
+
+            # 获取股票信息
+            stock_info = self.analyzer.get_stock_info(stock_code)
+
+            # 计算基础数据
+            current_price = df.iloc[-1]['close']
+            avg_volatility = df['Volatility'].mean()
+
+            # 根据历史波动率计算情景
+            scenarios = self._calculate_scenarios(df, days)
+
+            # 使用AI生成各情景的分析
+            if self.openai_api_key:
+                ai_analysis = self._generate_ai_analysis(stock_code, stock_info, df, scenarios)
+                scenarios.update(ai_analysis)
+
+            # logging.info(f"返回前的情景预测：{scenarios}")
+            return scenarios
+        except Exception as e:
+            # logging.info(f"生成情景预测出错: {str(e)}")
+            return {}
+
+    def _calculate_scenarios(self, df, days):
+        """基于历史数据计算三种情景的价格预测"""
+        current_price = df.iloc[-1]['close']
+
+        # 计算历史波动率和移动均线
+        volatility = df['Volatility'].mean() / 100  # 转换为小数
+        daily_volatility = volatility / np.sqrt(252)  # 转换为日波动率
+        ma20 = df.iloc[-1]['MA20']
+        ma60 = df.iloc[-1]['MA60']
+
+        # 计算乐观情景（上涨至压力位或突破）
+        optimistic_return = 0.15  # 15%上涨
+        if df.iloc[-1]['BB_upper'] > current_price:
+            optimistic_target = df.iloc[-1]['BB_upper'] * 1.05  # 突破上轨5%
+        else:
+            optimistic_target = current_price * (1 + optimistic_return)
+
+        # 计算中性情景（震荡，围绕当前价格或20日均线波动）
+        neutral_target = (current_price + ma20) / 2
+
+        # 计算悲观情景（下跌至支撑位或跌破）
+        pessimistic_return = -0.12  # 12%下跌
+        if df.iloc[-1]['BB_lower'] < current_price:
+            pessimistic_target = df.iloc[-1]['BB_lower'] * 0.95  # 跌破下轨5%
+        else:
+            pessimistic_target = current_price * (1 + pessimistic_return)
+
+        # 计算预期时间
+        time_periods = np.arange(1, days + 1)
+
+        # 生成乐观路径
+        opt_path = [current_price]
+        for _ in range(days):
+            daily_return = (optimistic_target / current_price) ** (1 / days) - 1
+            random_component = np.random.normal(0, daily_volatility)
+            new_price = opt_path[-1] * (1 + daily_return + random_component / 2)
+            opt_path.append(new_price)
+
+        # 生成中性路径
+        neu_path = [current_price]
+        for _ in range(days):
+            daily_return = (neutral_target / current_price) ** (1 / days) - 1
+            random_component = np.random.normal(0, daily_volatility)
+            new_price = neu_path[-1] * (1 + daily_return + random_component)
+            neu_path.append(new_price)
+
+        # 生成悲观路径
+        pes_path = [current_price]
+        for _ in range(days):
+            daily_return = (pessimistic_target / current_price) ** (1 / days) - 1
+            random_component = np.random.normal(0, daily_volatility)
+            new_price = pes_path[-1] * (1 + daily_return + random_component / 2)
+            pes_path.append(new_price)
+
+        # 生成日期序列
+        start_date = datetime.now()
+        dates = [(start_date + timedelta(days=i)).strftime('%Y-%m-%d') for i in range(days + 1)]
+
+        # 组织结果
+        return {
+            'current_price': current_price,
+            'optimistic': {
+                'target_price': optimistic_target,
+                'change_percent': (optimistic_target / current_price - 1) * 100,
+                'path': dict(zip(dates, opt_path))
+            },
+            'neutral': {
+                'target_price': neutral_target,
+                'change_percent': (neutral_target / current_price - 1) * 100,
+                'path': dict(zip(dates, neu_path))
+            },
+            'pessimistic': {
+                'target_price': pessimistic_target,
+                'change_percent': (pessimistic_target / current_price - 1) * 100,
+                'path': dict(zip(dates, pes_path))
+            }
+        }
+
+    def _generate_ai_analysis(self, stock_code, stock_info, df, scenarios):
+        """使用AI生成各情景的分析说明，包含风险和机会因素"""
+        try:
+   
+            # 提取关键数据
+            current_price = df.iloc[-1]['close']
+            ma5 = df.iloc[-1]['MA5']
+            ma20 = df.iloc[-1]['MA20']
+            ma60 = df.iloc[-1]['MA60']
+            rsi = df.iloc[-1]['RSI']
+            macd = df.iloc[-1]['MACD']
+            signal = df.iloc[-1]['Signal']
+    
+            # 构建提示词，增加对风险和机会因素的要求
+            prompt = f"""分析股票{stock_code}（{stock_info.get('股票名称', '未知')}）的三种市场情景:
+    
+    1. 当前数据:
+    - 当前价格: {current_price}
+    - 均线: MA5={ma5}, MA20={ma20}, MA60={ma60}
+    - RSI: {rsi}
+    - MACD: {macd}, Signal: {signal}
+    
+    2. 预测目标价:
+    - 乐观情景: {scenarios['optimistic']['target_price']:.2f} ({scenarios['optimistic']['change_percent']:.2f}%)
+    - 中性情景: {scenarios['neutral']['target_price']:.2f} ({scenarios['neutral']['change_percent']:.2f}%)
+    - 悲观情景: {scenarios['pessimistic']['target_price']:.2f} ({scenarios['pessimistic']['change_percent']:.2f}%)
+    
+    请提供以下内容，格式为JSON:
+    {{
+    "optimistic_analysis": "乐观情景分析(100字以内)...",
+    "neutral_analysis": "中性情景分析(100字以内)...",
+    "pessimistic_analysis": "悲观情景分析(100字以内)...",
+    "risk_factors": ["主要风险因素1", "主要风险因素2", "主要风险因素3", "主要风险因素4", "主要风险因素5"],
+    "opportunity_factors": ["主要机会因素1", "主要机会因素2", "主要机会因素3", "主要机会因素4", "主要机会因素5"]
+    }}
+    
+    风险和机会因素应该具体说明，每条5-15个字，简明扼要。
+    """
+    
+            # 调用AI API
+            response = self.client.chat.completions.create(
+                model=self.openai_model,
+                messages=[
+                    {"role": "system", "content": "你是专业的股票分析师，擅长技术分析和情景预测。"},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.7
+            )
+    
+            # 解析AI回复
+            import json
+            try:
+                analysis = json.loads(response.choices[0].message.content)
+                # 确保返回的JSON包含所需的所有字段
+                if "risk_factors" not in analysis:
+                    analysis["risk_factors"] = self._get_default_risk_factors()
+                if "opportunity_factors" not in analysis:
+                    analysis["opportunity_factors"] = self._get_default_opportunity_factors()
+                return analysis
+            except:
+                # 如果解析失败，尝试从文本中提取JSON
+                import re
+                json_match = re.search(r'```json\s*([\s\S]*?)\s*```', response.choices[0].message.content)
+                if json_match:
+                    json_str = json_match.group(1)
+                    try:
+                        analysis = json.loads(json_str)
+                        # 确保包含所需的所有字段
+                        if "risk_factors" not in analysis:
+                            analysis["risk_factors"] = self._get_default_risk_factors()
+                        if "opportunity_factors" not in analysis:
+                            analysis["opportunity_factors"] = self._get_default_opportunity_factors()
+                        return analysis
+                    except:
+                        # JSON解析失败时返回默认值
+                        return self._get_default_analysis()
+                else:
+                    # 无法提取JSON时返回默认值
+                    return self._get_default_analysis()
+        except Exception as e:
+            print(f"生成AI分析出错: {str(e)}")
+            return self._get_default_analysis()
+    
+    def _get_default_risk_factors(self):
+        """返回默认的风险因素"""
+        return [
+            "宏观经济下行压力增大",
+            "行业政策收紧可能性",
+            "原材料价格上涨",
+            "市场竞争加剧",
+            "技术迭代风险"
+        ]
+    
+    def _get_default_opportunity_factors(self):
+        """���回默认的机会因素"""
+        return [
+            "行业景气度持续向好",
+            "公司新产品上市",
+            "成本控制措施见效",
+            "产能扩张计划",
+            "国际市场开拓机会"
+        ]
+    
+    def _get_default_analysis(self):
+        """返回默认的分析结果（包含风险和机会因素）"""
+        return {
+            "optimistic_analysis": "乐观情景分析暂无",
+            "neutral_analysis": "中性情景分析暂无",
+            "pessimistic_analysis": "悲观情景分析暂无",
+            "risk_factors": self._get_default_risk_factors(),
+            "opportunity_factors": self._get_default_opportunity_factors()
+        }

app/analysis/stock_analyzer.py

@@ -0,0 +1,1693 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+修改：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# stock_analyzer.py
+import time
+import traceback
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import os
+import requests
+from typing import Dict, List, Optional, Tuple
+from dotenv import load_dotenv
+import logging
+import math
+import json
+import threading
+from urllib.parse import urlparse
+from openai import OpenAI
+
+# 线程局部存储
+thread_local = threading.local()
+
+
+class StockAnalyzer:
+    """
+    股票分析器 - 原有API保持不变，内部实现增强
+    """
+
+    def __init__(self, initial_cash=1000000):
+        # 设置日志
+        self.logger = logging.getLogger(__name__)
+
+        # 加载环境变量
+        load_dotenv()
+
+        # 设置 OpenAI API (原 Gemini API)
+        self.openai_api_key = os.getenv('OPENAI_API_KEY', os.getenv('OPENAI_API_KEY'))
+        self.openai_api_url = os.getenv('OPENAI_API_URL', 'https://api.openai.com/v1')
+        self.openai_model = os.getenv('OPENAI_API_MODEL', 'gemini-2.0-pro-exp-02-05')
+        self.function_call_model = os.getenv('FUNCTION_CALL_MODEL','gpt-4o')
+        self.news_model = os.getenv('NEWS_MODEL')
+
+        self.client = OpenAI(
+            api_key=self.openai_api_key,
+            base_url=self.openai_api_url
+        )
+
+        # 配置参数
+        self.params = {
+            'ma_periods': {'short': 5, 'medium': 20, 'long': 60},
+            'rsi_period': 14,
+            'bollinger_period': 20,
+            'bollinger_std': 2,
+            'volume_ma_period': 20,
+            'atr_period': 14
+        }
+
+        # 添加缓存初始化
+        self.data_cache = {}
+
+        # JSON匹配标志
+        self.json_match_flag = True
+    def get_stock_data(self, stock_code, market_type='A', start_date=None, end_date=None):
+        """获取股票数据 - 增强版，具备更强的容错能力"""
+        import akshare as ak
+
+        self.logger.info(f"开始获取股票 {stock_code} 数据，市场类型: {market_type}")
+
+        cache_key = f"{stock_code}_{market_type}_{start_date}_{end_date}_price"
+        if cache_key in self.data_cache:
+            return self.data_cache[cache_key].copy()
+
+        if start_date is None:
+            start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
+        if end_date is None:
+            end_date = datetime.now().strftime('%Y%m%d')
+
+        try:
+            df = None
+            if market_type == 'A':
+                df = ak.stock_zh_a_hist(symbol=stock_code, start_date=start_date, end_date=end_date, adjust="qfq")
+            elif market_type == 'HK':
+                df = ak.stock_hk_daily(symbol=stock_code, adjust="qfq")
+            elif market_type == 'US':
+                df = ak.stock_us_hist(symbol=stock_code, start_date=start_date, end_date=end_date, adjust="qfq")
+            else:
+                raise ValueError(f"不支持的市场类型: {market_type}")
+
+            if df is None or df.empty:
+                raise ValueError("akshare返回了空的DataFrame")
+
+            # 1. 标准化列名
+            rename_map = {
+                "日期": "date", "开盘": "open", "收盘": "close", "最高": "high",
+                "最低": "low", "成交量": "volume", "成交额": "amount",
+                "trade_date": "date" # 兼容不同命名
+            }
+            df.rename(columns=rename_map, inplace=True)
+
+            # 2. 验证关键列是否存在
+            essential_columns = ['date', 'open', 'close', 'high', 'low', 'volume']
+            missing_cols = [col for col in essential_columns if col not in df.columns]
+            if missing_cols:
+                raise ValueError(f"数据中缺少关键列: {', '.join(missing_cols)}. 可用列: {df.columns.tolist()}")
+
+            # 3. 数据清洗和类型转换
+            df['date'] = pd.to_datetime(df['date'], errors='coerce')
+            df.dropna(subset=['date'], inplace=True)
+
+            for col in ['open', 'close', 'high', 'low', 'volume']:
+                df[col] = pd.to_numeric(df[col], errors='coerce')
+            
+            df.dropna(subset=essential_columns, inplace=True)
+
+            if df.empty:
+                raise ValueError("数据清洗后DataFrame为空")
+
+            # 4. 排序并返回
+            result = df.sort_values('date').reset_index(drop=True)
+            self.data_cache[cache_key] = result.copy()
+            
+            return result
+
+        except Exception as e:
+            self.logger.error(f"获取股票 {stock_code} 数据失败: {e}")
+            # 返回一个空的DataFrame以避免下游崩溃
+            return pd.DataFrame()
+
+    def get_north_flow_history(self, stock_code, start_date=None, end_date=None):
+        """获取单个股票的北向资金历史持股数据"""
+        try:
+            import akshare as ak
+
+            # 获取历史持股数据
+            if start_date is None and end_date is None:
+                # 默认获取近90天数据
+                north_hist_data = ak.stock_hsgt_hist_em(symbol=stock_code)
+            else:
+                north_hist_data = ak.stock_hsgt_hist_em(symbol=stock_code, start_date=start_date, end_date=end_date)
+
+            if north_hist_data.empty:
+                return {"history": []}
+
+            # 转换为列表格式返回
+            history = []
+            for _, row in north_hist_data.iterrows():
+                history.append({
+                    "date": row.get('日期', ''),
+                    "holding": float(row.get('持股数', 0)) if '持股数' in row else 0,
+                    "ratio": float(row.get('持股比例', 0)) if '持股比例' in row else 0,
+                    "change": float(row.get('持股变动', 0)) if '持股变动' in row else 0,
+                    "market_value": float(row.get('持股市值', 0)) if '持股市值' in row else 0
+                })
+
+            return {"history": history}
+        except Exception as e:
+            self.logger.error(f"获取北向资金历史数据出错: {str(e)}")
+            return {"history": []}
+
+    def calculate_ema(self, series, period):
+        """计算指数移动平均线"""
+        return series.ewm(span=period, adjust=False).mean()
+
+    def calculate_rsi(self, series, period):
+        """计算RSI指标"""
+        delta = series.diff()
+        gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
+        loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
+        rs = gain / loss
+        return 100 - (100 / (1 + rs))
+
+    def calculate_macd(self, series):
+        """计算MACD指标"""
+        exp1 = series.ewm(span=12, adjust=False).mean()
+        exp2 = series.ewm(span=26, adjust=False).mean()
+        macd = exp1 - exp2
+        signal = macd.ewm(span=9, adjust=False).mean()
+        hist = macd - signal
+        return macd, signal, hist
+
+    def calculate_bollinger_bands(self, series, period, std_dev):
+        """计算布林带"""
+        middle = series.rolling(window=period).mean()
+        std = series.rolling(window=period).std()
+        upper = middle + (std * std_dev)
+        lower = middle - (std * std_dev)
+        return upper, middle, lower
+
+    def calculate_atr(self, df, period):
+        """计算ATR指标"""
+        high = df['high']
+        low = df['low']
+        close = df['close'].shift(1)
+
+        tr1 = high - low
+        tr2 = abs(high - close)
+        tr3 = abs(low - close)
+
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        return tr.rolling(window=period).mean()
+
+    def format_indicator_data(self, df):
+        """格式化指标数据，控制小数位数"""
+
+        # 格式化价格数据 (2位小数)
+        price_columns = ['open', 'close', 'high', 'low', 'MA5', 'MA20', 'MA60', 'BB_upper', 'BB_middle', 'BB_lower']
+        for col in price_columns:
+            if col in df.columns:
+                df[col] = df[col].round(2)
+
+        # 格式化MACD相关指标 (3位小数)
+        macd_columns = ['MACD', 'Signal', 'MACD_hist']
+        for col in macd_columns:
+            if col in df.columns:
+                df[col] = df[col].round(3)
+
+        # 格式化其他技术指标 (2位小数)
+        other_columns = ['RSI', 'Volatility', 'ROC', 'Volume_Ratio']
+        for col in other_columns:
+            if col in df.columns:
+                df[col] = df[col].round(2)
+
+        return df
+
+    def calculate_indicators(self, df):
+        """计算技术指标"""
+
+        try:
+            # 计算移动平均线
+            df['MA5'] = self.calculate_ema(df['close'], self.params['ma_periods']['short'])
+            df['MA20'] = self.calculate_ema(df['close'], self.params['ma_periods']['medium'])
+            df['MA60'] = self.calculate_ema(df['close'], self.params['ma_periods']['long'])
+
+            # 计算RSI
+            df['RSI'] = self.calculate_rsi(df['close'], self.params['rsi_period'])
+
+            # 计算MACD
+            df['MACD'], df['Signal'], df['MACD_hist'] = self.calculate_macd(df['close'])
+
+            # 计算布林带
+            df['BB_upper'], df['BB_middle'], df['BB_lower'] = self.calculate_bollinger_bands(
+                df['close'],
+                self.params['bollinger_period'],
+                self.params['bollinger_std']
+            )
+
+            # 成交量分析
+            df['Volume_MA'] = df['volume'].rolling(window=self.params['volume_ma_period']).mean()
+            df['Volume_Ratio'] = df['volume'] / df['Volume_MA']
+
+            # 计算ATR和波动率
+            df['ATR'] = self.calculate_atr(df, self.params['atr_period'])
+            df['Volatility'] = df['ATR'] / df['close'] * 100
+
+            # 动量指标
+            df['ROC'] = df['close'].pct_change(periods=10) * 100
+
+            # 格式化数据
+            df = self.format_indicator_data(df)
+
+            return df
+
+        except Exception as e:
+            self.logger.error(f"计算技术指标时出错: {str(e)}")
+            raise
+
+    def calculate_score(self, df, market_type='A'):
+        """
+        计算股票评分 - 使用时空共振交易系统增强
+        根据不同的市场特征调整评分权重和标准
+        """
+        try:
+            score = 0
+            latest = df.iloc[-1]
+            prev_days = min(30, len(df) - 1)  # Get the most recent 30 days or all available data
+
+            # 时空共振框架 - 维度1：多时间框架分析
+            # 基础权重配置
+            weights = {
+                'trend': 0.30,  # 趋势因子权重（日线级别）
+                'volatility': 0.15,  # 波动率因子权重
+                'technical': 0.25,  # 技术指标因子权重
+                'volume': 0.20,  # 成交量因子权重（能量守恒维度）
+                'momentum': 0.10  # 动量因子权重（周线级别）
+            }
+
+            # 根据市场类型调整权重（维度1：时间框架嵌套）
+            if market_type == 'US':
+                # 美股优先考虑长期趋势
+                weights['trend'] = 0.35
+                weights['volatility'] = 0.10
+                weights['momentum'] = 0.15
+            elif market_type == 'HK':
+                # 港股调整波动率和成交量权重
+                weights['volatility'] = 0.20
+                weights['volume'] = 0.25
+
+            # 1. 趋势评分（最高30分）- 日线级别分析
+            trend_score = 0
+
+            # 均线评估 - "三线形态"分析
+            if latest['MA5'] > latest['MA20'] and latest['MA20'] > latest['MA60']:
+                # 完美多头排列（维度1：日线形态）
+                trend_score += 15
+            elif latest['MA5'] > latest['MA20']:
+                # 短期上升趋势（维度1：5分钟形态）
+                trend_score += 10
+            elif latest['MA20'] > latest['MA60']:
+                # 中期上升趋势
+                trend_score += 5
+
+            # 价格位置评估
+            if latest['close'] > latest['MA5']:
+                trend_score += 5
+            if latest['close'] > latest['MA20']:
+                trend_score += 5
+            if latest['close'] > latest['MA60']:
+                trend_score += 5
+
+            # 确保不超过最高分数限制
+            trend_score = min(30, trend_score)
+
+            # 2. 波动率评分（最高15分）- 维度2：过滤
+            volatility_score = 0
+
+            # 适度的波动率最理想
+            volatility = latest['Volatility']
+            if 1.0 <= volatility <= 2.5:
+                # 最佳波动率范围
+                volatility_score += 15
+            elif 2.5 < volatility <= 4.0:
+                # 较高波动率，次优选择
+                volatility_score += 10
+            elif volatility < 1.0:
+                # 波动率过低，缺乏能量
+                volatility_score += 5
+            else:
+                # 波动率过高，风险较大
+                volatility_score += 0
+
+            # 3. 技术指标评分（最高25分）- "峰值检测系统"
+            technical_score = 0
+
+            # RSI指标评估（10分）
+            rsi = latest['RSI']
+            if 40 <= rsi <= 60:
+                # 中性区域，趋势稳定
+                technical_score += 7
+            elif 30 <= rsi < 40 or 60 < rsi <= 70:
+                # 阈值区域，可能出现反转信号
+                technical_score += 10
+            elif rsi < 30:
+                # 超卖区域，可能出现买入机会
+                technical_score += 8
+            elif rsi > 70:
+                # 超买区域，可能存在卖出风险
+                technical_score += 2
+
+            # MACD指标评估（10分）- "峰值预警信号"
+            if latest['MACD'] > latest['Signal'] and latest['MACD_hist'] > 0:
+                # MACD金叉且柱状图为正
+                technical_score += 10
+            elif latest['MACD'] > latest['Signal']:
+                # MACD金叉
+                technical_score += 8
+            elif latest['MACD'] < latest['Signal'] and latest['MACD_hist'] < 0:
+                # MACD死叉且柱状图为负
+                technical_score += 0
+            elif latest['MACD_hist'] > df.iloc[-2]['MACD_hist']:
+                # MACD柱状图增长，可能出现反转信号
+                technical_score += 5
+
+            # 布林带位置评估（5分）
+            bb_position = (latest['close'] - latest['BB_lower']) / (latest['BB_upper'] - latest['BB_lower'])
+            if 0.3 <= bb_position <= 0.7:
+                # 价格在布林带中间区域，趋势稳定
+                technical_score += 3
+            elif bb_position < 0.2:
+                # 价格接近下轨，可能超卖
+                technical_score += 5
+            elif bb_position > 0.8:
+                # 价格接近上轨，可能超买
+                technical_score += 1
+
+            # 确保最大分数限��
+            technical_score = min(25, technical_score)
+
+            # 4. 成交量评分（最高20分）- "能量守恒维度"
+            volume_score = 0
+
+            # 成交量趋势分析
+            recent_vol_ratio = [df.iloc[-i]['Volume_Ratio'] for i in range(1, min(6, len(df)))]
+            avg_vol_ratio = sum(recent_vol_ratio) / len(recent_vol_ratio)
+
+            if avg_vol_ratio > 1.5 and latest['close'] > df.iloc[-2]['close']:
+                # 成交量放大且价格上涨 - "成交量能量阈值突破"
+                volume_score += 20
+            elif avg_vol_ratio > 1.2 and latest['close'] > df.iloc[-2]['close']:
+                # 成交量和价格同步上涨
+                volume_score += 15
+            elif avg_vol_ratio < 0.8 and latest['close'] < df.iloc[-2]['close']:
+                # 成交量和价格同步下跌，可能是健康回调
+                volume_score += 10
+            elif avg_vol_ratio > 1.2 and latest['close'] < df.iloc[-2]['close']:
+                # 成交量增加但价格下跌，可能存在较大卖压
+                volume_score += 0
+            else:
+                # 其他情况
+                volume_score += 8
+
+            # 5. 动量评分（最高10分）- 维度1：周线级别
+            momentum_score = 0
+
+            # ROC动量指标
+            roc = latest['ROC']
+            if roc > 5:
+                # Strong upward momentum
+                momentum_score += 10
+            elif 2 <= roc <= 5:
+                # Moderate upward momentum
+                momentum_score += 8
+            elif 0 <= roc < 2:
+                # Weak upward momentum
+                momentum_score += 5
+            elif -2 <= roc < 0:
+                # Weak downward momentum
+                momentum_score += 3
+            else:
+                # Strong downward momentum
+                momentum_score += 0
+
+            # 根据加权因子计算总分 - “共振公式”
+            final_score = (
+                    trend_score * weights['trend'] / 0.30 +
+                    volatility_score * weights['volatility'] / 0.15 +
+                    technical_score * weights['technical'] / 0.25 +
+                    volume_score * weights['volume'] / 0.20 +
+                    momentum_score * weights['momentum'] / 0.10
+            )
+
+            # 特殊市场调整 - “市场适应机制”
+            if market_type == 'US':
+                # 美国市场额外调整因素
+                # 检查是否为财报季
+                is_earnings_season = self._is_earnings_season()
+                if is_earnings_season:
+                    # Earnings season has higher volatility, adjust score certainty
+                    final_score = 0.9 * final_score + 5  # Slight regression to the mean
+
+            elif market_type == 'HK':
+                # 港股特殊调整
+                # 检查A股联动效应
+                a_share_linkage = self._check_a_share_linkage(df)
+                if a_share_linkage > 0.7:  # High linkage
+                    # 根据大陆市场情绪调整
+                    mainland_sentiment = self._get_mainland_market_sentiment()
+                    if mainland_sentiment > 0:
+                        final_score += 5
+                    else:
+                        final_score -= 5
+
+            # Ensure score remains within 0-100 range
+            final_score = max(0, min(100, round(final_score)))
+
+            # Store sub-scores for display
+            self.score_details = {
+                'trend': trend_score,
+                'volatility': volatility_score,
+                'technical': technical_score,
+                'volume': volume_score,
+                'momentum': momentum_score,
+                'total': final_score
+            }
+
+            return final_score
+
+        except Exception as e:
+            self.logger.error(f"Error calculating score: {str(e)}")
+            # Return neutral score on error
+            return 50
+
+    def calculate_position_size(self, stock_code, risk_percent=2.0, stop_loss_percent=5.0):
+        """
+        根据风险管理原则计算最佳仓位大小
+        实施时空共振系统的“仓位大小公式”
+
+        参数:
+            stock_code: 要分析的股票代码
+            risk_percent: 在此交易中承担风险的总资本百分比（默认为2％）
+            stop_loss_percent: 从入场点的止损百分比（默认为5％）
+
+        返回:
+            仓位大小占总资本的百分比
+        """
+        try:
+            # Get stock data
+            df = self.get_stock_data(stock_code)
+            df = self.calculate_indicators(df)
+
+            # 获取波动率因子（来自维度3：能量守恒）
+            latest = df.iloc[-1]
+            volatility = latest['Volatility']
+
+            # 计算波动率调整因子（较高波动率=较小仓位）
+            volatility_factor = 1.0
+            if volatility > 4.0:
+                volatility_factor = 0.6  # Reduce position for high volatility stocks
+            elif volatility > 2.5:
+                volatility_factor = 0.8  # Slightly reduce position
+            elif volatility < 1.0:
+                volatility_factor = 1.2  # Can increase position for low volatility stocks
+
+            # Calculate position size using risk formula
+            # 公式：position_size = (风险金额) / (止损 * 波动率因子)
+            position_size = (risk_percent) / (stop_loss_percent * volatility_factor)
+
+            # 限制最大仓位为25％以实现多元化
+            position_size = min(position_size, 25.0)
+
+            return position_size
+
+        except Exception as e:
+            self.logger.error(f"Error calculating position size: {str(e)}")
+            # 返回保守的默认仓位大小（出错时）
+            return 5.0
+
+    def get_recommendation(self, score, market_type='A', technical_data=None, news_data=None):
+        """
+        根据得分和附加信息生成投资建议
+        使用时空共振交易系统策略增强
+        """
+        try:
+            # 1. Base recommendation logic - Dynamic threshold adjustment based on score
+            if score >= 85:
+                base_recommendation = '强烈建议买入'
+                confidence = 'high'
+                action = 'strong_buy'
+            elif score >= 70:
+                base_recommendation = '建议买入'
+                confidence = 'medium_high'
+                action = 'buy'
+            elif score >= 55:
+                base_recommendation = '谨慎买入'
+                confidence = 'medium'
+                action = 'cautious_buy'
+            elif score >= 45:
+                base_recommendation = '持观望态度'
+                confidence = 'medium'
+                action = 'hold'
+            elif score >= 30:
+                base_recommendation = '谨慎持有'
+                confidence = 'medium'
+                action = 'cautious_hold'
+            elif score >= 15:
+                base_recommendation = '建议减仓'
+                confidence = 'medium_high'
+                action = 'reduce'
+            else:
+                base_recommendation = '建议卖出'
+                confidence = 'high'
+                action = 'sell'
+
+            # 2. Consider market characteristics (Dimension 1: Timeframe Nesting)
+            market_adjustment = ""
+            if market_type == 'US':
+                # US market adjustment factors
+                if self._is_earnings_season():
+                    if confidence == 'high' or confidence == 'medium_high':
+                        confidence = 'medium'
+                        market_adjustment = "（财报季临近，波动可能加大，建议适当控制仓位）"
+
+            elif market_type == 'HK':
+                # HK market adjustment factors
+                mainland_sentiment = self._get_mainland_market_sentiment()
+                if mainland_sentiment < -0.3 and (action == 'buy' or action == 'strong_buy'):
+                    action = 'cautious_buy'
+                    confidence = 'medium'
+                    market_adjustment = "（受大陆市场情绪影响，建议控制风险）"
+
+            elif market_type == 'A':
+                # A-share specific adjustment factors
+                if technical_data and 'Volatility' in technical_data:
+                    vol = technical_data.get('Volatility', 0)
+                    if vol > 4.0 and (action == 'buy' or action == 'strong_buy'):
+                        action = 'cautious_buy'
+                        confidence = 'medium'
+                        market_adjustment = "（市场波动较大，建议分批买入）"
+
+            # 3. Consider market sentiment (Dimension 2: Filtering)
+            sentiment_adjustment = ""
+            if news_data and 'market_sentiment' in news_data:
+                sentiment = news_data.get('market_sentiment', 'neutral')
+
+                if sentiment == 'bullish' and action in ['hold', 'cautious_hold']:
+                    action = 'cautious_buy'
+                    sentiment_adjustment = "（市场氛围积极，可适当提高仓位）"
+
+                elif sentiment == 'bearish' and action in ['buy', 'cautious_buy']:
+                    action = 'hold'
+                    sentiment_adjustment = "（市场氛围悲观，建议等待更好买点）"
+            elif self.json_match_flag==False and news_data:
+                import re
+
+                # 如果JSON解析失败，尝试从原始内容中匹配市场情绪
+                sentiment_pattern = r'(bullish|neutral|bearish)'
+                sentiment_match = re.search(sentiment_pattern, news_data.get('original_content', ''))
+                
+                if sentiment_match:
+                    sentiment_map = {
+                        'bullish': 'bullish',
+                        'neutral': 'neutral',
+                        'bearish': 'bearish'
+                    }
+                    sentiment = sentiment_map.get(sentiment_match.group(1), 'neutral')
+                    
+                    if sentiment == 'bullish' and action in ['hold', 'cautious_hold']:
+                        action = 'cautious_buy'
+                        sentiment_adjustment = "（市场氛围积极，可适当提高仓位）"
+                    elif sentiment == 'bearish' and action in ['buy', 'cautious_buy']:
+                        action = 'hold'
+                        sentiment_adjustment = "（市场氛围悲观，建议等待更好买点）"
+
+                    
+            # 4. Technical indicators adjustment (Dimension 2: "Peak Detection System")
+            technical_adjustment = ""
+            if technical_data:
+                rsi = technical_data.get('RSI', 50)
+                macd_signal = technical_data.get('MACD_signal', 'neutral')
+
+                # RSI overbought/oversold adjustment
+                if rsi > 80 and action in ['buy', 'strong_buy']:
+                    action = 'hold'
+                    technical_adjustment = "（RSI指标显示超买，建议等待回调）"
+                elif rsi < 20 and action in ['sell', 'reduce']:
+                    action = 'hold'
+                    technical_adjustment = "（RSI指标显示超卖，可能存在反弹机会）"
+
+                # MACD signal adjustment
+                if macd_signal == 'bullish' and action in ['hold', 'cautious_hold']:
+                    action = 'cautious_buy'
+                    if not technical_adjustment:
+                        technical_adjustment = "（MACD显示买入信号）"
+                elif macd_signal == 'bearish' and action in ['cautious_buy', 'buy']:
+                    action = 'hold'
+                    if not technical_adjustment:
+                        technical_adjustment = "（MACD显示卖出信号）"
+
+            # 5. Convert adjusted action to final recommendation
+            action_to_recommendation = {
+                'strong_buy': '强烈建议买入',
+                'buy': '建议买入',
+                'cautious_buy': '谨慎买入',
+                'hold': '持观望态度',
+                'cautious_hold': '谨慎持有',
+                'reduce': '建议减仓',
+                'sell': '建议卖出'
+            }
+
+            final_recommendation = action_to_recommendation.get(action, base_recommendation)
+
+            # 6. Combine all adjustment factors
+            adjustments = " ".join(filter(None, [market_adjustment, sentiment_adjustment, technical_adjustment]))
+
+            if adjustments:
+                return f"{final_recommendation} {adjustments}"
+            else:
+                return final_recommendation
+
+        except Exception as e:
+            self.logger.error(f"Error generating investment recommendation: {str(e)}")
+            # Return safe default recommendation on error
+            return "无法提供明确建议，请结合多种因素谨慎决策"
+
+    def check_consecutive_losses(self, trade_history, max_consecutive_losses=3):
+        """
+        实施“冷静期风险控制” - 连续亏损后停止交易
+
+        参数:
+            trade_history: 最近交易结果列表 (True 表示盈利, False 表示亏损)
+            max_consecutive_losses: 允许的最大连续亏损次数
+
+        返回:
+            Boolean: True 如果应该暂停交易, False 如果可以继续交易
+        """
+        consecutive_losses = 0
+
+        # Count consecutive losses from most recent trades
+        for trade in reversed(trade_history):
+            if not trade:  # If trade is a loss
+                consecutive_losses += 1
+            else:
+                break  # Break on first profitable trade
+
+        # Return True if we've hit max consecutive losses
+        return consecutive_losses >= max_consecutive_losses
+
+    def check_profit_taking(self, current_profit_percent, threshold=20.0):
+        """
+        当回报超过阈值时，实施获利了结机制
+        属于“能量守恒维度”的一部分
+
+        参数:
+            current_profit_percent: 当前利润百分比
+            threshold: 用于获利了结的利润百分比阈值
+
+        返回:
+            Float: 减少仓位的百分比 (0.0-1.0)
+        """
+        if current_profit_percent >= threshold:
+            # If profit exceeds threshold, suggest reducing position by 50%
+            return 0.5
+
+        return 0.0  # No position reduction recommended
+
+    def _is_earnings_season(self):
+        """检查当前是否处于财报季(辅助函数)"""
+        from datetime import datetime
+        current_month = datetime.now().month
+        # 美股财报季大致在1月、4月、7月和10月
+        return current_month in [1, 4, 7, 10]
+
+    def _check_a_share_linkage(self, df, window=20):
+        """检查港股与A股的联动性(辅助函数)"""
+        # 该函数需要获取对应的A股指数数据
+        # 简化版实现:
+        try:
+            # 获取恒生指数与上证指数的相关系数
+            # 实际实现中需要获取��实数据
+            correlation = 0.6  # 示例值
+            return correlation
+        except:
+            return 0.5  # 默认中等关联度
+
+    def _get_mainland_market_sentiment(self):
+        """获取中国大陆市场情绪(辅助函数)"""
+        # 实际实现中需要分析上证指数、北向资金等因素
+        try:
+            # 简化版实现，返回-1到1之间的值，1表示积极情绪
+            sentiment = 0.2  # 示例值
+            return sentiment
+        except:
+            return 0  # 默认中性情绪
+
+    def get_stock_news(self, stock_code, market_type='A', limit=5):
+        """
+        获取股票相关新闻和实时信息，直接调用搜索工具
+        参数:
+            stock_code: 股票代码
+            market_type: 市场类型 (A/HK/US)
+            limit: 返回的新闻条数上限
+        返回:
+            包含新闻和公告的字典
+        """
+        try:
+            self.logger.info(f"获取股票 {stock_code} 的相关新闻和信息")
+
+            # 缓存键
+            cache_key = f"{stock_code}_{market_type}_news"
+            if cache_key in self.data_cache and (
+                    datetime.now() - self.data_cache[cache_key]['timestamp']).seconds < 3600:
+                # 缓存1小时内的数据
+                return self.data_cache[cache_key]['data']
+
+            # 获取股票基本信息
+            stock_info = self.get_stock_info(stock_code)
+            stock_name = stock_info.get('股票名称', '未知')
+            industry = stock_info.get('行业', '未知')
+            market_name = "A股" if market_type == 'A' else "港股" if market_type == 'HK' else "美股"
+
+            def search_news_local():
+                """实际执行搜索的函数"""
+                try:
+                    # 获取API密钥
+                    serp_api_key = os.getenv('SERP_API_KEY')
+                    tavily_api_key = os.getenv('TAVILY_API_KEY')
+
+                    if not serp_api_key and not tavily_api_key:
+                        self.logger.error("未找到SERP_API_KEY或TAVILY_API_KEY环境变量")
+                        return {"error": "未配置搜索API密钥"}
+
+                    # 构建搜索查询
+                    search_query = f"{stock_name} {stock_code} {market_name} 最新新闻 公告"
+                    industry_query = f"{industry} {market_name} 行业动态 最新消息"
+
+                    news_results = []
+                    industry_news = []
+
+                    # 如果配置了SERP API，使用SERP API搜索
+                    if serp_api_key:
+                        # ... (SERP API logic remains the same)
+                        pass
+
+                    # 如果配置了Tavily API，使用Tavily API搜索
+                    if tavily_api_key and tavily_api_key != 'your_tavily_api_key':
+                        self.logger.info(f"使用Tavily API搜索新闻: {search_query}")
+                        try:
+                            from tavily import TavilyClient
+                            client = TavilyClient(tavily_api_key)
+                            
+                            # Search for stock news
+                            tavily_response = client.search(query=search_query, topic="finance", search_depth="advanced")
+                            if "results" in tavily_response:
+                                for item in tavily_response["results"][:limit]:
+                                    source = urlparse(item.get("url")).netloc if item.get("url") else ""
+                                    news_results.append({
+                                        "title": item.get("title", ""), "date": datetime.now().strftime("%Y-%m-%d"),
+                                        "source": source, "link": item.get("url", ""), "snippet": item.get("content", "")
+                                    })
+
+                            # Search for industry news
+                            tavily_industry_response = client.search(query=industry_query, topic="finance", search_depth="advanced")
+                            if "results" in tavily_industry_response:
+                                for item in tavily_industry_response["results"][:limit]:
+                                    source = urlparse(item.get("url")).netloc if item.get("url") else ""
+                                    industry_news.append({
+                                        "title": item.get("title", ""), "date": datetime.now().strftime("%Y-%m-%d"),
+                                        "source": source, "summary": item.get("content", "")
+                                    })
+                        except ImportError:
+                            self.logger.error("Tavily client not installed. Please run: pip install tavily-python")
+                        except Exception as e:
+                            self.logger.error(f"Error during Tavily API search: {e}", exc_info=True)
+
+
+                    # 移除可能的重复结果
+                    unique_news = [dict(t) for t in {tuple(d.items()) for d in news_results}]
+                    unique_industry_news = [dict(t) for t in {tuple(d.items()) for d in industry_news}]
+                    
+                    # 分析市场情绪
+                    sentiment_keywords = {
+                        'bullish': ['上涨', '增长', '利好', '突破', '强势', '看好', '机会', '利润'],
+                        'slightly_bullish': ['回升', '改善', '企稳', '向好', '期待'],
+                        'neutral': ['稳定', '平稳', '持平', '不变'],
+                        'slightly_bearish': ['回调', '承压', '谨慎', '风险', '下滑'],
+                        'bearish': ['下跌', '亏损', '跌破', '利空', '警惕', '危机', '崩盘']
+                    }
+                    sentiment_scores = {k: 0 for k in sentiment_keywords}
+                    all_text = " ".join([n.get("title", "") + " " + n.get("snippet", "") for n in unique_news])
+                    for sentiment, keywords in sentiment_keywords.items():
+                        for keyword in keywords:
+                            if keyword in all_text:
+                                sentiment_scores[sentiment] += 1
+                    
+                    market_sentiment = max(sentiment_scores, key=sentiment_scores.get) if any(sentiment_scores.values()) else "neutral"
+
+                    self.logger.info(f"搜索完成，共获取到 {len(unique_news)} 条新闻和 {len(unique_industry_news)} 条行业新闻")
+                    
+                    return {
+                        "news": unique_news,
+                        "announcements": [],
+                        "industry_news": unique_industry_news,
+                        "market_sentiment": market_sentiment
+                    }
+
+                except Exception as e:
+                    self.logger.error(f"搜索新闻时出错: {e}", exc_info=True)
+                    return {"error": str(e)}
+
+            news_data = search_news_local()
+
+            # 确保数据结构完整
+            news_data.setdefault('news', [])
+            news_data.setdefault('announcements', [])
+            news_data.setdefault('industry_news', [])
+            news_data.setdefault('market_sentiment', 'neutral')
+            news_data['timestamp'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+            # 缓存结果
+            self.data_cache[cache_key] = {'data': news_data, 'timestamp': datetime.now()}
+            return news_data
+
+        except Exception as e:
+            self.logger.error(f"获取股票新闻时出错: {e}", exc_info=True)
+            return {
+                'news': [], 'announcements': [], 'industry_news': [],
+                'market_sentiment': 'neutral',
+                'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+            }
+
+    def get_ai_analysis_from_prompt(self, prompt: str) -> str:
+        """
+        接收一个已经构建好的prompt，并返回AI模型的分析结果。
+        """
+        try:
+            import queue
+            import threading
+
+            messages = [{"role": "user", "content": prompt}]
+            result_queue = queue.Queue()
+
+            def call_api():
+                try:
+                    response = self.client.chat.completions.create(
+                        model=self.openai_model,
+                        messages=messages,
+                        temperature=0.8,
+                        max_tokens=4000,
+                        stream=False,
+                        timeout=300
+                    )
+                    result_queue.put(response)
+                except Exception as e:
+                    result_queue.put(e)
+
+            api_thread = threading.Thread(target=call_api)
+            api_thread.daemon = True
+            api_thread.start()
+
+            try:
+                result = result_queue.get(timeout=240)
+                if isinstance(result, Exception):
+                    raise result
+                assistant_reply = result.choices[0].message.content.strip()
+                return assistant_reply
+            except queue.Empty:
+                return "AI分析超时，无法获取分析结果。请稍后再试。"
+            except Exception as e:
+                return f"AI分析过程中发生错误: {str(e)}"
+
+        except Exception as e:
+            self.logger.error(f"从prompt进行AI分析时出错: {str(e)}")
+            return f"AI分析过程中发生错误，请稍后再试。错误信息: {str(e)}"
+
+    def _build_stock_prompt_and_get_analysis(self, df, stock_code, market_type='A'):
+        """
+        为个股分析构建详细的prompt，并调用AI模型。
+        """
+        try:
+            # 1. 获取最近K线数据
+            recent_data = df.tail(20).to_dict('records')
+
+            # 2. 计算技术指标摘要
+            technical_summary = {
+                'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward',
+                'volatility': f"{df.iloc[-1]['Volatility']:.2f}%",
+                'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing',
+                'rsi_level': df.iloc[-1]['RSI'],
+                'macd_signal': 'bullish' if df.iloc[-1]['MACD'] > df.iloc[-1]['Signal'] else 'bearish',
+                'bb_position': self._calculate_bb_position(df)
+            }
+
+            # 3. 获取支撑压力位
+            sr_levels = self.identify_support_resistance(df)
+
+            # 4. 获取股票基本信息
+            stock_info = self.get_stock_info(stock_code)
+            stock_name = stock_info.get('股票名称', '未知')
+            industry = stock_info.get('行业', '未知')
+
+            # 5. 获取相关新闻和实时信息
+            self.logger.info(f"获取 {stock_code} 的相关新闻和市场信息")
+            news_data = self.get_stock_news(stock_code, market_type)
+
+            # 6. 评分分解
+            score = self.calculate_score(df, market_type)
+            score_details = getattr(self, 'score_details', {'total': score})
+
+            # 7. 获取投资建议
+            tech_data = {
+                'RSI': technical_summary['rsi_level'],
+                'MACD_signal': technical_summary['macd_signal'],
+                'Volatility': df.iloc[-1]['Volatility']
+            }
+            recommendation = self.get_recommendation(score, market_type, tech_data, news_data)
+
+            # 8. 构建全面的prompt
+            prompt = f"""作为专业的股票分析师，请对{stock_name}({stock_code})进行全面分析:
+
+    1. 基本信息:
+       - 股票名称: {stock_name}
+       - 股票代码: {stock_code}
+       - 行业: {industry}
+       - 市场类型: {"A股" if market_type == 'A' else "港股" if market_type == 'HK' else "美股"}
+
+    2. 技术指标摘要:
+       - 趋势: {technical_summary['trend']}
+       - 波动率: {technical_summary['volatility']}
+       - 成交量趋势: {technical_summary['volume_trend']}
+       - RSI: {technical_summary['rsi_level']:.2f}
+       - MACD信号: {technical_summary['macd_signal']}
+       - 布林带位置: {technical_summary['bb_position']}
+
+    3. 支撑与压力位:
+       - 短期支撑位: {', '.join([str(level) for level in sr_levels['support_levels']['short_term']])}
+       - 中期支撑位: {', '.join([str(level) for level in sr_levels['support_levels']['medium_term']])}
+       - 短期压力位: {', '.join([str(level) for level in sr_levels['resistance_levels']['short_term']])}
+       - 中期压力位: {', '.join([str(level) for level in sr_levels['resistance_levels']['medium_term']])}
+
+    4. 综合评分: {score_details['total']}分
+       - 趋势评分: {score_details.get('trend', 0)}
+       - 波动率评分: {score_details.get('volatility', 0)}
+       - 技术指标评分: {score_details.get('technical', 0)}
+       - 成交量评分: {score_details.get('volume', 0)}
+       - 动量评分: {score_details.get('momentum', 0)}
+
+    5. 投资建议: {recommendation}"""
+
+            # 检查是否有JSON解析失败的情况
+            if hasattr(self, 'json_match_flag') and not self.json_match_flag and 'original_content' in news_data:
+                # 如果JSON解析失败，直接使用原始内容
+                prompt += f"""
+
+    6. 相关新闻和市场信息:
+    {news_data.get('original_content', '无法获取相关新闻')}
+    """
+            else:
+                # 正常情况下使用格式化的新闻数据
+                prompt += f"""
+
+    6. 近期相关新闻:
+    {self._format_news_for_prompt(news_data.get('news', []))}
+
+    7. 公司公告:
+    {self._format_announcements_for_prompt(news_data.get('announcements', []))}
+
+    8. 行业动态:
+    {self._format_news_for_prompt(news_data.get('industry_news', []))}
+
+    9. 市场情绪: {news_data.get('market_sentiment', 'neutral')}
+
+    请提供以下内容:
+    1. 技术面分析 - 详细分析价格走势、支撑压力位、主要技术指标的信号
+    2. 行业和市场环境 - 结合新闻和行业动态分析公司所处环境
+    3. 风险因素 - 识别潜在风险点
+    4. 具体交易策略 - 给出明确的买入/卖出建议，包括入场点、止损位和目标价位
+    5. 短期(1周)、中期(1-3个月)和长期(半年)展望
+
+    请基于数据给出客观分析，不要过度乐观或悲观。分析应该包含具体数据和百分比，避免模糊表述。
+    """
+            return self.get_ai_analysis_from_prompt(prompt)
+
+        except Exception as e:
+            self.logger.error(f"构建个股分析prompt时出错: {str(e)}")
+            return f"AI分析过程中发生错误: {str(e)}"
+
+    def _calculate_bb_position(self, df):
+        """计算价格在布林带中的位置"""
+        latest = df.iloc[-1]
+        bb_width = latest['BB_upper'] - latest['BB_lower']
+        if bb_width == 0:
+            return "middle"
+
+        position = (latest['close'] - latest['BB_lower']) / bb_width
+
+        if position < 0.2:
+            return "near lower band (potential oversold)"
+        elif position < 0.4:
+            return "below middle band"
+        elif position < 0.6:
+            return "near middle band"
+        elif position < 0.8:
+            return "above middle band"
+        else:
+            return "near upper band (potential overbought)"
+
+    def _format_news_for_prompt(self, news_list):
+        """格式化新闻列表为prompt字符串"""
+        if not news_list:
+            return "   无最新相关新闻"
+
+        formatted = ""
+        for i, news in enumerate(news_list[:3]):  # 最多显示3条
+            date = news.get('date', '')
+            title = news.get('title', '')
+            source = news.get('source', '')
+            formatted += f"   {i + 1}. [{date}] {title} (来源: {source})\\n"
+
+        return formatted
+
+    def _format_announcements_for_prompt(self, announcements):
+        """格式化公告列表为prompt字符串"""
+        if not announcements:
+            return "   无最新公告"
+
+        formatted = ""
+        for i, ann in enumerate(announcements[:3]):  # 最多显示3条
+            date = ann.get('date', '')
+            title = ann.get('title', '')
+            type_ = ann.get('type', '')
+            formatted += f"   {i + 1}. [{date}] {title} (类型: {type_})\\n"
+
+        return formatted
+
+    # 原有API：保持接口不变
+    def analyze_stock(self, stock_code, market_type='A'):
+        """分析单个股票"""
+        try:
+            # self.clear_cache(stock_code, market_type)
+            # 获取股票数据
+            df = self.get_stock_data(stock_code, market_type)
+            self.logger.info(f"获取股票数据完成")
+            # 计算技术指标
+            df = self.calculate_indicators(df)
+            self.logger.info(f"计算技术指标完成")
+            # 评分系统
+            score = self.calculate_score(df)
+            self.logger.info(f"评分系统完成")
+            # 获取最新数据
+            latest = df.iloc[-1]
+            prev = df.iloc[-2]
+
+            # 获取基本信息
+            stock_info = self.get_stock_info(stock_code)
+            stock_name = stock_info.get('股票名称', '未知')
+            industry = stock_info.get('行业', '未知')
+
+            # 生成报告（保持原有格式）
+            report = {
+                'stock_code': stock_code,
+                'stock_name': stock_name,
+                'industry': industry,
+                'analysis_date': datetime.now().strftime('%Y-%m-%d'),
+                'score': score,
+                'price': latest['close'],
+                'price_change': (latest['close'] - prev['close']) / prev['close'] * 100,
+                'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
+                'rsi': latest['RSI'],
+                'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL',
+                'volume_status': '放量' if latest['Volume_Ratio'] > 1.5 else '平量',
+                'recommendation': self.get_recommendation(score),
+                'ai_analysis': self._build_stock_prompt_and_get_analysis(df, stock_code)
+            }
+
+            return report
+
+        except Exception as e:
+            self.logger.error(f"分析股票时出错: {str(e)}")
+            raise
+
+    # 原有API：保持接口不变
+    def scan_market(self, stock_list, min_score=60, market_type='A'):
+        """扫描市场，寻找符合条件的股票"""
+        recommendations = []
+        total_stocks = len(stock_list)
+
+        self.logger.info(f"开始市场扫描，共 {total_stocks} 只股票")
+        start_time = time.time()
+        processed = 0
+
+        # 批量处理，减少日志输出
+        batch_size = 10
+        for i in range(0, total_stocks, batch_size):
+            batch = stock_list[i:i + batch_size]
+            batch_results = []
+
+            for stock_code in batch:
+                try:
+                    # 使用简化版分析以加快速度
+                    report = self.quick_analyze_stock(stock_code, market_type)
+                    if report['score'] >= min_score:
+                        batch_results.append(report)
+                except Exception as e:
+                    self.logger.error(f"分析股票 {stock_code} 时出错: {str(e)}")
+                    continue
+
+            # 添加批处理结果
+            recommendations.extend(batch_results)
+
+            # 更新处理进度
+            processed += len(batch)
+            elapsed = time.time() - start_time
+            remaining = (elapsed / processed) * (total_stocks - processed) if processed > 0 else 0
+
+            self.logger.info(
+                f"已处理 {processed}/{total_stocks} 只股票，耗时 {elapsed:.1f}秒，预计剩余 {remaining:.1f}秒")
+
+        # 按得分排序
+        recommendations.sort(key=lambda x: x['score'], reverse=True)
+
+        total_time = time.time() - start_time
+        self.logger.info(
+            f"市场扫描完成，共分析 {total_stocks} 只股票，找到 {len(recommendations)} 只符合条件的股票，总耗时 {total_time:.1f}秒")
+
+        return recommendations
+
+    # def quick_analyze_stock(self, stock_code, market_type='A'):
+    #     """快速分析股票，用于市场扫描"""
+    #     try:
+    #         # 获取股票数据
+    #         df = self.get_stock_data(stock_code, market_type)
+
+    #         # 计算技术指标
+    #         df = self.calculate_indicators(df)
+
+    #         # 简化评分计算
+    #         score = self.calculate_score(df)
+
+    #         # 获取最新数据
+    #         latest = df.iloc[-1]
+    #         prev = df.iloc[-2] if len(df) > 1 else latest
+
+    #         # 尝试获取股票名称和行业
+    #         try:
+    #             stock_info = self.get_stock_info(stock_code)
+    #             stock_name = stock_info.get('股票名称', '未知')
+    #             industry = stock_info.get('行业', '未知')
+    #         except:
+    #             stock_name = '未知'
+    #             industry = '未知'
+
+    #         # 生成简化报告
+    #         report = {
+    #             'stock_code': stock_code,
+    #             'stock_name': stock_name,
+    #             'industry': industry,
+    #             'analysis_date': datetime.now().strftime('%Y-%m-%d'),
+    #             'score': score,
+    #             'price': float(latest['close']),
+    #             'price_change': float((latest['close'] - prev['close']) / prev['close'] * 100),
+    #             'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
+    #             'rsi': float(latest['RSI']),
+    #             'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL',
+    #             'volume_status': '放量' if latest['Volume_Ratio'] > 1.5 else '平量',
+    #             'recommendation': self.get_recommendation(score)
+    #         }
+
+    #         return report
+    #     except Exception as e:
+    #         self.logger.error(f"快速分析股票 {stock_code} 时出错: {str(e)}")
+    #         raise
+
+    def quick_analyze_stock(self, stock_code, market_type='A'):
+        """快速分析股票，用于市场扫描"""
+        try:
+            # 获取股票数据
+            df = self.get_stock_data(stock_code, market_type)
+
+            if df.empty:
+                self.logger.warning(f"无法为 {stock_code} 获取有效数据，跳过分析。")
+                raise ValueError(f"股票 {stock_code} 的数据为空或无法处理")
+
+            # 计算技术指标
+            df = self.calculate_indicators(df)
+
+            # 简化评分计算
+            score = self.calculate_score(df)
+
+            # 获取最新数据
+            latest = df.iloc[-1]
+            prev = df.iloc[-2] if len(df) > 1 else latest
+
+            # 先获取股票信息再生成报告
+            try:
+                stock_info = self.get_stock_info(stock_code)
+                stock_name = stock_info.get('股票名称', '未知')
+                industry = stock_info.get('行业', '未知')
+
+                # 添加日志
+                self.logger.info(f"股票 {stock_code} 信息: 名称={stock_name}, 行业={industry}")
+            except Exception as e:
+                self.logger.error(f"获取股票 {stock_code} 信息时出错: {str(e)}")
+                stock_name = '未知'
+                industry = '未知'
+
+            # 生成简化报告
+            report = {
+                'stock_code': stock_code,
+                'stock_name': stock_name,
+                'industry': industry,
+                'analysis_date': datetime.now().strftime('%Y-%m-%d'),
+                'score': score,
+                'price': float(latest['close']),
+                'price_change': float((latest['close'] - prev['close']) / prev['close'] * 100),
+                'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
+                'rsi': float(latest['RSI']),
+                'macd_signal': 'BUY' if latest['MACD'] > latest['Signal'] else 'SELL',
+                'volume_status': 'HIGH' if latest['Volume_Ratio'] > 1.5 else 'NORMAL',
+                'recommendation': self.get_recommendation(score)
+            }
+
+            return report
+        except Exception as e:
+            self.logger.error(f"快速分析股票 {stock_code} 时出错: {str(e)}")
+            raise
+
+    # ======================== 新增功能 ========================#
+
+    def get_stock_info(self, stock_code):
+        """获取股票基本信息"""
+        import akshare as ak
+
+        cache_key = f"{stock_code}_info"
+        if cache_key in self.data_cache:
+            return self.data_cache[cache_key]
+
+        try:
+            # 获取A股股票基本信息
+            stock_info = ak.stock_individual_info_em(symbol=stock_code)
+
+            # 修改：使用列名而不是索引访问数据
+            info_dict = {}
+            for _, row in stock_info.iterrows():
+                # 使用iloc安全地获取数据
+                if len(row) >= 2:  # 确保有至少两列
+                    info_dict[row.iloc[0]] = row.iloc[1]
+
+            # 获取股票名称
+            try:
+                stock_name_df = ak.stock_info_a_code_name()
+                
+                # 标准化列名
+                rename_map = {
+                    "代码": "code", "名称": "name", "symbol": "code", "股票代码": "code", "stock_code": "code",
+                    "股票名称": "name", "stock_name": "name"
+                }
+                stock_name_df.rename(columns=lambda c: rename_map.get(c, c), inplace=True)
+
+                if 'code' in stock_name_df.columns and 'name' in stock_name_df.columns:
+                    name_series = stock_name_df.set_index('code')['name']
+                    name = name_series.get(str(stock_code))
+                    if not name:
+                         self.logger.warning(f"无法从 stock_info_a_code_name 找到股票代码 {stock_code} 的名称")
+                         name = "未知"
+                else:
+                    self.logger.warning(f"stock_info_a_code_name 返回的DataFrame缺少 'code' 或 'name' 列: {stock_name_df.columns.tolist()}")
+                    name = "未知"
+
+            except Exception as e:
+                self.logger.error(f"获取股票名称时出错: {str(e)}")
+                name = "未知"
+
+            info_dict['股票名称'] = name
+
+            # 确保基本字段存在
+            if '行业' not in info_dict:
+                info_dict['行业'] = "未知"
+            if '地区' not in info_dict:
+                info_dict['地区'] = "未知"
+
+            # 增加更多日志来调试问题
+            self.logger.info(f"获取到股票信息: 名称={name}, 行业={info_dict.get('行业', '未知')}")
+
+            self.data_cache[cache_key] = info_dict
+            return info_dict
+        except Exception as e:
+            self.logger.error(f"获取股票信息失败: {str(e)}")
+            return {"股票名称": "未知", "行业": "未知", "地区": "未知"}
+
+    def identify_support_resistance(self, df):
+        """识别支撑位和压力位"""
+        latest_price = df['close'].iloc[-1]
+
+        # 使用布林带作为支撑压力参考
+        support_levels = [df['BB_lower'].iloc[-1]]
+        resistance_levels = [df['BB_upper'].iloc[-1]]
+
+        # 添加主要均线作为支撑压力
+        if latest_price < df['MA5'].iloc[-1]:
+            resistance_levels.append(df['MA5'].iloc[-1])
+        else:
+            support_levels.append(df['MA5'].iloc[-1])
+
+        if latest_price < df['MA20'].iloc[-1]:
+            resistance_levels.append(df['MA20'].iloc[-1])
+        else:
+            support_levels.append(df['MA20'].iloc[-1])
+
+        # 添加整数关口
+        price_digits = len(str(int(latest_price)))
+        base = 10 ** (price_digits - 1)
+
+        lower_integer = math.floor(latest_price / base) * base
+        upper_integer = math.ceil(latest_price / base) * base
+
+        if lower_integer < latest_price:
+            support_levels.append(lower_integer)
+        if upper_integer > latest_price:
+            resistance_levels.append(upper_integer)
+
+        # 排序并格式化
+        support_levels = sorted(set([round(x, 2) for x in support_levels if x < latest_price]), reverse=True)
+        resistance_levels = sorted(set([round(x, 2) for x in resistance_levels if x > latest_price]))
+
+        # 分类为短期和中期
+        short_term_support = support_levels[:1] if support_levels else []
+        medium_term_support = support_levels[1:2] if len(support_levels) > 1 else []
+        short_term_resistance = resistance_levels[:1] if resistance_levels else []
+        medium_term_resistance = resistance_levels[1:2] if len(resistance_levels) > 1 else []
+
+        return {
+            'support_levels': {
+                'short_term': short_term_support,
+                'medium_term': medium_term_support
+            },
+            'resistance_levels': {
+                'short_term': short_term_resistance,
+                'medium_term': medium_term_resistance
+            }
+        }
+
+    def calculate_technical_score(self, df):
+        """计算技术面评分 (0-40分)"""
+        try:
+            score = 0
+            # 确保有足够的数据
+            if len(df) < 2:
+                self.logger.warning("数据不足，无法计算技术面评分")
+                return {'total': 0, 'trend': 0, 'indicators': 0, 'support_resistance': 0, 'volatility_volume': 0}
+
+            latest = df.iloc[-1]
+            prev = df.iloc[-2]  # 获取前一个时间点的数据
+            prev_close = prev['close']
+
+            # 1. 趋势分析 (0-10分)
+            trend_score = 0
+
+            # 均线排列情况
+            if latest['MA5'] > latest['MA20'] > latest['MA60']:  # 多头排列
+                trend_score += 5
+            elif latest['MA5'] < latest['MA20'] < latest['MA60']:  # 空头排列
+                trend_score = 0
+            else:  # 交叉状��
+                if latest['MA5'] > latest['MA20']:
+                    trend_score += 3
+                if latest['MA20'] > latest['MA60']:
+                    trend_score += 2
+
+            # 价格与均线关系
+            if latest['close'] > latest['MA5']:
+                trend_score += 3
+            elif latest['close'] > latest['MA20']:
+                trend_score += 2
+
+            # 限制最大值
+            trend_score = min(trend_score, 10)
+            score += trend_score
+
+            # 2. 技术指标分析 (0-10分)
+            indicator_score = 0
+
+            # RSI
+            if 40 <= latest['RSI'] <= 60:  # 中性
+                indicator_score += 2
+            elif 30 <= latest['RSI'] < 40 or 60 < latest['RSI'] <= 70:  # 边缘区域
+                indicator_score += 4
+            elif latest['RSI'] < 30:  # 超卖
+                indicator_score += 5
+            elif latest['RSI'] > 70:  # 超买
+                indicator_score += 0
+
+            # MACD
+            if latest['MACD'] > latest['Signal']:  # MACD金叉或在零轴上方
+                indicator_score += 3
+            else:
+                # 修复：比较当前和前一个时间点的MACD柱状图值
+                if latest['MACD_hist'] > prev['MACD_hist']:  # 柱状图上升
+                    indicator_score += 1
+
+            # 限制最大值和最小值
+            indicator_score = max(0, min(indicator_score, 10))
+            score += indicator_score
+
+            # 3. 支撑压力位分析 (0-10分)
+            sr_score = 0
+
+            # 识别支撑位和压力位
+            middle_price = latest['close']
+            upper_band = latest['BB_upper']
+            lower_band = latest['BB_lower']
+
+            # 距离布林带上下轨的距离
+            upper_distance = (upper_band - middle_price) / middle_price * 100
+            lower_distance = (middle_price - lower_band) / middle_price * 100
+
+            if lower_distance < 2:  # 接近下轨
+                sr_score += 5
+            elif lower_distance < 5:
+                sr_score += 3
+
+            if upper_distance > 5:  # 距上轨较远
+                sr_score += 5
+            elif upper_distance > 2:
+                sr_score += 2
+
+            # 限制最大值
+            sr_score = min(sr_score, 10)
+            score += sr_score
+
+            # 4. 波动性和成交量分析 (0-10分)
+            vol_score = 0
+
+            # 波动率分析
+            if latest['Volatility'] < 2:  # 低波动率
+                vol_score += 3
+            elif latest['Volatility'] < 4:  # 中等波动率
+                vol_score += 2
+
+            # 成交量分析
+            if 'Volume_Ratio' in df.columns:
+                if latest['Volume_Ratio'] > 1.5 and latest['close'] > prev_close:  # 放量上涨
+                    vol_score += 4
+                elif latest['Volume_Ratio'] < 0.8 and latest['close'] < prev_close:  # 缩量下跌
+                    vol_score += 3
+                elif latest['Volume_Ratio'] > 1 and latest['close'] > prev_close:  # 普通放量上涨
+                    vol_score += 2
+
+            # 限制最大值
+            vol_score = min(vol_score, 10)
+            score += vol_score
+
+            # 保存各个维度的分数
+            technical_scores = {
+                'total': score,
+                'trend': trend_score,
+                'indicators': indicator_score,
+                'support_resistance': sr_score,
+                'volatility_volume': vol_score
+            }
+
+            return technical_scores
+
+        except Exception as e:
+            self.logger.error(f"计算技术面评分时出错: {str(e)}")
+            self.logger.error(f"错误详情: {traceback.format_exc()}")
+            return {'total': 0, 'trend': 0, 'indicators': 0, 'support_resistance': 0, 'volatility_volume': 0}
+
+    def perform_enhanced_analysis(self, stock_code, market_type='A'):
+        """执行增强版分析"""
+        try:
+            # 记录开始时间，便于性能分析
+            start_time = time.time()
+            self.logger.info(f"开始执行股票 {stock_code} 的增强分析")
+
+            # 获取股票数据
+            df = self.get_stock_data(stock_code, market_type)
+            data_time = time.time()
+            self.logger.info(f"获取股票数据耗时: {data_time - start_time:.2f}秒")
+
+            # 计算技术指标
+            df = self.calculate_indicators(df)
+            indicator_time = time.time()
+            self.logger.info(f"计算技术指标耗时: {indicator_time - data_time:.2f}秒")
+
+            # 获取最新数据
+            latest = df.iloc[-1]
+            prev = df.iloc[-2] if len(df) > 1 else latest
+
+            # 获取支撑压力位
+            sr_levels = self.identify_support_resistance(df)
+
+            # 计算技术面评分
+            technical_score = self.calculate_technical_score(df)
+
+            # 获取股票信息
+            stock_info = self.get_stock_info(stock_code)
+
+            # 确保technical_score包含必要的字段
+            if 'total' not in technical_score:
+                technical_score['total'] = 0
+
+            # 生成增强版报告
+            enhanced_report = {
+                'basic_info': {
+                    'stock_code': stock_code,
+                    'stock_name': stock_info.get('股票名称', '未知'),
+                    'industry': stock_info.get('行业', '未知'),
+                    'analysis_date': datetime.now().strftime('%Y-%m-%d')
+                },
+                'price_data': {
+                    'current_price': float(latest['close']),  # 确保是Python原生类型
+                    'price_change': float((latest['close'] - prev['close']) / prev['close'] * 100),
+                    'price_change_value': float(latest['close'] - prev['close'])
+                },
+                'technical_analysis': {
+                    'trend': {
+                        'ma_trend': 'UP' if latest['MA5'] > latest['MA20'] else 'DOWN',
+                        'ma_status': "多头排列" if latest['MA5'] > latest['MA20'] > latest['MA60'] else
+                        "空头排列" if latest['MA5'] < latest['MA20'] < latest['MA60'] else
+                        "交叉状态",
+                        'ma_values': {
+                            'ma5': float(latest['MA5']),
+                            'ma20': float(latest['MA20']),
+                            'ma60': float(latest['MA60'])
+                        }
+                    },
+                    'indicators': {
+                        # 确保所有指标都存在并是原生类型
+                        'rsi': float(latest['RSI']) if 'RSI' in latest else 50.0,
+                        'macd': float(latest['MACD']) if 'MACD' in latest else 0.0,
+                        'macd_signal': float(latest['Signal']) if 'Signal' in latest else 0.0,
+                        'macd_histogram': float(latest['MACD_hist']) if 'MACD_hist' in latest else 0.0,
+                        'volatility': float(latest['Volatility']) if 'Volatility' in latest else 0.0
+                    },
+                    'volume': {
+                        'current_volume': float(latest['volume']) if 'volume' in latest else 0.0,
+                        'volume_ratio': float(latest['Volume_Ratio']) if 'Volume_Ratio' in latest else 1.0,
+                        'volume_status': '放量' if 'Volume_Ratio' in latest and latest['Volume_Ratio'] > 1.5 else '平量'
+                    },
+                    'support_resistance': sr_levels
+                },
+                'scores': technical_score,
+                'recommendation': {
+                    'action': self.get_recommendation(technical_score['total']),
+                    'key_points': []
+                },
+                'ai_analysis': self._build_stock_prompt_and_get_analysis(df, stock_code)
+            }
+
+            # 最后检查并修复报告结构
+            self._validate_and_fix_report(enhanced_report)
+
+            # 在函数结束时记录总耗时
+            end_time = time.time()
+            self.logger.info(f"执行增强分析总耗时: {end_time - start_time:.2f}秒")
+
+            return enhanced_report
+
+        except Exception as e:
+            self.logger.error(f"执行增强版分析时出错: {str(e)}")
+            self.logger.error(traceback.format_exc())
+
+            # 返回基础错误报告
+            return {
+                'basic_info': {
+                    'stock_code': stock_code,
+                    'stock_name': '分析失败',
+                    'industry': '未知',
+                    'analysis_date': datetime.now().strftime('%Y-%m-%d')
+                },
+                'price_data': {
+                    'current_price': 0.0,
+                    'price_change': 0.0,
+                    'price_change_value': 0.0
+                },
+                'technical_analysis': {
+                    'trend': {
+                        'ma_trend': 'UNKNOWN',
+                        'ma_status': '未知',
+                        'ma_values': {'ma5': 0.0, 'ma20': 0.0, 'ma60': 0.0}
+                    },
+                    'indicators': {
+                        'rsi': 50.0,
+                        'macd': 0.0,
+                        'macd_signal': 0.0,
+                        'macd_histogram': 0.0,
+                        'volatility': 0.0
+                    },
+                    'volume': {
+                        'current_volume': 0.0,
+                        'volume_ratio': 0.0,
+                        'volume_status': 'NORMAL'
+                    },
+                    'support_resistance': {
+                        'support_levels': {'short_term': [], 'medium_term': []},
+                        'resistance_levels': {'short_term': [], 'medium_term': []}
+                    }
+                },
+                'scores': {'total': 0},
+                'recommendation': {'action': '分析出错，无法提供建议'},
+                'ai_analysis': f"分���过程中出错: {str(e)}"
+            }
+
+            return error_report
+
+    # 添加一个辅助方法确保报告结构完整
+    def _validate_and_fix_report(self, report):
+        """确保分析报告结构完整"""
+        # 检查必要的顶级字段
+        required_sections = ['basic_info', 'price_data', 'technical_analysis', 'scores', 'recommendation',
+                             'ai_analysis']
+        for section in required_sections:
+            if section not in report:
+                self.logger.warning(f"报告缺少 {section} 部分，添加空对象")
+                report[section] = {}
+
+        # 检查technical_analysis的结构
+        if 'technical_analysis' in report:
+            tech = report['technical_analysis']
+            if not isinstance(tech, dict):
+                report['technical_analysis'] = {}
+                tech = report['technical_analysis']
+
+            # 检查indicators部分
+            if 'indicators' not in tech or not isinstance(tech['indicators'], dict):
+                tech['indicators'] = {
+                    'rsi': 50.0,
+                    'macd': 0.0,
+                    'macd_signal': 0.0,
+                    'macd_histogram': 0.0,
+                    'volatility': 0.0
+                }
+
+            # 转换所有指标为原生Python类型
+            for key, value in tech['indicators'].items():
+                try:
+                    tech['indicators'][key] = float(value)
+                except (TypeError, ValueError):
+                    tech['indicators'][key] = 0.0

app/analysis/stock_qa.py

@@ -0,0 +1,484 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+
+stock_qa.py - 提供股票相关问题的智能问答功能，支持联网搜索实时信息和多轮对话
+"""
+
+import os
+import json
+import traceback
+import openai
+from openai import OpenAI
+from urllib.parse import urlparse
+from datetime import datetime
+
+
+class StockQA:
+    def __init__(self, analyzer, openai_api_key=None):
+        self.analyzer = analyzer
+        self.openai_api_key = os.getenv('OPENAI_API_KEY', openai_api_key)
+        self.openai_api_url = os.getenv('OPENAI_API_URL', 'https://api.openai.com/v1')
+        self.openai_model = os.getenv('OPENAI_API_MODEL', 'gpt-4o')
+        self.client = OpenAI(
+            api_key=self.openai_api_key,
+            base_url=self.openai_api_url
+        )
+        self.serp_api_key = os.getenv('SERP_API_KEY')
+        self.tavily_api_key = os.getenv('TAVILY_API_KEY')
+        self.max_qa_rounds = int(os.getenv('MAX_QA', '10'))  # 默认保留10轮对话
+        
+        # 对话历史存储 - 使用字典存储不同股票的对话历史
+        self.conversation_history = {}
+        
+        # 设置日志记录
+        import logging
+        self.logger = logging.getLogger(__name__)
+
+    def answer_question(self, stock_code, question, market_type='A', conversation_id=None, clear_history=False):
+        """
+        回答关于股票的问题，支持联网搜索实时信息和多轮对话
+        
+        参数:
+            stock_code: 股票代码
+            question: 用户问题
+            market_type: 市场类型 (A/HK/US)
+            conversation_id: 对话ID，用于跟踪对话历史，如果为None则自动生成
+            clear_history: 是否清除对话历史，开始新对话
+        
+        返回:
+            包含回答和元数据的字典
+        """
+        try:
+            if not self.openai_api_key:
+                return {"error": "未配置API密钥，无法使用智能问答功能"}
+
+            # 处理对话ID和历史
+            if conversation_id is None:
+                # 生成新的对话ID
+                import uuid
+                conversation_id = f"{stock_code}_{uuid.uuid4().hex[:8]}"
+            
+            # 获取或创建对话历史
+            if clear_history or conversation_id not in self.conversation_history:
+                self.conversation_history[conversation_id] = []
+            
+            # 获取股票信息和技术指标 - 每次都获取最新数据
+            stock_context = self._get_stock_context(stock_code, market_type)
+            stock_name = stock_context.get("stock_name", "未知")
+                
+            # 定义搜索新闻的工具
+            tools = [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": "search_stock_news",
+                        "description": "搜索股票相关的最新新闻、公告和行业动态信息，以获取实时市场信息",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "query": {
+                                    "type": "string",
+                                    "description": "搜索查询词，用于查找相关新闻"
+                                }
+                            },
+                            "required": ["query"]
+                        }
+                    }
+                }
+            ]
+
+            # 设置增强版系统提示
+            system_content = """你是摩根大通的高级宏观策略师和首席投资顾问，拥有哈佛经济学博士学位和20年华尔街顶级投行经验。你同时也是国家发改委、央行和证监会的政策研究顾问团专家，了解中国宏观经济和产业政策走向。
+
+你的特点是：
+1. 思维深度 - 从表面现象洞察深层次的经济周期、产业迁移和资本流向规律，预见市场忽视的长期趋势
+2. 全局视角 - 将个股分析放在全球经济格局、国内政策环境、产业转型、供应链重构和流动性周期的大背景下
+3. 结构化思考 - 运用专业框架如PEST分析、波特五力模型、杜邦分析、价值链分析和SWOT分析等系统评估
+4. 多层次透视 - 能同时从资本市场定价、产业发展阶段、公司竞争地位和治理结构等维度剖析股票价值
+5. 前瞻预判 - 善于前瞻性分析科技创新、产业政策和地缘政治变化对中长期市场格局的影响
+
+沟通时，你会：
+- 将复杂的金融概念转化为简洁明了的比喻和案例，使普通投资者理解专业分析
+- 强调投资思维和方法论，而非简单的买卖建议
+- 提供层次分明的分析：1)微观公司基本面 2)中观产业格局 3)宏观经济环境
+- 引用相关研究、历史案例或数据支持你的观点
+- 在必要时搜���最新资讯，确保观点基于最新市场情况
+- 兼顾短中长期视角，帮助投资者建立自己的投资决策框架
+
+作为金融专家，你始终：
+- 谨慎评估不同情景下的概率分布，而非做出确定性预测
+- 坦承市场的不确定性和你认知的边界
+- 同时提供乐观和保守的观点，帮助用户全面权衡
+- 强调风险管理和长期投资价值
+- 避免传播市场谣言或未经证实的信息
+
+请记住，你的价值在于提供深度思考框架和专业视角，帮助投资者做出明智决策，而非简单的投资指令。在需要时，使用search_stock_news工具获取最新市场信息。
+"""
+
+            # 准备对话消息列表，加入系统提示和股票上下文
+            messages = [
+                {"role": "system", "content": system_content},
+                {"role": "user", "content": f"以下是关于股票的基础信息，作为我们对话的背景资料：\n\n{stock_context['context']}"}
+            ]
+            
+            # 添加对话历史记录
+            messages.extend(self.conversation_history[conversation_id])
+            
+            # 添加当前问题
+            messages.append({"role": "user", "content": question})
+            
+            
+            # 调用AI API
+            #第一步：调用模型，让它决定是否使用工具
+            first_response = self.client.chat.completions.create(
+                model=self.openai_model,
+                messages=messages,
+                tools=tools,
+                tool_choice="auto",
+                temperature=0.7
+            )
+
+            # 获取初始响应
+            assistant_message = first_response.choices[0].message
+            response_content = assistant_message.content
+            used_search_tool = False
+            
+            # 检查是否需要使用工具调用
+            if hasattr(assistant_message, 'tool_calls') and assistant_message.tool_calls:
+                used_search_tool = True
+                # 创建新的消息列表，包含工具调用
+                tool_messages = list(messages)  # 复制原始消息列表
+                tool_messages.append({"role": "assistant", "tool_calls": assistant_message.tool_calls})
+                
+                # 处理工具调用
+                for tool_call in assistant_message.tool_calls:
+                    function_name = tool_call.function.name
+                    function_args = json.loads(tool_call.function.arguments)
+                    
+                    if function_name == "search_stock_news":
+                        # 执行新闻搜索
+                        search_query = function_args.get("query")
+                        self.logger.info(f"执行新闻搜索: {search_query}")
+                        search_results = self.search_stock_news(
+                            search_query, 
+                            stock_context.get("stock_name", ""),
+                            stock_code, 
+                            stock_context.get("industry", ""),
+                            market_type
+                        )
+                        
+                        # 添加工具响应
+                        tool_messages.append({
+                            "tool_call_id": tool_call.id,
+                            "role": "tool",
+                            "name": function_name,
+                            "content": json.dumps(search_results, ensure_ascii=False)
+                        })
+                
+                # 第二步：让模型根据工具调用结果生成最终响应
+                second_response = self.client.chat.completions.create(
+                    model=self.openai_model,
+                    messages=tool_messages,
+                    temperature=0.7
+                )
+                
+                response_content = second_response.choices[0].message.content
+                assistant_message = {"role": "assistant", "content": response_content}
+            else:
+                assistant_message = {"role": "assistant", "content": response_content}
+            
+            # 更新对话历史
+            self.conversation_history[conversation_id].append({"role": "user", "content": question})
+            self.conversation_history[conversation_id].append(assistant_message)
+            
+            # 限制对话历史长度
+            if len(self.conversation_history[conversation_id]) > self.max_qa_rounds * 2:
+                # 保留最近的MAX_QA轮对话
+                self.conversation_history[conversation_id] = self.conversation_history[conversation_id][-self.max_qa_rounds * 2:]
+            
+            # 返回结果
+            return {
+                "conversation_id": conversation_id,
+                "question": question,
+                "answer": response_content,
+                "stock_code": stock_code,
+                "stock_name": stock_name,
+                "used_search_tool": used_search_tool,
+                "conversation_length": len(self.conversation_history[conversation_id]) // 2  # 轮数
+            }
+
+        except Exception as e:
+            self.logger.error(f"智能问答出错: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            return {
+                "question": question,
+                "answer": f"抱歉，回答问题时出错: {str(e)}",
+                "stock_code": stock_code,
+                "error": str(e)
+            }
+
+    def _get_stock_context(self, stock_code, market_type='A'):
+        """获取股票上下文信息"""
+        try:
+            # 获取股票信息
+            stock_info = self.analyzer.get_stock_info(stock_code)
+            stock_name = stock_info.get('股票名称', '未知')
+            industry = stock_info.get('行业', '未知')
+
+            # 获取技术指标数据
+            df = self.analyzer.get_stock_data(stock_code, market_type)
+            df = self.analyzer.calculate_indicators(df)
+
+            # 提取最新数据
+            latest = df.iloc[-1]
+
+            # 计算评分
+            score = self.analyzer.calculate_score(df)
+
+            # 获取支撑压力位
+            sr_levels = self.analyzer.identify_support_resistance(df)
+
+            # 构建上下文
+            context = f"""股票信息:
+- 代码: {stock_code}
+- 名称: {stock_name}
+- 行业: {industry}
+
+技术指标(最新数据):
+- 价格: {latest['close']}
+- 5日均线: {latest['MA5']}
+- 20日均线: {latest['MA20']}
+- 60日均线: {latest['MA60']}
+- RSI: {latest['RSI']}
+- MACD: {latest['MACD']}
+- MACD信号线: {latest['Signal']}
+- 布林带上轨: {latest['BB_upper']}
+- 布林带中轨: {latest['BB_middle']}
+- 布林带下轨: {latest['BB_lower']}
+- 波动率: {latest['Volatility']}%
+
+技术评分: {score}分
+
+支撑位:
+- 短期: {', '.join([str(level) for level in sr_levels['support_levels']['short_term']])}
+- 中期: {', '.join([str(level) for level in sr_levels['support_levels']['medium_term']])}
+
+压力位:
+- 短期: {', '.join([str(level) for level in sr_levels['resistance_levels']['short_term']])}
+- 中期: {', '.join([str(level) for level in sr_levels['resistance_levels']['medium_term']])}"""
+
+            # 尝试获取基本面数据
+            try:
+                # 导入基本面分析器
+                from app.analysis.fundamental_analyzer import FundamentalAnalyzer
+                fundamental = FundamentalAnalyzer()
+
+                # 获取基本面数据
+                indicators = fundamental.get_financial_indicators(stock_code)
+
+                # 添加到上下文
+                context += f"""
+
+基本面指标:
+- PE(TTM): {indicators.get('pe_ttm', '未知')}
+- PB: {indicators.get('pb', '未知')}
+- ROE: {indicators.get('roe', '未知')}%
+- 毛利率: {indicators.get('gross_margin', '未知')}%
+- 净利率: {indicators.get('net_profit_margin', '未知')}%"""
+            except Exception as e:
+                self.logger.warning(f"获取基本面数据失败: {str(e)}")
+                context += "\n\n注意：未能获取基本面数据"
+
+            return {
+                "context": context,
+                "stock_name": stock_name,
+                "industry": industry
+            }
+        except Exception as e:
+            self.logger.error(f"获取股票上下文信息出错: {str(e)}")
+            return {
+                "context": f"无法获取股票 {stock_code} 的完整信息: {str(e)}",
+                "stock_name": "未知",
+                "industry": "未知"
+            }
+
+    def clear_conversation(self, conversation_id=None, stock_code=None):
+        """
+        清除特定对话或与特定股票相关的所有对话历史
+        
+        参数:
+            conversation_id: 指定要清除的对话ID
+            stock_code: 指定要清除的股票相关的所有对话
+        """
+        if conversation_id and conversation_id in self.conversation_history:
+            # 清除特定对话
+            del self.conversation_history[conversation_id]
+            return {"message": f"已清除对话 {conversation_id}"}
+            
+        elif stock_code:
+            # 清除与特定股票相关的所有对话
+            removed = []
+            for conv_id in list(self.conversation_history.keys()):
+                if conv_id.startswith(f"{stock_code}_"):
+                    del self.conversation_history[conv_id]
+                    removed.append(conv_id)
+            return {"message": f"已清除与股票 {stock_code} 相关的 {len(removed)} 个对话"}
+            
+        else:
+            # 清除所有对话
+            count = len(self.conversation_history)
+            self.conversation_history.clear()
+            return {"message": f"已清除所有 {count} 个对话"}
+
+    def get_conversation_history(self, conversation_id):
+        """获取特定对话的历史记录"""
+        if conversation_id not in self.conversation_history:
+            return {"error": f"找不到对话 {conversation_id}"}
+            
+        # 提取用户问题和助手回答
+        history = []
+        conversation = self.conversation_history[conversation_id]
+        
+        # 按对话轮次提取历史
+        for i in range(0, len(conversation), 2):
+            if i+1 < len(conversation):
+                history.append({
+                    "question": conversation[i]["content"],
+                    "answer": conversation[i+1]["content"]
+                })
+                
+        return {
+            "conversation_id": conversation_id,
+            "history": history,
+            "round_count": len(history)
+        }
+
+    def search_stock_news(self, query, stock_name, stock_code, industry, market_type='A'):
+        """搜索股票相关新闻和实时信息"""
+        try:
+            self.logger.info(f"搜索股票新闻: {query}")
+            
+            # 确定市场名称
+            market_name = "A股" if market_type == 'A' else "港股" if market_type == 'HK' else "美股"
+            
+            # 检查API密钥
+            if not self.serp_api_key and not self.tavily_api_key:
+                self.logger.warning("未配置搜索API密钥")
+                return {
+                    "message": "无法搜索新闻，未配置搜索API密钥",
+                    "results": []
+                }
+            
+            news_results = []
+            
+            # 使用SERP API搜索
+            if self.serp_api_key:
+                try:
+                    import requests
+                    
+                    # 构建搜索查询
+                    search_query = f"{stock_name} {stock_code} {market_name} {query}"
+                    
+                    # 调用SERP API
+                    url = "https://serpapi.com/search"
+                    params = {
+                        "engine": "google",
+                        "q": search_query,
+                        "api_key": self.serp_api_key,
+                        "tbm": "nws",  # 新闻搜索
+                        "num": 5  # 获取5条结果
+                    }
+                    
+                    response = requests.get(url, params=params)
+                    search_results = response.json()
+                    
+                    # 提取新闻结果
+                    if "news_results" in search_results:
+                        for item in search_results["news_results"]:
+                            news_results.append({
+                                "title": item.get("title", ""),
+                                "date": item.get("date", ""),
+                                "source": item.get("source", ""),
+                                "snippet": item.get("snippet", ""),
+                                "link": item.get("link", "")
+                            })
+                except Exception as e:
+                    self.logger.error(f"SERP API搜索出错: {str(e)}")
+            
+            # 使用Tavily API搜索
+            if self.tavily_api_key:
+                try:
+                    from tavily import TavilyClient
+                    
+                    client = TavilyClient(self.tavily_api_key)
+                    
+                    # 构建搜索查询
+                    search_query = f"{stock_name} {stock_code} {market_name} {query}"
+                    
+                    # 调用Tavily API
+                    response = client.search(
+                        query=search_query,
+                        topic="finance",
+                        search_depth="advanced"
+                    )
+                    
+                    # 提取结果
+                    if "results" in response:
+                        for item in response["results"]:
+                            # 从URL提取域名作为来源
+                            source = ""
+                            if item.get("url"):
+                                try:
+                                    parsed_url = urlparse(item.get("url"))
+                                    source = parsed_url.netloc
+                                except:
+                                    source = "未知来源"
+                            
+                            news_results.append({
+                                "title": item.get("title", ""),
+                                "date": datetime.now().strftime("%Y-%m-%d"),  # Tavily不提供日期
+                                "source": source,
+                                "snippet": item.get("content", ""),
+                                "link": item.get("url", "")
+                            })
+                except ImportError:
+                    self.logger.warning("未安装Tavily客户端库，请使用pip install tavily-python安装")
+                except Exception as e:
+                    self.logger.error(f"Tavily API搜索出错: {str(e)}")
+            
+            # 去重并限制结果数量
+            unique_results = []
+            seen_titles = set()
+            
+            for item in news_results:
+                title = item.get("title", "").strip()
+                if title and title not in seen_titles:
+                    seen_titles.add(title)
+                    unique_results.append(item)
+                    if len(unique_results) >= 5:  # 最多返回5条结果
+                        break
+            
+            # 创建格式化的摘要文本
+            summary_text = ""
+            for i, item in enumerate(unique_results):
+                summary_text += f"{i+1}、{item.get('title', '')}\n"
+                summary_text += f"{item.get('snippet', '')}\n"
+                summary_text += f"来源: {item.get('source', '')} {item.get('date', '')}\n\n"
+            
+            return {
+                "message": f"找到 {len(unique_results)} 条相关新闻",
+                "results": unique_results,
+                "summary": summary_text
+            }
+            
+        except Exception as e:
+            self.logger.error(f"搜索股票新闻时出错: {str(e)}")
+            self.logger.error(traceback.format_exc())
+            return {
+                "message": f"搜索新闻时出错: {str(e)}",
+                "results": []
+            }

app/analysis/us_stock_service.py

@@ -0,0 +1,67 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+修改：熊猫大侠
+版本：v2.1.0
+"""
+# us_stock_service.py
+import akshare as ak
+import pandas as pd
+import logging
+
+
+class USStockService:
+    def __init__(self):
+        logging.basicConfig(level=logging.INFO,
+                            format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger(__name__)
+
+    def search_us_stocks(self, keyword):
+        """
+        搜索美股代码
+        :param keyword: 搜索关键词
+        :return: 匹配的股票列表
+        """
+        try:
+            # 获取美股数据
+            df = ak.stock_us_spot_em()
+
+            # 转换列名
+            df = df.rename(columns={
+                "序号": "index",
+                "名称": "name",
+                "最新价": "price",
+                "涨跌额": "price_change",
+                "涨跌幅": "price_change_percent",
+                "开盘价": "open",
+                "最高价": "high",
+                "最低价": "low",
+                "昨收价": "pre_close",
+                "总市值": "market_value",
+                "市盈率": "pe_ratio",
+                "成交量": "volume",
+                "成交额": "turnover",
+                "振幅": "amplitude",
+                "换手率": "turnover_rate",
+                "代码": "symbol"
+            })
+
+            # 模糊匹配搜索
+            mask = df['name'].str.contains(keyword, case=False, na=False)
+            results = df[mask]
+
+            # 格式化返回结果并处理 NaN 值
+            formatted_results = []
+            for _, row in results.iterrows():
+                formatted_results.append({
+                    'name': row['name'] if pd.notna(row['name']) else '',
+                    'symbol': str(row['symbol']) if pd.notna(row['symbol']) else '',
+                    'price': float(row['price']) if pd.notna(row['price']) else 0.0,
+                    'market_value': float(row['market_value']) if pd.notna(row['market_value']) else 0.0
+                })
+
+            return formatted_results
+
+        except Exception as e:
+            self.logger.error(f"搜索美股代码时出错: {str(e)}")
+            raise Exception(f"搜索美股代码失败: {str(e)}")

app/core/database.py

@@ -0,0 +1,102 @@
+import os
+from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime, Text, JSON
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+from datetime import datetime
+
+# 读取配置
+DATABASE_URL = os.getenv('DATABASE_URL', 'sqlite:///data/stock_analyzer.db')
+USE_DATABASE = os.getenv('USE_DATABASE', 'False').lower() == 'true'
+
+# 创建引擎
+engine = create_engine(DATABASE_URL)
+Base = declarative_base()
+
+
+# 定义模型
+class StockInfo(Base):
+    __tablename__ = 'stock_info'
+
+    id = Column(Integer, primary_key=True)
+    stock_code = Column(String(10), nullable=False, index=True)
+    stock_name = Column(String(50))
+    market_type = Column(String(5))
+    industry = Column(String(50))
+    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
+
+    def to_dict(self):
+        return {
+            'stock_code': self.stock_code,
+            'stock_name': self.stock_name,
+            'market_type': self.market_type,
+            'industry': self.industry,
+            'updated_at': self.updated_at.strftime('%Y-%m-%d %H:%M:%S') if self.updated_at else None
+        }
+
+
+class AnalysisResult(Base):
+    __tablename__ = 'analysis_results'
+
+    id = Column(Integer, primary_key=True)
+    stock_code = Column(String(10), nullable=False, index=True)
+    market_type = Column(String(5))
+    analysis_date = Column(DateTime, default=datetime.now)
+    score = Column(Float)
+    recommendation = Column(String(100))
+    technical_data = Column(JSON)
+    fundamental_data = Column(JSON)
+    capital_flow_data = Column(JSON)
+    ai_analysis = Column(Text)
+
+    def to_dict(self):
+        return {
+            'stock_code': self.stock_code,
+            'market_type': self.market_type,
+            'analysis_date': self.analysis_date.strftime('%Y-%m-%d %H:%M:%S') if self.analysis_date else None,
+            'score': self.score,
+            'recommendation': self.recommendation,
+            'technical_data': self.technical_data,
+            'fundamental_data': self.fundamental_data,
+            'capital_flow_data': self.capital_flow_data,
+            'ai_analysis': self.ai_analysis
+        }
+
+
+class Portfolio(Base):
+    __tablename__ = 'portfolios'
+
+    id = Column(Integer, primary_key=True)
+    user_id = Column(String(50), nullable=False, index=True)
+    name = Column(String(100))
+    created_at = Column(DateTime, default=datetime.now)
+    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
+    stocks = Column(JSON)  # 存储股票列表的JSON
+
+    def to_dict(self):
+        return {
+            'id': self.id,
+            'user_id': self.user_id,
+            'name': self.name,
+            'created_at': self.created_at.strftime('%Y-%m-%d %H:%M:%S') if self.created_at else None,
+            'updated_at': self.updated_at.strftime('%Y-%m-%d %H:%M:%S') if self.updated_at else None,
+            'stocks': self.stocks
+        }
+
+
+# 创建会话工厂
+Session = sessionmaker(bind=engine)
+
+
+# 初始化数据库
+def init_db():
+    Base.metadata.create_all(engine)
+
+
+# 获取数据库会话
+def get_session():
+    return Session()
+
+
+# 如果启用数据库，则初始化
+if USE_DATABASE:
+    init_db()

app/web/auth_middleware.py

@@ -0,0 +1,79 @@
+from functools import wraps
+from flask import request, jsonify
+import os
+import time
+import hashlib
+import hmac
+
+
+def get_api_key():
+    return os.getenv('API_KEY', 'UZXJfw3YNX80DLfN')
+
+
+def require_api_key(f):
+    """需要API密钥验证的装饰器"""
+    @wraps(f)
+    def decorated_function(*args, **kwargs):
+        api_key = request.headers.get('X-API-Key')
+        if not api_key:
+            return jsonify({'error': '缺少API密钥'}), 401
+
+        if api_key != get_api_key():
+            return jsonify({'error': '无效的API密钥'}), 403
+
+        return f(*args, **kwargs)
+    return decorated_function
+
+
+def generate_hmac_signature(data, secret_key=None):
+    if secret_key is None:
+        secret_key = os.getenv('HMAC_SECRET', 'default_hmac_secret_for_development')
+
+    if isinstance(data, dict):
+        # 对字典进行排序，确保相同的数据产生相同的签名
+        data = '&'.join(f"{k}={v}" for k, v in sorted(data.items()))
+
+    # 使用HMAC-SHA256生成签名
+    signature = hmac.new(
+        secret_key.encode(),
+        data.encode(),
+        hashlib.sha256
+    ).hexdigest()
+
+    return signature
+
+
+def verify_hmac_signature(request_signature, data, secret_key=None):
+    expected_signature = generate_hmac_signature(data, secret_key)
+    return hmac.compare_digest(request_signature, expected_signature)
+
+
+def require_hmac_auth(f):
+    """需要HMAC认证的装饰器"""
+    @wraps(f)
+    def decorated_function(*args, **kwargs):
+        request_signature = request.headers.get('X-HMAC-Signature')
+        if not request_signature:
+            return jsonify({'error': '缺少HMAC签名'}), 401
+
+        # 获取请求数据
+        data = request.get_json(silent=True) or {}
+
+        # 添加时间戳防止重放攻击
+        timestamp = request.headers.get('X-Timestamp')
+        if not timestamp:
+            return jsonify({'error': '缺少时间戳'}), 401
+
+        # 验证时间戳有效性（有效期5分钟）
+        current_time = int(time.time())
+        if abs(current_time - int(timestamp)) > 300:
+            return jsonify({'error': '时间戳已过期'}), 401
+
+        # 将时间戳加入验证数据
+        verification_data = {**data, 'timestamp': timestamp}
+
+        # 验证签名
+        if not verify_hmac_signature(request_signature, verification_data):
+            return jsonify({'error': '签名无效'}), 403
+        return f(*args, **kwargs)
+    return decorated_function

app/web/industry_api_endpoints.py

@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""
+智能分析系统（股票） - 股票市场数据分析系统
+开发者：熊猫大侠
+版本：v2.1.0
+许可证：MIT License
+"""
+# industry_api_endpoints.py
+# 预留接口

app/web/static/css/theme.css

@@ -0,0 +1,354 @@
+:root {
+    --bg-color: #f8f9fa;
+    --text-color: #212529;
+    --card-bg-color: #ffffff;
+    --border-color: #dee2e6;
+    --primary-color: #0d6efd;
+    --sidebar-bg-color: #343a40;
+    --sidebar-text-color: #ced4da;
+    --sidebar-hover-bg-color: rgba(255, 255, 255, 0.1);
+    --sidebar-active-bg-color: rgba(255, 255, 255, 0.2);
+    --link-color: #0d6efd;
+    --link-hover-color: #0a58ca;
+}
+
+html[data-theme='dark'] {
+    --bg-color: #1c1c1e;
+    --text-color: #e0e0e0;
+    --card-bg-color: #2c2c2e;
+    --border-color: #444444;
+    --primary-color: #4da6ff;
+    --sidebar-bg-color: #232325;
+    --sidebar-text-color: #b0b0b0;
+    --sidebar-hover-bg-color: rgba(255, 255, 255, 0.08);
+    --sidebar-active-bg-color: rgba(255, 255, 255, 0.15);
+    --link-color: #4da6ff;
+    --link-hover-color: #87c3ff;
+    --navbar-bg-color: #343a40;
+    --btn-secondary-bg: #495057;
+    --btn-secondary-color: #f8f9fa;
+}
+
+body {
+    background-color: var(--bg-color);
+    color: var(--text-color);
+}
+
+.card {
+    background-color: var(--card-bg-color);
+    border-color: var(--border-color);
+}
+
+.card-header {
+    background-color: var(--card-bg-color);
+    border-bottom: 1px solid var(--border-color);
+}
+
+.table {
+    --bs-table-bg: var(--card-bg-color);
+    --bs-table-border-color: var(--border-color);
+    --bs-table-color: var(--text-color);
+    --bs-table-striped-bg: rgba(0, 0, 0, 0.02);
+}
+
+html[data-theme='dark'] .table-striped > tbody > tr:nth-of-type(odd) > * {
+    --bs-table-accent-bg: rgba(255, 255, 255, 0.03);
+}
+
+.form-control, .form-select {
+    background-color: var(--bg-color);
+    color: var(--text-color);
+    border-color: var(--border-color);
+}
+
+.form-control:focus, .form-select:focus {
+    background-color: var(--bg-color);
+    color: var(--text-color);
+}
+
+.input-group-text {
+    background-color: var(--bg-color);
+    color: var(--text-color);
+    border-color: var(--border-color);
+}
+
+.sidebar {
+    background-color: var(--sidebar-bg-color);
+}
+
+.sidebar .nav-link {
+    color: var(--sidebar-text-color);
+}
+
+.sidebar .nav-link:hover {
+    background-color: var(--sidebar-hover-bg-color);
+    color: #fff;
+}
+
+.sidebar .nav-link.active {
+    background-color: var(--sidebar-active-bg-color);
+     color: #fff;
+}
+
+html[data-theme='dark'] .navbar.bg-primary {
+    background-color: var(--navbar-bg-color) !important;
+}
+
+html[data-theme='dark'] .btn-light {
+    background-color: var(--btn-secondary-bg);
+    color: var(--btn-secondary-color);
+    border-color: var(--border-color);
+}
+
+html[data-theme='dark'] .form-control {
+    background-color: var(--card-bg-color) !important;
+    color: var(--text-color) !important;
+    border-color: var(--border-color) !important;
+}
+
+html[data-theme='dark'] .form-control::placeholder {
+  color: #6c757d;
+}
+
+html[data-theme='dark'] .alert-success {
+    background-color: #1a3e29;
+    color: #a3d9b8;
+    border-color: #2c6846;
+}
+
+html[data-theme='dark'] .alert-danger {
+    background-color: #4f2125;
+    color: #f5c2c7;
+    border-color: #8d3d44;
+}
+
+html[data-theme='dark'] .alert-info {
+    background-color: #1c3a4f;
+    color: #b8d4e9;
+    border-color: #2f658b;
+}
+
+html[data-theme='dark'] .badge.bg-light {
+    background-color: var(--btn-secondary-bg) !important;
+    color: var(--btn-secondary-color) !important;
+}
+
+html[data-theme='dark'] #loading-overlay {
+    background-color: rgba(0, 0, 0, 0.7);
+}
+
+html[data-theme='dark'] .modal-content {
+    background-color: var(--card-bg-color);
+    color: var(--text-color);
+}
+
+html[data-theme='dark'] .modal-header {
+    border-bottom-color: var(--border-color);
+}
+
+html[data-theme='dark'] .modal-footer {
+    border-top-color: var(--border-color);
+}
+
+html[data-theme='dark'] .btn-close {
+    filter: invert(1) grayscale(100%) brightness(200%);
+}
+
+
+/* Dark mode styles for finance portal */
+html[data-theme='dark'] .finance-portal-container {
+    background-color: var(--bg-color);
+}
+
+html[data-theme='dark'] .portal-sidebar,
+html[data-theme='dark'] .portal-news,
+html[data-theme='dark'] .portal-hotspot,
+html[data-theme='dark'] .portal-footer {
+    background-color: var(--card-bg-color);
+    box-shadow: 0 2px 5px rgba(0,0,0,0.3);
+}
+
+html[data-theme='dark'] .sidebar-header,
+html[data-theme='dark'] .news-header,
+html[data-theme='dark'] .hotspot-header {
+    border-bottom-color: var(--border-color);
+    background-color: var(--card-bg-color);
+}
+
+html[data-theme='dark'] .sidebar-header h5,
+html[data-theme='dark'] .news-header h5,
+html[data-theme='dark'] .hotspot-header h5,
+html[data-theme='dark'] .hotspot-title,
+html[data-theme='dark'] .sidebar-nav a,
+html[data-theme='dark'] .group-title,
+html[data-theme='dark'] .status-item,
+html[data-theme='dark'] .current-time,
+html[data-theme='dark'] .news-content,
+html[data-theme='dark'] .ticker-item,
+html[data-theme='dark'] .time-label,
+html[data-theme='dark'] .time-date {
+    color: var(--text-color);
+}
+
+html[data-theme='dark'] .sidebar-nav a:hover {
+    background-color: var(--sidebar-hover-bg-color);
+}
+
+html[data-theme='dark'] .time-point:before {
+    background-color: var(--primary-color);
+}
+
+html[data-theme='dark'] .time-point:after {
+    background-color: var(--border-color);
+}
+
+html[data-theme='dark'] .news-items {
+    background-color: #3a3a3c;
+}
+
+html[data-theme='dark'] .news-item,
+html[data-theme='dark'] .hotspot-item,
+html[data-theme='dark'] .market-status {
+    border-bottom-color: var(--border-color);
+}
+
+html[data-theme='dark'] .hotspot-rank {
+    background-color: #495057;
+    color: var(--text-color);
+}
+
+html[data-theme='dark'] .hotspot-rank.rank-top {
+    background-color: #fb6340;
+    color: #fff;
+}
+
+html[data-theme='dark'] .ticker-news {
+    background-color: #3a3a3c;
+}
+
+/* Dark mode fixes for custom styles */
+html[data-theme='dark'] .analysis-para,
+html[data-theme='dark'] .analysis-para strong,
+html[data-theme='dark'] .analysis-para b {
+    color: var(--text-color);
+}
+
+html[data-theme='dark'] .keyword {
+    color: #58a6ff; /* Lighter blue */
+}
+
+html[data-theme='dark'] .term {
+    color: #ff85b3; /* Lighter pink */
+}
+
+html[data-theme='dark'] .price {
+    color: #7ee787; /* Lighter green */
+    background: #2d332d;
+}
+
+html[data-theme='dark'] .date {
+    color: #8b949e; /* Lighter grey */
+}
+
+/* Ensure card headers and titles are readable */
+html[data-theme='dark'] .card-header,
+html[data-theme='dark'] .card-title,
+html[data-theme='dark'] h1,
+html[data-theme='dark'] h2,
+html[data-theme='dark'] h3,
+html[data-theme='dark'] h4,
+html[data-theme='dark'] h5,
+html[data-theme='dark'] h6,
+html[data-theme='dark'] .text-dark,
+html[data-theme='dark'] [class*="text-"] {
+    color: var(--text-color) !important;
+}
+
+html[data-theme='dark'] .text-muted {
+    color: #8b949e !important;
+}
+
+
+/* ApexCharts dark theme overrides */
+html[data-theme='dark'] .apexcharts-tooltip,
+html[data-theme='dark'] .apexcharts-xaxistooltip,
+html[data-theme='dark'] .apexcharts-yaxistooltip {
+    background: #3c3c3e !important;
+    border-color: #545456 !important;
+    color: var(--text-color) !important;
+}
+
+html[data-theme='dark'] .apexcharts-tooltip-title {
+    background: #4a4a4c !important;
+    border-bottom-color: #545456 !important;
+    color: var(--text-color) !important;
+}
+
+html[data-theme='dark'] .apexcharts-text,
+html[data-theme='dark'] .apexcharts-xaxis-label,
+html[data-theme='dark'] .apexcharts-yaxis-label,
+html[data-theme='dark'] .apexcharts-datalabel {
+    fill: var(--text-color);
+}
+html[data-theme='dark'] .apexcharts-radar-series path {
+    stroke-opacity: 0.5;
+}
+html[data-theme='dark'] .apexcharts-radar-series .apexcharts-datalabels text {
+    fill: #ffffff !important;
+    font-weight: bold;
+}
+
+/* --- MORE DARK MODE FIXES --- */
+
+/* Brighter navbar text */
+html[data-theme='dark'] .navbar-dark .navbar-nav .nav-link,
+html[data-theme='dark'] .navbar-dark .navbar-brand {
+    color: var(--text-color);
+}
+
+/* General text inside cards */
+html[data-theme='dark'] .card-body,
+html[data-theme='dark'] .card-title,
+html[data-theme='dark'] .card-subtitle,
+html[data-theme='dark'] .card-text,
+html[data-theme='dark'] .list-group-item {
+    color: var(--text-color) !important;
+}
+
+html[data-theme='dark'] .list-group-item {
+     background-color: var(--card-bg-color);
+     border-color: var(--border-color);
+}
+
+/* Fix for colored text like stock price changes */
+html[data-theme='dark'] .text-success {
+    color: #2dce89 !important;
+}
+
+html[data-theme='dark'] .text-danger {
+    color: #f5365c !important;
+}
+
+/* Specific fix for the main price display */
+html[data-theme='dark'] h2.text-success,
+html[data-theme='dark'] .display-4 {
+     color: #2dce89 !important;
+}
+
+/* Labels in cards (e.g., "今开", "最高") */
+html[data-theme='dark'] .small, html[data-theme='dark'] small {
+    color: #8b949e !important;
+}
+
+/* Make AI analysis keywords even brighter */
+html[data-theme='dark'] .keyword {
+    color: #79c0ff;
+}
+html[data-theme='dark'] .price {
+    color: #8eff97;
+    background: #223c24;
+}
+
+
+
+

app/web/static/swagger.json

@@ -0,0 +1,573 @@
+{
+  "swagger": "2.0",
+  "info": {
+    "title": "股票智能分析系统 API",
+    "description": "股票智能分析系统的REST API文档",
+    "version": "2.1.0"
+  },
+  "host": "localhost:8888",
+  "basePath": "/",
+  "schemes": ["http", "https"],
+  "paths": {
+    "/analyze": {
+      "post": {
+        "summary": "分析股票",
+        "description": "分析单只或多只股票",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_codes": {
+                  "type": "array",
+                  "items": {
+                    "type": "string"
+                  },
+                  "example": ["600519", "000858"]
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功分析股票"
+          },
+          "400": {
+            "description": "请求参数错误"
+          },
+          "500": {
+            "description": "服务器内部错误"
+          }
+        }
+      }
+    },
+    "/api/start_stock_analysis": {
+      "post": {
+        "summary": "启动股票分析任务",
+        "description": "启动异步股票分析任务",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功启动分析任务"
+          }
+        }
+      }
+    },
+"/api/analysis_status/{task_id}": {
+      "get": {
+        "summary": "获取分析任务状态",
+        "description": "获取异步分析任务的状态和结果",
+        "parameters": [
+          {
+            "name": "task_id",
+            "in": "path",
+            "required": true,
+            "type": "string"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取任务状态和结果"
+          },
+          "404": {
+            "description": "找不到指定的任务"
+          }
+        }
+      }
+    },
+    "/api/stock_data": {
+      "get": {
+        "summary": "获取股票数据",
+        "description": "获取股票历史数据和技术指标",
+        "parameters": [
+          {
+            "name": "stock_code",
+            "in": "query",
+            "required": true,
+            "type": "string"
+          },
+          {
+            "name": "market_type",
+            "in": "query",
+            "required": false,
+            "type": "string",
+            "default": "A"
+          },
+          {
+            "name": "period",
+            "in": "query",
+            "required": false,
+            "type": "string",
+            "enum": ["1m", "3m", "6m", "1y"],
+            "default": "1y"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取股票数据"
+          },
+          "400": {
+            "description": "请求参数错误"
+          },
+          "500": {
+            "description": "服务器内部错误"
+          }
+        }
+      }
+    },
+    "/api/start_market_scan": {
+      "post": {
+        "summary": "启动市场扫描任务",
+        "description": "启动异步市场扫描任务",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_list": {
+                  "type": "array",
+                  "items": {
+                    "type": "string"
+                  },
+                  "example": ["600519", "000858"]
+                },
+                "min_score": {
+                  "type": "integer",
+                  "example": 60
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功启动扫描任务"
+          }
+        }
+      }
+    },
+    "/api/scan_status/{task_id}": {
+      "get": {
+        "summary": "获取扫描任务状态",
+        "description": "获取异步扫描任务的状态和结果",
+        "parameters": [
+          {
+            "name": "task_id",
+            "in": "path",
+            "required": true,
+            "type": "string"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取任务状态和结果"
+          },
+          "404": {
+            "description": "找不到指定的任务"
+          }
+        }
+      }
+    },
+    "/api/index_stocks": {
+      "get": {
+        "summary": "获取指数成分股",
+        "description": "获取指定指数的成分股列表",
+        "parameters": [
+          {
+            "name": "index_code",
+            "in": "query",
+            "required": true,
+            "type": "string",
+            "example": "000300"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取指数成分股"
+          },
+          "400": {
+            "description": "请求参数错误"
+          },
+          "500": {
+            "description": "服务器内部错误"
+          }
+        }
+      }
+    },
+    "/api/industry_stocks": {
+      "get": {
+        "summary": "获取行业成分股",
+        "description": "获取指定行业的成分股列表",
+        "parameters": [
+          {
+            "name": "industry",
+            "in": "query",
+            "required": true,
+            "type": "string",
+            "example": "银行"
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取行业成分股"
+          },
+          "400": {
+            "description": "请求参数错误"
+          },
+          "500": {
+            "description": "服务器内部错误"
+          }
+        }
+      }
+    },
+    "/api/fundamental_analysis": {
+      "post": {
+        "summary": "基本面分析",
+        "description": "获取股票的基本面分析结果",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取基本面分析结果"
+          }
+        }
+      }
+    },
+    "/api/capital_flow": {
+      "post": {
+        "summary": "资金流向分析",
+        "description": "获取股票的资金流向分析结果",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                },
+                "days": {
+                  "type": "integer",
+                  "example": 10
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取资金流向分析结果"
+          }
+        }
+      }
+    },
+    "/api/scenario_predict": {
+      "post": {
+        "summary": "情景预测",
+        "description": "获取股票的多情景预测结果",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                },
+                "days": {
+                  "type": "integer",
+                  "example": 60
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取情景预测结果"
+          }
+        }
+      }
+    },
+    "/api/qa": {
+      "post": {
+        "summary": "智能问答",
+        "description": "获取股票相关问题的智能回答",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                },
+                "question": {
+                  "type": "string",
+                  "example": "这只股票的主要支撑位是多少？"
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取智能回答"
+          }
+        }
+      }
+    },
+    "/api/risk_analysis": {
+      "post": {
+        "summary": "风险分析",
+        "description": "获取股票的风险分析结果",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "stock_code": {
+                  "type": "string",
+                  "example": "600519"
+                },
+                "market_type": {
+                  "type": "string",
+                  "example": "A"
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取风险分析结果"
+          }
+        }
+      }
+    },
+    "/api/portfolio_risk": {
+      "post": {
+        "summary": "投资组合风险分析",
+        "description": "获取投资组合的整体风险分析结果",
+        "parameters": [
+          {
+            "name": "body",
+            "in": "body",
+            "required": true,
+            "schema": {
+              "type": "object",
+              "properties": {
+                "portfolio": {
+                  "type": "array",
+                  "items": {
+                    "type": "object",
+                    "properties": {
+                      "stock_code": {
+                        "type": "string"
+                      },
+                      "weight": {
+                        "type": "number"
+                      },
+                      "market_type": {
+                        "type": "string"
+                      }
+                    }
+                  },
+                  "example": [
+                    {
+                      "stock_code": "600519",
+                      "weight": 30,
+                      "market_type": "A"
+                    },
+                    {
+                      "stock_code": "000858",
+                      "weight": 20,
+                      "market_type": "A"
+                    }
+                  ]
+                }
+              }
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取投资组合风险分析结果"
+          }
+        }
+      }
+    },
+    "/api/index_analysis": {
+      "get": {
+        "summary": "指数分析",
+        "description": "获取指数的整体分析结果",
+        "parameters": [
+          {
+            "name": "index_code",
+            "in": "query",
+            "required": true,
+            "type": "string",
+            "example": "000300"
+          },
+          {
+            "name": "limit",
+            "in": "query",
+            "required": false,
+            "type": "integer",
+            "example": 30
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取指数分析结果"
+          }
+        }
+      }
+    },
+    "/api/industry_analysis": {
+      "get": {
+        "summary": "行业分析",
+        "description": "获取行业的整体分析结果",
+        "parameters": [
+          {
+            "name": "industry",
+            "in": "query",
+            "required": true,
+            "type": "string",
+            "example": "银行"
+          },
+          {
+            "name": "limit",
+            "in": "query",
+            "required": false,
+            "type": "integer",
+            "example": 30
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取行业分析结果"
+          }
+        }
+      }
+    },
+    "/api/industry_compare": {
+      "get": {
+        "summary": "行业比较",
+        "description": "比较不同行业的表现",
+        "parameters": [
+          {
+            "name": "limit",
+            "in": "query",
+            "required": false,
+            "type": "integer",
+            "example": 10
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "成功获取行业比较结果"
+          }
+        }
+      }
+    }
+  },
+  "definitions": {
+    "Stock": {
+      "type": "object",
+      "properties": {
+        "stock_code": {
+          "type": "string"
+        },
+        "stock_name": {
+          "type": "string"
+        },
+        "price": {
+          "type": "number"
+        },
+        "price_change": {
+          "type": "number"
+        }
+      }
+    },
+    "AnalysisResult": {
+      "type": "object",
+      "properties": {
+        "score": {
+          "type": "number"
+        },
+        "recommendation": {
+          "type": "string"
+        }
+      }
+    }
+  }
+}

app/web/templates/agent_analysis.html

@@ -0,0 +1,703 @@
+{% extends "layout.html" %}
+
+{% block head %}
+<style>
+    /* 修复“开始分析”按钮中加载动画和文字的对齐问题 */
+    #start-analysis-btn {
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        gap: 0.5rem; /* 在图标/加载动画和文字之间添加一些间距 */
+    }
+
+    /* 为市场下拉菜单应用自定义样式 */
+    #market-type.form-select {
+        -webkit-appearance: none;
+        -moz-appearance: none;
+        appearance: none;
+        background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill='none' stroke='%23343a40' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/%3e%3c/svg%3e");
+        background-repeat: no-repeat;
+        background-position: right 0.75rem center;
+        background-size: 16px 12px;
+    }
+</style>
+{% endblock %}
+
+{% block title %}智能体分析 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+
+    <!-- Page Title -->
+    <div class="row mb-3">
+        <div class="col-12">
+            <h2 class="mb-0">智能体分析</h2>
+            <p class="text-muted">利用多智能体框架进行深度股票评估</p>
+        </div>
+    </div>
+
+    <!-- Input Form Card -->
+    <div class="row">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">分析设置</h5>
+                </div>
+                <div class="card-body py-2">
+                    <form id="agent-analysis-form" class="row g-3 align-items-end">
+                        <div class="col-md-3">
+                            <label for="stock-code" class="form-label">股票代码</label>
+                            <div class="input-group">
+                                <span class="input-group-text"><i class="fas fa-barcode"></i></span>
+                                <input type="text" class="form-control" id="stock-code" placeholder="例如: 600519, AAPL" required>
+                            </div>
+                        </div>
+                        <div class="col-md-2">
+                            <label for="market-type" class="form-label">市场</label>
+                            <div class="input-group">
+                                <select class="form-select" id="market-type">
+                                    <option value="A" selected>A股</option>
+                                    <option value="US">美股</option>
+                                    <option value="HK">港股</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-2">
+                           <label for="analysis-date" class="form-label">分析日期</label>
+                           <div class="input-group">
+                               <input type="date" class="form-control" id="analysis-date">
+                           </div>
+                       </div>
+                        <div class="col-md-3">
+                            <label for="research-depth" class="form-label">研究深度</label>
+                            <select class="form-select" id="research-depth">
+                                <option value="1">1级 - 快速</option>
+                                <option value="2">2级 - 基础</option>
+                                <option value="3" selected>3级 - 标准</option>
+                                <option value="4">4级 - 深度</option>
+                                <option value="5">5级 - 全面</option>
+                            </select>
+                        </div>
+                        <div class="col-md-2">
+                            <button type="submit" id="start-analysis-btn" class="btn btn-primary w-100">
+                                <i class="fas fa-rocket"></i> 开始分析
+                            </button>
+                        </div>
+
+                        <div class="col-12 mt-2">
+                            <label class="form-label mb-1">选择分析师团队:</label>
+                            <div id="analyst-selection" class="d-flex flex-wrap gap-3">
+                                <div class="form-check form-check-inline">
+                                    <input class="form-check-input" type="checkbox" id="analyst-market" value="market" checked>
+                                    <label class="form-check-label" for="analyst-market">市场</label>
+                                </div>
+                                <div class="form-check form-check-inline">
+                                    <input class="form-check-input" type="checkbox" id="analyst-news" value="news" checked>
+                                    <label class="form-check-label" for="analyst-news">新闻</label>
+                                </div>
+                                <div class="form-check form-check-inline">
+                                    <input class="form-check-input" type="checkbox" id="analyst-social" value="social" checked>
+                                    <label class="form-check-label" for="analyst-social">社交</label>
+                                </div>
+                                <div class="form-check form-check-inline">
+                                    <input class="form-check-input" type="checkbox" id="analyst-fundamentals" value="fundamentals" checked>
+                                    <label class="form-check-label" for="analyst-fundamentals">基本面</label>
+                                </div>
+                            </div>
+                        </div>
+
+                        <div class="col-md-9 mt-2">
+                             <label for="max-output-length" class="form-label">最大输出长度: <span id="max-output-length-value" class="fw-bold text-primary">2048</span> tokens</label>
+                             <input type="range" class="form-range" id="max-output-length" min="512" max="8192" step="256" value="2048">
+                        </div>
+                        <div class="col-md-3 mt-2 d-flex align-items-end justify-content-end">
+                            <div class="form-check form-switch pb-2">
+                                <input class="form-check-input" type="checkbox" id="enable-memory" checked>
+                                <label class="form-check-label" for="enable-memory">启用记忆功能</label>
+                            </div>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Results Area -->
+    <div id="results-area" class="mt-4" style="display: none;">
+        <!-- Progress Bar -->
+        <div id="progress-container" class="mb-3">
+            <h5 id="progress-step" class="text-center mb-2">正在初始化...</h5>
+            <div class="progress" style="height: 20px;">
+                <div id="progress-bar" class="progress-bar progress-bar-striped progress-bar-animated" role="progressbar" style="width: 0%;" aria-valuenow="0" aria-valuemin="0" aria-valuemax="100">0%</div>
+            </div>
+        </div>
+
+        <!-- Analysis Results Card -->
+        <div id="results-card" class="card" style="display: none;">
+            <div class="card-header d-flex justify-content-between align-items-center">
+                <h5 class="mb-0">分析报告</h5>
+                <div id="export-buttons">
+                    <!-- Export buttons will be added here -->
+                </div>
+            </div>
+            <div class="card-body">
+                <div id="results-content">
+                    <!-- Dynamic results will be rendered here -->
+                </div>
+            </div>
+        </div>
+    </div>
+    <!-- History Card -->
+    <div class="row mt-4">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header d-flex justify-content-between align-items-center py-2">
+                    <h5 class="mb-0"><i class="fas fa-history me-2"></i>分析历史</h5>
+                    <button id="refresh-history-btn" class="btn btn-sm btn-outline-primary">
+                        <i class="fas fa-sync-alt"></i> 刷新
+                    </button>
+                </div>
+                <div class="card-body">
+                    <div id="history-table-container">
+                        <!-- History table will be rendered here -->
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+$(document).ready(function() {
+    let pollingInterval;
+
+    $('#agent-analysis-form').submit(function(e) {
+        e.preventDefault();
+        const stockCode = $('#stock-code').val().trim();
+        const researchDepth = $('#research-depth').val();
+        const marketType = $('#market-type').val();
+        const analysisDate = $('#analysis-date').val();
+        const enableMemory = $('#enable-memory').is(':checked');
+        const maxOutputLength = $('#max-output-length').val();
+        const selectedAnalysts = $('#analyst-selection input:checked').map(function() {
+            return $(this).val();
+        }).get();
+        
+        if (!stockCode) {
+            showError('请输入股票代码！');
+            return;
+        }
+
+        if (selectedAnalysts.length === 0) {
+            showError('请至少选择一个分析师！');
+            return;
+        }
+
+        // Disable button and show progress
+        $('#start-analysis-btn').prop('disabled', true).html('<span class="spinner-border spinner-border-sm" role="status" aria-hidden="true"></span> 分析中...');
+        $('#results-area').show();
+        $('#progress-container').show();
+        $('#results-card').hide();
+        updateProgress(0, '任务已提交...');
+
+        // Start analysis
+        $.ajax({
+            url: '/api/start_agent_analysis',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_code: stockCode,
+                market_type: marketType,
+                selected_analysts: selectedAnalysts,
+                research_depth: parseInt(researchDepth),
+                analysis_date: analysisDate,
+                enable_memory: enableMemory,
+                max_output_length: parseInt(maxOutputLength)
+            }),
+            success: function(response) {
+                if (response.task_id) {
+                    startPolling(response.task_id);
+                } else {
+                    showError(response.error || '无法启动分析任务');
+                    resetForm();
+                }
+            },
+            error: function(xhr) {
+                const errorMsg = xhr.responseJSON ? xhr.responseJSON.error : '请求失败';
+                showError('启动分析失败: ' + errorMsg);
+                resetForm();
+            }
+        });
+    });
+
+    function startPolling(taskId) {
+        pollingInterval = setInterval(function() {
+            $.ajax({
+                url: `/api/agent_analysis_status/${taskId}`,
+                type: 'GET',
+                cache: false, // 禁用缓存，确保每次都获取最新状态
+                success: function(response) {
+                    if (!response) return;
+
+                    // 优先使用 result.current_step，如果没有则使用默认文本
+                    const currentStep = response.result ? response.result.current_step : '处理中...';
+                    updateProgress(response.progress, currentStep);
+
+                    if (response.status === 'completed' || response.status === 'failed') {
+                        clearInterval(pollingInterval);
+                        if (response.status === 'completed') {
+                            displayResults(response.result);
+                        } else {
+                            showError(response.error || '分析任务失败');
+                        }
+                        resetForm();
+                    }
+                },
+                error: function(xhr) {
+                    clearInterval(pollingInterval);
+                    const errorMsg = xhr.responseJSON ? xhr.responseJSON.error : '请求失败';
+                    showError('获取状态失败: ' + errorMsg);
+                    resetForm();
+                }
+            });
+        }, 3000); // Poll every 3 seconds
+    }
+
+    function updateProgress(percent, step) {
+        $('#alerts-container').empty(); // 清除“正在恢复”等提示信息
+        percent = Math.min(percent, 100);
+        $('#progress-bar').css('width', percent + '%').attr('aria-valuenow', percent).text(percent + '%');
+        $('#progress-step').text(step);
+    }
+
+    function displayResults(result) {
+        $('#progress-container').hide();
+        $('#results-card').show();
+        
+        let contentHtml = '<h4><i class="fas fa-exclamation-triangle text-danger me-2"></i>分析结果加载失败</h4>';
+
+        if (result && result.decision) {
+            const decision = result.decision;
+            const state = result.final_state || {};
+            const stockInfo = {
+                code: state.company_of_interest || 'N/A',
+                name: state.company_name || '未知公司',
+                market: state.market_type || 'N/A'
+            };
+
+            // --- Helper functions ---
+            const parseFundamentals = (report) => {
+                if (typeof report !== 'string') {
+                    return {};
+                }
+                const metrics = {};
+                const patterns = {
+                    debt_ratio: /\*\*?资产负债率\*\*?\s*[:：\s是为]*\s*([\d\.]+)/i,
+                    liquidity_ratio: /\*\*?流动比率\*\*?\s*[:：\s是为]*\s*([\d\.]+)/i,
+                    quick_ratio: /\*\*?速动比率\*\*?\s*[:：\s是为]*\s*([\d\.]+)/i,
+                    gross_margin: /\*\*?毛利率\*\*?\s*[:：\s是为]*\s*([\d\.]+)/i,
+                    net_margin: /\*\*?净利率\*\*?\s*[:：\s是为]*\s*([\d\.]+)/i,
+                    pb_ratio: /\*\*?市净率(?:\(PB\))?\*\*?\s*[:：\s是为]*\s*([\d\.]+)/i
+                };
+
+                for (const key in patterns) {
+                    const match = report.match(patterns[key]);
+                    if (match && match[1]) {
+                        metrics[key] = parseFloat(match[1]);
+                    } else {
+                        metrics[key] = null;
+                    }
+                }
+                return metrics;
+            };
+            
+            const fundamentals = parseFundamentals(state.fundamentals_report);
+
+            const getRatingClass = (value, thresholds, ascending = true) => {
+                if (value === null || value === undefined || isNaN(value)) return 'text-muted';
+                const [low, mid, high] = thresholds;
+                if (ascending) {
+                    if (value >= high) return 'text-success';
+                    if (value >= mid) return 'text-primary';
+                    if (value >= low) return 'text-warning';
+                    return 'text-danger';
+                } else {
+                    if (value <= low) return 'text-success';
+                    if (value <= mid) return 'text-primary';
+                    if (value <= high) return 'text-warning';
+                    return 'text-danger';
+                }
+            };
+
+            const getRatingText = (value, thresholds, labels, ascending = true) => {
+                if (value === null || value === undefined || isNaN(value)) return 'N/A';
+                const [low, mid, high] = thresholds;
+                const [labelLow, labelMid, labelHigh, labelVeryHigh] = labels;
+                if (ascending) {
+                    if (value >= high) return labelVeryHigh;
+                    if (value >= mid) return labelHigh;
+                    if (value >= low) return labelMid;
+                    return labelLow;
+                } else {
+                    if (value <= low) return labelVeryHigh;
+                    if (value <= mid) return labelHigh;
+                    if (value <= high) return labelMid;
+                    return labelLow;
+                }
+            };
+
+            const renderTable = (title, headers, rows) => {
+                let tableHtml = `<div class="card h-100 shadow-sm">
+                    <div class="card-header bg-light">
+                        <h6 class="mb-0">${title}</h6>
+                    </div>
+                    <div class="card-body p-0">
+                        <table class="table table-sm table-hover mb-0">
+                            <thead><tr>${headers.map(h => `<th>${h}</th>`).join('')}</tr></thead>
+                            <tbody>`;
+                rows.forEach(row => {
+                    tableHtml += `<tr>${row.map(cell => `<td>${cell}</td>`).join('')}</tr>`;
+                });
+                tableHtml += `</tbody></table></div></div>`;
+                return tableHtml;
+            };
+
+            // --- Main Content ---
+            contentHtml = `
+                <!-- Header -->
+                <div class="text-center mb-4">
+                    <h2>${stockInfo.name} (${stockInfo.code}) - 智能体深度分析报告</h2>
+                    <p class="text-muted">报告生成日期: ${new Date().toLocaleDateString()}</p>
+                </div>
+
+                <!-- Investment Suggestion -->
+                <div class="alert ${getDecisionClass(decision.action).replace('text-', 'alert-')} border-0 shadow-lg mb-4">
+                    <h4 class="alert-heading"><i class="fas fa-lightbulb me-2"></i>投资建议: <span class="fw-bold">${decision.action}</span></h4>
+                    <p class="mb-0">${decision.reasoning}</p>
+                </div>
+
+                <!-- Key Metrics -->
+                <div class="row mb-4">
+                    <div class="col-md-4">
+                        <div class="card text-center h-100 shadow-sm">
+                            <div class="card-body">
+                                <h6 class="text-muted">置信度</h6>
+                                <p class="display-5 fw-bold mb-0">${(decision.confidence * 100).toFixed(1)}%</p>
+                            </div>
+                        </div>
+                    </div>
+                    <div class="col-md-4">
+                        <div class="card text-center h-100 shadow-sm">
+                            <div class="card-body">
+                                <h6 class="text-muted">风险评分</h6>
+                                <p class="display-5 fw-bold mb-0 ${getRatingClass(decision.risk_score * 100, [30, 50, 70], false)}">${(decision.risk_score * 100).toFixed(1)}%</p>
+                            </div>
+                        </div>
+                    </div>
+                    <div class="col-md-4">
+                        <div class="card text-center h-100 shadow-sm">
+                            <div class="card-body">
+                                <h6 class="text-muted">市场情绪</h6>
+                                <p class="display-5 fw-bold mb-0">${state.sentiment_score || 'N/A'}</p>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- Financial, Profitability & Valuation Tables -->
+                <div class="row g-4 mb-4">
+                    <div class="col-lg-6">
+                        ${renderTable(
+                            '<i class="fas fa-balance-scale me-2"></i>财务状况评估',
+                            ['指标', '数值', '评估'],
+                            [
+                                ['资产负债率', fundamentals.debt_ratio !== null ? `${fundamentals.debt_ratio}%` : 'N/A', `<span class="${getRatingClass(fundamentals.debt_ratio, [40, 60, 80], false)}">${getRatingText(fundamentals.debt_ratio, [40, 60, 80], ['高风险', '较高风险', '中等', '良好'], false)}</span>`],
+                                ['流动比率', fundamentals.liquidity_ratio !== null ? fundamentals.liquidity_ratio : 'N/A', `<span class="${getRatingClass(fundamentals.liquidity_ratio, [1, 1.5, 2], true)}">${getRatingText(fundamentals.liquidity_ratio, [1, 1.5, 2], ['风险', '吃紧', '良好', '优秀'], true)}</span>`],
+                                ['速动比率', fundamentals.quick_ratio !== null ? fundamentals.quick_ratio : 'N/A', `<span class="${getRatingClass(fundamentals.quick_ratio, [0.8, 1, 1.2], true)}">${getRatingText(fundamentals.quick_ratio, [0.8, 1, 1.2], ['危险', '风险', '尚可', '良好'], true)}</span>`]
+                            ]
+                        )}
+                    </div>
+                    <div class="col-lg-6">
+                        ${renderTable(
+                            '<i class="fas fa-chart-line me-2"></i>盈利与估值',
+                            ['指标', '数值', '评估'],
+                            [
+                                ['毛利率', fundamentals.gross_margin !== null ? `${fundamentals.gross_margin}%` : 'N/A', `<span class="${getRatingClass(fundamentals.gross_margin, [10, 20, 30], true)}">${getRatingText(fundamentals.gross_margin, [10, 20, 30], ['极低', '偏低', '中等', '优秀'], true)}</span>`],
+                                ['净利率', fundamentals.net_margin !== null ? `${fundamentals.net_margin}%` : 'N/A', `<span class="${getRatingClass(fundamentals.net_margin, [0, 5, 10], true)}">${getRatingText(fundamentals.net_margin, [0, 5, 10], ['亏损', '微利', '良好', '优秀'], true)}</span>`],
+                                ['市净率(PB)', fundamentals.pb_ratio !== null ? fundamentals.pb_ratio : 'N/A', `<span class="${getRatingClass(fundamentals.pb_ratio, [1, 2, 4], false)}">${getRatingText(fundamentals.pb_ratio, [1, 2, 4], ['高估', '偏高', '合理', '低估'], false)}</span>`]
+                            ]
+                        )}
+                    </div>
+                </div>
+
+                <!-- Detailed Reports Accordion -->
+                <h3 class="mt-5 mb-3">详细分析报告</h3>
+                <div class="accordion" id="reports-accordion">
+            `;
+
+            const reports = [
+                { title: '<i class="fas fa-chart-pie me-2"></i>市场分析报告', content: state.market_report, id: 'market' },
+                { title: '<i class="fas fa-comments me-2"></i>社交媒体情绪报告', content: state.sentiment_report, id: 'social' },
+                { title: '<i class="far fa-newspaper me-2"></i>新闻分析报告', content: state.news_report, id: 'news' },
+                { title: '<i class="fas fa-file-invoice-dollar me-2"></i>基本面分析报告', content: state.fundamentals_report, id: 'fundamentals' },
+                { title: '<i class="fas fa-gavel me-2"></i>投资辩论总结', content: state.investment_debate_state?.judge_decision, id: 'invest_debate' },
+                { title: '<i class="fas fa-exclamation-triangle me-2"></i>风险分析总结', content: state.risk_debate_state?.judge_decision, id: 'risk_debate' }
+            ];
+
+            reports.forEach((report, index) => {
+                if (report.content && (typeof report.content === 'string' ? report.content.trim() !== '' : true)) {
+                    let reportContent;
+                    if (typeof report.content === 'object') {
+                        reportContent = `<pre>${JSON.stringify(report.content, null, 2)}</pre>`;
+                    } else if (typeof report.content === 'string') {
+                        // 使用Marked.js渲染Markdown
+                        reportContent = marked.parse(report.content);
+                    } else {
+                        reportContent = '<p>内容格式无法解析。</p>';
+                    }
+
+                    contentHtml += `
+                        <div class="accordion-item">
+                            <h2 class="accordion-header" id="heading-${report.id}">
+                                <button class="accordion-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapse-${report.id}" aria-expanded="false" aria-controls="collapse-${report.id}">
+                                    <strong>${report.title}</strong>
+                                </button>
+                            </h2>
+                            <div id="collapse-${report.id}" class="accordion-collapse collapse" aria-labelledby="heading-${report.id}" data-bs-parent="#reports-accordion">
+                                <div class="accordion-body bg-light">
+                                    ${reportContent}
+                                </div>
+                            </div>
+                        </div>
+                    `;
+                }
+            });
+
+            contentHtml += '</div>';
+        }
+        
+        $('#results-content').html(contentHtml);
+    }
+
+    function getDecisionClass(action) {
+        if (action.toLowerCase().includes('buy') || action.toLowerCase().includes('买入')) {
+            return 'text-success';
+        } else if (action.toLowerCase().includes('sell') || action.toLowerCase().includes('卖出')) {
+            return 'text-danger';
+        }
+        return 'text-warning';
+    }
+
+    function resetForm() {
+        $('#start-analysis-btn').prop('disabled', false).html('<i class="fas fa-rocket"></i> 开始分析');
+    }
+
+    // You can reuse the showError function from layout.html if it's globally available
+    function showError(message) {
+        const alertHtml = `
+            <div class="alert alert-danger alert-dismissible fade show" role="alert">
+                ${message}
+                <button type="button" class="btn-close" data-bs-dismiss="alert" aria-label="Close"></button>
+            </div>
+        `;
+        $('#alerts-container').html(alertHtml);
+    }
+
+    // Check for task_id in URL on page load to resume polling
+    const urlParams = new URLSearchParams(window.location.search);
+    const taskIdFromUrl = urlParams.get('task_id');
+    if (taskIdFromUrl) {
+        showInfo('正在恢复分析任务...');
+        $('#results-area').show();
+        $('#progress-container').show();
+        $('#results-card').hide();
+        // Fetch initial task state to get stock code
+        $.ajax({
+            url: `/api/agent_analysis_status/${taskIdFromUrl}`,
+            type: 'GET',
+            success: function(task) {
+                if (task && task.params && task.params.stock_code) {
+                    $('#stock-code').val(task.params.stock_code);
+                }
+                startPolling(taskIdFromUrl);
+            },
+            error: function() {
+                showError('无法恢复分析任务，任务ID可能已失效。');
+            }
+        });
+    }
+
+    // --- History Section Logic ---
+    function loadHistory() {
+        $('#history-table-container').html('<div class="text-center p-3"><div class="spinner-border text-primary" role="status"><span class="visually-hidden">Loading...</span></div></div>');
+        
+        $.ajax({
+            url: '/api/agent_analysis_history',
+            type: 'GET',
+            cache: false,
+            success: function(response) {
+                if (response && response.history) {
+                    renderHistoryTable(response.history);
+                } else {
+                    showError('无法加载分析历史。');
+                }
+            },
+            error: function(xhr) {
+                const errorMsg = xhr.responseJSON ? xhr.responseJSON.error : '请求失败';
+                showError('加载历史失败: ' + errorMsg);
+                $('#history-table-container').html('<div class="alert alert-danger">加载历史记录失败。</div>');
+            }
+        });
+    }
+
+    function renderHistoryTable(history) {
+        if (history.length === 0) {
+            $('#history-table-container').html('<div class="alert alert-info">没有找到任何分析历史记录。</div>');
+            return;
+        }
+
+        let tableHtml = `
+            <div class="mb-3">
+                <button id="delete-selected-btn" class="btn btn-danger" disabled>
+                    <i class="fas fa-trash-alt me-2"></i>删除选中项
+                </button>
+            </div>
+            <div class="table-responsive">
+                <table class="table table-hover table-sm align-middle">
+                    <thead>
+                        <tr>
+                            <th><input class="form-check-input" type="checkbox" id="select-all-checkbox"></th>
+                            <th>#</th>
+                            <th>股票代码</th>
+                            <th>状态</th>
+                            <th>研究深度</th>
+                            <th>分析师</th>
+                            <th>创建时间</th>
+                            <th>更新时间</th>
+                            <th class="text-end">操作</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+        `;
+
+        history.forEach((task, index) => {
+            const params = task.params || {};
+            const analysts = params.selected_analysts || [];
+            const statusBadge = getStatusBadge(task.status);
+            
+            tableHtml += `
+                <tr data-task-id="${task.id}">
+                    <td><input class="form-check-input row-checkbox" type="checkbox" value="${task.id}"></td>
+                    <td>${index + 1}</td>
+                    <td><strong>${params.stock_code || 'N/A'}</strong> <span class="badge bg-secondary">${params.market_type || ''}</span></td>
+                    <td>${statusBadge}</td>
+                    <td><span class="badge bg-info">${params.research_depth || 'N/A'}级</span></td>
+                    <td><small>${analysts.join(', ')}</small></td>
+                    <td><small>${task.created_at}</small></td>
+                    <td><small>${task.updated_at}</small></td>
+                    <td class="text-end">
+                        <a href="/agent_analysis?task_id=${task.id}" class="btn btn-sm btn-primary">
+                            <i class="fas fa-eye"></i> 查看
+                        </a>
+                        <button class="btn btn-sm btn-outline-danger delete-single-btn" data-task-id="${task.id}">
+                            <i class="fas fa-trash-alt"></i>
+                        </button>
+                    </td>
+                </tr>
+            `;
+        });
+
+        tableHtml += '</tbody></table></div>';
+        $('#history-table-container').html(tableHtml);
+    }
+
+    function getStatusBadge(status) {
+        if (status === 'completed') {
+            return '<span class="badge bg-success">已完成</span>';
+        } else if (status === 'failed') {
+            return '<span class="badge bg-danger">失败</span>';
+        }
+        return `<span class="badge bg-secondary">${status}</span>`;
+    }
+
+    $('#refresh-history-btn').click(function() {
+        loadHistory();
+    });
+
+    // Initial load of history
+    loadHistory();
+
+    // --- Deletion and Selection Logic ---
+    function performDeletion(taskIds) {
+        if (!confirm(`您确定要删除 ${taskIds.length} 个分析记录吗？此操作无法撤销。`)) {
+            return;
+        }
+
+        $.ajax({
+            url: '/api/delete_agent_analysis',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({ task_ids: taskIds }),
+            success: function(response) {
+                if (response.success) {
+                    showSuccess(response.message || '成功删除记录');
+                    loadHistory(); // Refresh the history table
+                } else {
+                    showError(response.error || '删除失败');
+                }
+            },
+            error: function(xhr) {
+                const errorMsg = xhr.responseJSON ? xhr.responseJSON.error : '请求失败';
+                showError('删除操作失败: ' + errorMsg);
+            }
+        });
+    }
+
+    // Event delegation for dynamically added elements
+    $('#history-table-container').on('click', '.delete-single-btn', function() {
+        const taskId = $(this).data('task-id');
+        performDeletion([taskId]);
+    });
+
+    $('#history-table-container').on('click', '#delete-selected-btn', function() {
+        const selectedIds = $('.row-checkbox:checked').map(function() {
+            return $(this).val();
+        }).get();
+        
+        if (selectedIds.length > 0) {
+            performDeletion(selectedIds);
+        }
+    });
+
+    // Checkbox selection logic
+    $('#history-table-container').on('change', '#select-all-checkbox', function() {
+        $('.row-checkbox').prop('checked', $(this).prop('checked'));
+        updateDeleteButtonState();
+    });
+
+    $('#history-table-container').on('change', '.row-checkbox', function() {
+        if ($('.row-checkbox:checked').length === $('.row-checkbox').length) {
+            $('#select-all-checkbox').prop('checked', true);
+        } else {
+            $('#select-all-checkbox').prop('checked', false);
+        }
+        updateDeleteButtonState();
+    });
+
+    // Set analysis date to today by default
+    document.getElementById('analysis-date').valueAsDate = new Date();
+
+    // Handle max output length slider
+    const maxLengthSlider = document.getElementById('max-output-length');
+    const maxLengthValue = document.getElementById('max-output-length-value');
+    if (maxLengthSlider) {
+        maxLengthSlider.addEventListener('input', function() {
+            maxLengthValue.textContent = this.value;
+        });
+    }
+});
+</script>
+{% endblock %}

app/web/templates/capital_flow.html

@@ -0,0 +1,866 @@
+{% extends "layout.html" %}
+
+{% block title %}资金流向 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">资金流向分析</h5>
+                </div>
+                <div class="card-body py-2">
+                    <form id="capital-flow-form" class="row g-2">
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">数据类型</span>
+                                <select class="form-select" id="data-type">
+                                    <option value="concept" selected>概念资金流</option>
+                                    <option value="individual" >个股资金流</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">周期</span>
+                                <select class="form-select" id="period-select">
+                                    <option value="10日排行" selected>10日排行</option>
+                                    <option value="5日排行">5日排行</option>
+                                    <option value="3日排行">3日排行</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-4 stock-input" style="display: none;">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">股票代码</span>
+                                <input type="text" class="form-control" id="stock-code" placeholder="例如: 600519">
+                            </div>
+                        </div>
+                        <div class="col-md-2">
+                            <button type="submit" class="btn btn-primary btn-sm w-100">
+                                <i class="fas fa-search"></i> 查询
+                            </button>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Loading Panel -->
+    <div id="loading-panel" class="text-center py-5" style="display: none;">
+        <div class="spinner-border text-primary" role="status">
+            <span class="visually-hidden">Loading...</span>
+        </div>
+        <p class="mt-3 mb-0">正在获取资金流向数据...</p>
+    </div>
+
+    <!-- Concept Fund Flow Panel -->
+    <div id="concept-flow-panel" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2 d-flex justify-content-between align-items-center">
+                    <h5 class="mb-0">概念资金流向</h5>
+                    <span id="concept-period-badge" class="badge bg-primary">10日排行</span>
+                </div>
+                <div class="card-body">
+                    <div class="table-responsive">
+                        <table class="table table-sm table-striped table-hover">
+                            <thead>
+                                <tr>
+                                    <th>序号</th>
+                                    <th>概念/行业</th>
+                                    <th data-sort="sector_index">行业指数 <i class="fas fa-sort"></i></th>
+                                    <th data-sort="change_percent">涨跌幅 <i class="fas fa-sort"></i></th>
+                                    <th data-sort="inflow">流入资金(亿) <i class="fas fa-sort"></i></th>
+                                    <th data-sort="outflow">流出资金(亿) <i class="fas fa-sort"></i></th>
+                                    <th data-sort="net_flow">净额(亿) <i class="fas fa-sort"></i></th>
+                                    <th data-sort="company_count">公司家数 <i class="fas fa-sort"></i></th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="concept-flow-table">
+                                <!-- 资金流向数据将在JS中填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Concept Stocks Panel -->
+    <div id="concept-stocks-panel" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 id="concept-stocks-title" class="mb-0">概念成分股</h5>
+                </div>
+                <div class="card-body">
+                    <div class="table-responsive">
+                        <table class="table table-sm table-striped table-hover">
+                            <thead>
+                                <tr>
+                                    <th>代码</th>
+                                    <th>名称</th>
+                                    <th>最新价</th>
+                                    <th>涨跌幅</th>
+                                    <th>主力净流入</th>
+                                    <th>主力净流入占比</th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="concept-stocks-table">
+                                <!-- 概念成分股数据将在JS中填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Individual Fund Flow Panel -->
+    <div id="individual-flow-panel" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2 d-flex justify-content-between align-items-center">
+                    <h5 id="individual-flow-title" class="mb-0">个股资金流向</h5>
+                    <span id="individual-period-badge" class="badge bg-primary">10日排行</span>
+                </div>
+                <div class="card-body">
+                    <div class="row">
+                        <div class="col-md-6">
+                            <h6>资金流向概览</h6>
+                            <table class="table table-sm">
+                                <tbody id="individual-flow-summary">
+                                    <!-- 个股资金流向概览将在JS中填充 -->
+                                </tbody>
+                            </table>
+                        </div>
+                        <div class="col-md-6">
+                            <h6>资金流入占比</h6>
+                            <div id="fund-flow-pie-chart" style="height: 200px;"></div>
+                        </div>
+                    </div>
+                    <div class="row mt-3">
+                        <div class="col-12">
+                            <h6>资金流向历史</h6>
+                            <div id="fund-flow-history-chart" style="height: 300px;"></div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Individual Fund Flow Rank Panel -->
+    <div id="individual-rank-panel" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2 d-flex justify-content-between align-items-center">
+                    <h5 class="mb-0">个股资金流向排名</h5>
+                    <span id="individual-rank-period-badge" class="badge bg-primary">10日排行</span>
+                </div>
+                <div class="card-body">
+                    <div class="table-responsive">
+                        <table class="table table-sm table-striped table-hover">
+                            <thead>
+                                <tr>
+                                    <th>序号</th>
+                                    <th>代码</th>
+                                    <th>名称</th>
+                                    <th data-sort="price">最新价 <i class="fas fa-sort"></i></th>
+                                    <th data-sort="change_percent">涨跌幅 <i class="fas fa-sort"></i></th>
+                                    <th data-sort="main_net_inflow">主力净流入 <i class="fas fa-sort"></i></th>
+                                    <th data-sort="main_net_inflow_percent">主力净流入占比 <i class="fas fa-sort"></i></th>
+                                    <th data-sort="super_large_net_inflow">超大单净流入 <i class="fas fa-sort"></i></th>
+                                    <th data-sort="large_net_inflow">大单净流入 <i class="fas fa-sort"></i></th>
+                                    <th data-sort="medium_net_inflow">中单净流入 <i class="fas fa-sort"></i></th>
+                                    <th data-sort="small_net_inflow">小单净流入 <i class="fas fa-sort"></i></th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="individual-rank-table">
+                                <!-- 个股资金流向排名数据将在JS中填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    let conceptData = [];
+    let individualRankData = [];
+    let sortConfig = {
+        concept: { key: 'rank', order: 'asc' },
+        individual: { key: 'rank', order: 'asc' }
+    };
+
+    $(document).ready(function() {
+        // 默认加载概念资金流向
+        loadConceptFundFlow('10日排行');
+
+        // 表单提交事件
+        $('#capital-flow-form').submit(function(e) {
+            e.preventDefault();
+            const dataType = $('#data-type').val();
+            const period = $('#period-select').val();
+            const stockCode = $('#stock-code').val().trim();
+
+            if (dataType === 'concept') {
+                loadConceptFundFlow(period);
+            } else if (dataType === 'individual') {
+                if (stockCode) {
+                    loadIndividualFundFlow(stockCode);
+                } else {
+                    loadIndividualFundFlowRank(period);
+                }
+            }
+        });
+
+        // 数据类型切换事件
+        $('#data-type').change(function() {
+            const dataType = $(this).val();
+            if (dataType === 'individual') {
+                $('.stock-input').show();
+            } else {
+                $('.stock-input').hide();
+            }
+        });
+
+        // 排序事件
+        $('#concept-flow-panel').on('click', 'th[data-sort]', function() {
+            handleSort('concept', $(this).data('sort'));
+        });
+
+        $('#individual-rank-panel').on('click', 'th[data-sort]', function() {
+            handleSort('individual', $(this).data('sort'));
+        });
+    });
+
+    function handleSort(type, key) {
+        const config = sortConfig[type];
+        if (config.key === key) {
+            config.order = config.order === 'asc' ? 'desc' : 'asc';
+        } else {
+            config.key = key;
+            config.order = 'asc';
+        }
+
+        if (type === 'concept') {
+            renderConceptFundFlow(conceptData, $('#concept-period-badge').text());
+        } else {
+            renderIndividualFundFlowRank(individualRankData, $('#individual-rank-period-badge').text());
+        }
+    }
+
+    // 加载概念资金流向
+    function loadConceptFundFlow(period) {
+        $('#loading-panel').show();
+        $('#concept-flow-panel, #concept-stocks-panel, #individual-flow-panel, #individual-rank-panel').hide();
+
+        $.ajax({
+            url: `/api/concept_fund_flow?period=${period}`,
+            type: 'GET',
+            success: function(response) {
+                renderConceptFundFlow(response, period);
+                $('#loading-panel').hide();
+                $('#concept-flow-panel').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                showError('获取概念资金流向数据失败: ' + error);
+            }
+        });
+    }
+
+    // 加载概念成分股
+    function loadConceptStocks(sector) {
+        $('#loading-panel').show();
+        $('#concept-stocks-panel').hide();
+
+        $.ajax({
+            url: `/api/sector_stocks?sector=${encodeURIComponent(sector)}`,
+            type: 'GET',
+            success: function(response) {
+                renderConceptStocks(response, sector);
+                $('#loading-panel').hide();
+                $('#concept-stocks-panel').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                showError('获取概念成分股数据失败: ' + error);
+            }
+        });
+    }
+
+    // 加载个股资金流向
+    function loadIndividualFundFlow(stockCode) {
+        $('#loading-panel').show();
+        $('#concept-flow-panel, #concept-stocks-panel, #individual-flow-panel, #individual-rank-panel').hide();
+
+        $.ajax({
+            url: `/api/individual_fund_flow?stock_code=${stockCode}`,
+            type: 'GET',
+            success: function(response) {
+                renderIndividualFundFlow(response);
+                $('#loading-panel').hide();
+                $('#individual-flow-panel').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                showError('获取个股资金流向数据失败: ' + error);
+            }
+        });
+    }
+
+    // 加载个股资金流向排名
+    function loadIndividualFundFlowRank(period) {
+        $('#loading-panel').show();
+        $('#concept-flow-panel, #concept-stocks-panel, #individual-flow-panel, #individual-rank-panel').hide();
+
+        $.ajax({
+            url: `/api/individual_fund_flow_rank?period=${period}`,
+            type: 'GET',
+            success: function(response) {
+                renderIndividualFundFlowRank(response, period);
+                $('#loading-panel').hide();
+                $('#individual-rank-panel').show();
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                showError('获取个股资金流向排名数据失败: ' + error);
+            }
+        });
+    }
+
+    // 渲染概念资金流向
+    function renderConceptFundFlow(data, period) {
+        if (data) {
+            conceptData = data;
+        }
+        $('#concept-period-badge').text(period);
+
+        // 更新排序图标
+        updateSortIcons('concept');
+
+        // 排序数据
+        const sortedData = sortData(conceptData, sortConfig.concept.key, sortConfig.concept.order);
+
+        let html = '';
+        if (!sortedData || sortedData.length === 0) {
+            html = '<tr><td colspan="9" class="text-center">暂无数据</td></tr>';
+        } else {
+            sortedData.forEach((item, index) => {
+                const changeClass = parseFloat(item.change_percent) >= 0 ? 'trend-up' : 'trend-down';
+                const changeIcon = parseFloat(item.change_percent) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                const netFlowClass = parseFloat(item.net_flow) >= 0 ? 'trend-up' : 'trend-down';
+                const netFlowIcon = parseFloat(item.net_flow) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${item.rank}</td>
+                        <td><a href="javascript:void(0)" onclick="loadConceptStocks('${item.sector}')">${item.sector}</a></td>
+                        <td>${formatNumber(item.sector_index, 2)}</td>
+                        <td class="${changeClass}">${changeIcon} ${formatPercent(item.change_percent)}</td>
+                        <td>${formatNumber(item.inflow, 2)}</td>
+                        <td>${formatNumber(item.outflow, 2)}</td>
+                        <td class="${netFlowClass}">${netFlowIcon} ${formatNumber(item.net_flow, 2)}</td>
+                        <td>${item.company_count}</td>
+                        <td>
+                            <button class="btn btn-sm btn-outline-primary" onclick="loadConceptStocks('${item.sector}')">
+                                <i class="fas fa-search"></i>
+                            </button>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#concept-flow-table').html(html);
+    }
+
+    // 渲染概念成分股
+    function renderConceptStocks(data, sector) {
+        $('#concept-stocks-title').text(`${sector} 成分股`);
+
+        let html = '';
+        if (!data || data.length === 0) {
+            html = '<tr><td colspan="7" class="text-center">暂无数据</td></tr>';
+        } else {
+            data.forEach((item) => {
+                const changeClass = parseFloat(item.change_percent) >= 0 ? 'trend-up' : 'trend-down';
+                const changeIcon = parseFloat(item.change_percent) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                const netFlowClass = parseFloat(item.main_net_inflow) >= 0 ? 'trend-up' : 'trend-down';
+                const netFlowIcon = parseFloat(item.main_net_inflow) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${item.code}</td>
+                        <td>${item.name}</td>
+                        <td>${formatNumber(item.price, 2)}</td>
+                        <td class="${changeClass}">${changeIcon} ${formatPercent(item.change_percent)}</td>
+                        <td class="${netFlowClass}">${netFlowIcon} ${formatMoney(item.main_net_inflow)}</td>
+                        <td class="${netFlowClass}">${formatPercent(item.main_net_inflow_percent)}</td>
+                        <td>
+                            <a href="/stock_detail/${item.code}" class="btn btn-sm btn-outline-primary">
+                                <i class="fas fa-chart-line"></i>
+                            </a>
+                            <button class="btn btn-sm btn-outline-info" onclick="loadIndividualFundFlow('${item.code}')">
+                                <i class="fas fa-money-bill-wave"></i>
+                            </button>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#concept-stocks-table').html(html);
+    }
+
+    // 渲染个股资金流向
+    function renderIndividualFundFlow(data) {
+        if (!data || !data.data || data.data.length === 0) {
+            showError('未获取到有效的个股资金流向数据');
+            return;
+        }
+
+        // Sort data by date (descending - newest first)
+        data.data.sort((a, b) => {
+            // Parse dates to ensure proper comparison
+            let dateA = new Date(a.date);
+            let dateB = new Date(b.date);
+            return dateB - dateA;
+        });
+
+        // Re-calculate summary for 90 days instead of relying on backend calculation
+        recalculateSummary(data, 90);
+
+        // 设置标题
+        $('#individual-flow-title').text(`${data.stock_code} 资金流向`);
+
+        // ��染概览
+        renderIndividualFlowSummary(data);
+
+        // 渲染资金流入占比饼图
+        renderFundFlowPieChart(data);
+
+        // 渲染资金流向历史图表
+        renderFundFlowHistoryChart(data);
+    }
+
+    function recalculateSummary(data, days) {
+        // Get recent data (up to the specified number of days)
+        const recent_data = data.data.slice(0, Math.min(days, data.data.length));
+
+        // Calculate summary statistics
+        const total_main_net_inflow = recent_data.reduce((sum, item) => sum + item.main_net_inflow, 0);
+        const avg_main_net_inflow_percent = recent_data.reduce((sum, item) => sum + item.main_net_inflow_percent, 0) / recent_data.length;
+        const positive_days = recent_data.filter(item => item.main_net_inflow > 0).length;
+        const negative_days = recent_data.length - positive_days;
+
+        // Create or update summary object
+        data.summary = {
+            recent_days: recent_data.length,
+            total_main_net_inflow: total_main_net_inflow,
+            avg_main_net_inflow_percent: avg_main_net_inflow_percent,
+            positive_days: positive_days,
+            negative_days: negative_days
+        };
+    }
+
+    // 渲染个股资金流向概览
+    function renderIndividualFlowSummary(data) {
+        if (!data.summary) return;
+
+        const summary = data.summary;
+        // Now using the first item after sorting
+        const recent = data.data[0]; // 最近一天的数据
+
+        let html = `
+            <tr>
+                <td>最新日期：</td>
+                <td>${recent.date}</td>
+                <td>最新价：</td>
+                <td>${formatNumber(recent.price, 2)}</td>
+            </tr>
+            <tr>
+                <td>涨跌幅：</td>
+                <td class="${recent.change_percent >= 0 ? 'trend-up' : 'trend-down'}">
+                    ${recent.change_percent >= 0 ? '↑' : '↓'} ${formatPercent(recent.change_percent)}
+                </td>
+                <td>分析周期：</td>
+                <td>${summary.recent_days}天</td>
+            </tr>
+            <tr>
+                <td>主力净流入：</td>
+                <td class="${summary.total_main_net_inflow >= 0 ? 'trend-up' : 'trend-down'}">
+                    ${summary.total_main_net_inflow >= 0 ? '↑' : '↓'} ${formatMoney(summary.total_main_net_inflow)}
+                </td>
+                <td>净流入占比：</td>
+                <td class="${summary.avg_main_net_inflow_percent >= 0 ? 'trend-up' : 'trend-down'}">
+                    ${summary.avg_main_net_inflow_percent >= 0 ? '↑' : '↓'} ${formatPercent(summary.avg_main_net_inflow_percent)}
+                </td>
+            </tr>
+            <tr>
+                <td>资金流入天数：</td>
+                <td>${summary.positive_days}天</td>
+                <td>资金流出天数：</td>
+                <td>${summary.negative_days}天</td>
+            </tr>
+        `;
+
+        $('#individual-flow-summary').html(html);
+    }
+
+    // 渲染资金流入占比饼图
+    function renderFundFlowPieChart(data) {
+        if (!data.data || data.data.length === 0) return;
+
+        // Using the first item after sorting
+        const recent = data.data[0]; // 最近一天的数据
+
+        // 计算资金流入总额（绝对值）
+        const totalInflow = Math.abs(recent.super_large_net_inflow) +
+                            Math.abs(recent.large_net_inflow) +
+                            Math.abs(recent.medium_net_inflow) +
+                            Math.abs(recent.small_net_inflow);
+
+        // 计算各类型占比
+        const superLargePct = Math.abs(recent.super_large_net_inflow) / totalInflow * 100;
+        const largePct = Math.abs(recent.large_net_inflow) / totalInflow * 100;
+        const mediumPct = Math.abs(recent.medium_net_inflow) / totalInflow * 100;
+        const smallPct = Math.abs(recent.small_net_inflow) / totalInflow * 100;
+
+        const options = {
+            series: [superLargePct, largePct, mediumPct, smallPct],
+            chart: {
+                type: 'pie',
+                height: 200
+            },
+            labels: ['超大单', '大单', '中单', '小单'],
+            colors: ['#0d6efd', '#198754', '#ffc107', '#dc3545'],
+            legend: {
+                position: 'bottom'
+            },
+            tooltip: {
+                y: {
+                    formatter: function(value) {
+                        return value.toFixed(2) + '%';
+                    }
+                }
+            }
+        };
+
+        // 清除旧图表
+        $('#fund-flow-pie-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#fund-flow-pie-chart"), options);
+        chart.render();
+    }
+
+    // 渲染资金流向历史图表
+    function renderFundFlowHistoryChart(data) {
+        if (!data.data || data.data.length === 0) return;
+
+        // 最近90天的数据
+        // Since we've already sorted the data, just get the first 90 and reverse for chronological display
+        const historyData = data.data.slice(0, 90).reverse();
+
+        const dates = historyData.map(item => item.date);
+        const mainNetInflow = historyData.map(item => item.main_net_inflow);
+        const superLargeInflow = historyData.map(item => item.super_large_net_inflow);
+        const largeInflow = historyData.map(item => item.large_net_inflow);
+        const mediumInflow = historyData.map(item => item.medium_net_inflow);
+        const smallInflow = historyData.map(item => item.small_net_inflow);
+        const priceChanges = historyData.map(item => item.change_percent);
+
+        const options = {
+            series: [
+                {
+                    name: '主力净流入',
+                    type: 'column',
+                    data: mainNetInflow
+                },
+                {
+                    name: '超大单',
+                    type: 'line',
+                    data: superLargeInflow
+                },
+                {
+                    name: '大单',
+                    type: 'line',
+                    data: largeInflow
+                },
+                {
+                    name: '价格涨跌幅',
+                    type: 'line',
+                    data: priceChanges
+                }
+            ],
+            chart: {
+                height: 300,
+                type: 'line',
+                toolbar: {
+                    show: false
+                }
+            },
+            stroke: {
+                width: [0, 2, 2, 2],
+                curve: 'smooth'
+            },
+            plotOptions: {
+                bar: {
+                    columnWidth: '50%'
+                }
+            },
+            colors: ['#0d6efd', '#198754', '#ffc107', '#dc3545'],
+            dataLabels: {
+                enabled: false
+            },
+            labels: dates,
+            xaxis: {
+                type: 'category'
+            },
+            yaxis: [
+                {
+                    title: {
+                        text: '资金流入(亿)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            // Convert to 亿 for display (divide by 100 million)
+                            return (val / 100000000).toFixed(2);
+                        }
+                    }
+                },
+                {
+                    opposite: true,
+                    title: {
+                        text: '价格涨跌幅(%)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            return val.toFixed(2);
+                        }
+                    },
+                    min: -10,
+                    max: 10,
+                    tickAmount: 5
+                }
+            ],
+            tooltip: {
+                shared: true,
+                intersect: false,
+                y: {
+                    formatter: function(y, { seriesIndex }) {
+                        if (seriesIndex === 3) {
+                            return y.toFixed(2) + '%';
+                        }
+                        // Display money values in 亿 (hundred million) units
+                        return (y / 100000000).toFixed(2) + ' 亿';
+                    }
+                }
+            },
+            legend: {
+                position: 'top'
+            }
+        };
+
+        // 清除旧图表
+        $('#fund-flow-history-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#fund-flow-history-chart"), options);
+        chart.render();
+    }
+
+    // 渲染个股资金流向排名
+    function renderIndividualFundFlowRank(data, period) {
+        if (data) {
+            individualRankData = data;
+        }
+        $('#individual-rank-period-badge').text(period);
+
+        // 更新排序图标
+        updateSortIcons('individual');
+
+        // 排序数据
+        const sortedData = sortData(individualRankData, sortConfig.individual.key, sortConfig.individual.order);
+
+        let html = '';
+        if (!sortedData || sortedData.length === 0) {
+            html = '<tr><td colspan="12" class="text-center">暂无数据</td></tr>';
+        } else {
+            sortedData.forEach((item) => {
+                const changeClass = parseFloat(item.change_percent) >= 0 ? 'trend-up' : 'trend-down';
+                const changeIcon = parseFloat(item.change_percent) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                const mainNetClass = parseFloat(item.main_net_inflow) >= 0 ? 'trend-up' : 'trend-down';
+                const mainNetIcon = parseFloat(item.main_net_inflow) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${item.rank}</td>
+                        <td>${item.code}</td>
+                        <td>${item.name}</td>
+                        <td>${formatNumber(item.price, 2)}</td>
+                        <td class="${changeClass}">${changeIcon} ${formatPercent(item.change_percent)}</td>
+                        <td class="${mainNetClass}">${mainNetIcon} ${formatMoney(item.main_net_inflow)}</td>
+                        <td class="${mainNetClass}">${formatPercent(item.main_net_inflow_percent)}</td>
+                        <td>${formatMoney(item.super_large_net_inflow)}</td>
+                        <td>${formatMoney(item.large_net_inflow)}</td>
+                        <td>${formatMoney(item.medium_net_inflow)}</td>
+                        <td>${formatMoney(item.small_net_inflow)}</td>
+                        <td>
+                            <a href="/stock_detail/${item.code}" class="btn btn-sm btn-outline-primary">
+                                <i class="fas fa-chart-line"></i>
+                            </a>
+                            <button class="btn btn-sm btn-outline-info" onclick="loadIndividualFundFlow('${item.code}')">
+                                <i class="fas fa-money-bill-wave"></i>
+                            </button>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#individual-rank-table').html(html);
+    }
+
+    // 格式化资金数字（支持大数字缩写）
+    function formatCompactNumber(num) {
+        if (Math.abs(num) >= 1.0e9) {
+            return (num / 1.0e9).toFixed(2) + "B";
+        } else if (Math.abs(num) >= 1.0e6) {
+            return (num / 1.0e6).toFixed(2) + "M";
+        } else if (Math.abs(num) >= 1.0e3) {
+            return (num / 1.0e3).toFixed(2) + "K";
+        } else {
+            return num.toFixed(2);
+        }
+    }
+
+    // 格式化资金
+    function formatMoney(value) {
+        if (value === null || value === undefined) {
+            return '--';
+        }
+
+        value = parseFloat(value);
+        if (isNaN(value)) {
+            return '--';
+        }
+
+        if (Math.abs(value) >= 1e8) {
+            return (value / 1e8).toFixed(2) + ' 亿';
+        } else if (Math.abs(value) >= 1e4) {
+            return (value / 1e4).toFixed(2) + ' 万';
+        } else {
+            return value.toFixed(2) + ' 元';
+        }
+    }
+
+    // 格式化百分比
+    function formatPercent(value) {
+        if (value === null || value === undefined) {
+            return '--';
+        }
+
+        value = parseFloat(value);
+        if (isNaN(value)) {
+            return '--';
+        }
+
+        return value.toFixed(2) + '%';
+    }
+
+    // 格式化数字
+    function formatNumber(value, decimals = 2) {
+        if (value === null || value === undefined) {
+            return '--';
+        }
+        value = parseFloat(value);
+        if (isNaN(value)) {
+            return '--';
+        }
+        return value.toFixed(decimals);
+    }
+
+    function sortData(data, key, order) {
+        if (!data) return [];
+        return data.sort((a, b) => {
+            let valA = a[key];
+            let valB = b[key];
+
+            if (typeof valA === 'string') {
+                valA = parseFloat(valA.replace(/,/g, ''));
+                valB = parseFloat(valB.replace(/,/g, ''));
+            }
+
+            if (order === 'asc') {
+                return valA - valB;
+            } else {
+                return valB - valA;
+            }
+        });
+    }
+
+    function updateSortIcons(type) {
+        const panelId = type === 'concept' ? '#concept-flow-panel' : '#individual-rank-panel';
+        const config = sortConfig[type];
+
+        $(`${panelId} th[data-sort] i`).removeClass('fa-sort-up fa-sort-down').addClass('fa-sort');
+
+        const icon = config.order === 'asc' ? 'fa-sort-up' : 'fa-sort-down';
+        $(`${panelId} th[data-sort="${config.key}"] i`).removeClass('fa-sort').addClass(icon);
+    }
+
+    document.addEventListener('DOMContentLoaded', function() {
+    const dataType = document.getElementById('data-type');
+    const periodSelect = document.getElementById('period-select');
+    const stockInput = document.querySelector('.stock-input');
+
+    // 初始加载时检查默认值
+    toggleOptions();
+
+    dataType.addEventListener('change', toggleOptions);
+
+    function toggleOptions() {
+        if (dataType.value === 'individual') {
+            // 个股资金流选项
+            periodSelect.innerHTML = `
+                <option value="3日">3日</option>
+                <option value="5日">5日</option>
+                <option value="10日">10日</option>
+            `;
+            stockInput.style.display = 'block';
+        } else {
+            // 概念资金流选项
+            periodSelect.innerHTML = `
+                <option value="10日排行" selected>10日排行</option>
+                <option value="5日排行">5日排��</option>
+                <option value="3日排行">3日排行</option>
+            `;
+            stockInput.style.display = 'none';
+        }
+    }
+});
+</script>
+{% endblock %}

app/web/templates/dashboard.html

@@ -0,0 +1,679 @@
+{% extends "layout.html" %}
+
+{% block title %}智能仪表盘 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+    <div class="row g-3 mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-1">  <!-- 减少padding-top和padding-bottom -->
+                    <h5 class="mb-0">智能股票分析</h5>
+                </div>
+                <div class="card-body py-2">  <!-- 减少padding-top和padding-bottom -->
+                    <form id="analysis-form" class="row g-2">  <!-- 减少间距g-3到g-2 -->
+                        <div class="col-md-4">
+                            <div class="input-group input-group-sm">  <!-- 添加input-group-sm使输入框更小 -->
+                                <span class="input-group-text">股票代码</span>
+                                <input type="text" class="form-control" id="stock-code" placeholder="例如: 600519" required>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">  <!-- 添加input-group-sm使下拉框更小 -->
+                                <span class="input-group-text">市场</span>
+                                <select class="form-select" id="market-type">
+                                    <option value="A" selected>A股</option>
+                                    <option value="HK">港股</option>
+                                    <option value="US">美股</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">  <!-- 添加input-group-sm使下拉框更小 -->
+                                <span class="input-group-text">周期</span>
+                                <select class="form-select" id="analysis-period">
+                                    <option value="1m">1个月</option>
+                                    <option value="3m">3个月</option>
+                                    <option value="6m">6个月</option>
+                                    <option value="1y" selected>1年</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-2">
+                            <button type="submit" class="btn btn-primary btn-sm w-100">  <!-- 使用btn-sm减小按钮尺寸 -->
+                                <i class="fas fa-chart-line"></i> 分析
+                            </button>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="analysis-result" style="display: none;">
+        <div class="row g-3 mb-3">
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">股票概要</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row mb-3">
+                            <div class="col-md-7">
+                                <h2 id="stock-name" class="mb-0 fs-4"></h2>
+                                <p id="stock-info" class="text-muted mb-0 small"></p>
+                            </div>
+                            <div class="col-md-5 text-end">
+                                <h3 id="stock-price" class="mb-0 fs-4"></h3>
+                                <p id="price-change" class="mb-0"></p>
+                            </div>
+                        </div>
+                        <div class="row">
+                            <div class="col-md-6">
+                                <div class="mb-2">
+                                    <span class="text-muted small">综合评分:</span>
+                                    <div class="mt-1">
+                                        <span id="total-score" class="badge rounded-pill score-pill"></span>
+                                    </div>
+                                </div>
+                                <div class="mb-2">
+                                    <span class="text-muted small">投资建议:</span>
+                                    <p id="recommendation" class="mb-0 text-strong"></p>
+                                </div>
+                            </div>
+                            <div class="col-md-6">
+                                <div class="mb-2">
+                                    <span class="text-muted small">技术面指标:</span>
+                                    <ul class="list-unstyled mt-1 mb-0 small">
+                                        <li><span class="text-muted">RSI:</span> <span id="rsi-value"></span></li>
+                                        <li><span class="text-muted">MA趋势:</span> <span id="ma-trend"></span></li>
+                                        <li><span class="text-muted">MACD信号:</span> <span id="macd-signal"></span></li>
+                                        <li><span class="text-muted">波动率:</span> <span id="volatility"></span></li>
+                                    </ul>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">多维度评分</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="radar-chart" style="height: 200px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-12">
+                <div class="card">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">价格与技术指标</h5>
+                    </div>
+                    <div class="card-body p-0">
+                        <div id="price-chart" style="height: 400px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">支撑与压力位</h5>
+                    </div>
+                    <div class="card-body">
+                        <table class="table table-sm">
+                            <thead>
+                                <tr>
+                                    <th>类型</th>
+                                    <th>价格</th>
+                                    <th>距离</th>
+                                </tr>
+                            </thead>
+                            <tbody id="support-resistance-table">
+                                <!-- 支撑压力位数据将在JS中动态填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-8">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">AI分析建议</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="ai-analysis" class="analysis-section">
+                            <!-- AI分析结果将在JS中动态填充 -->
+                            <div class="loading">
+                                <div class="spinner-border text-primary" role="status">
+                                    <span class="visually-hidden">Loading...</span>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    let stockData = [];
+        let analysisResult = null;
+
+    // === THEME-AWARE CHART LOGIC START ===
+    let charts = {}; // Store chart instances
+
+    function getApexChartThemeOptions() {
+        const isDarkMode = $('html').attr('data-theme') === 'dark';
+        const options = {
+            theme: {
+                mode: isDarkMode ? 'dark' : 'light'
+            },
+            chart: {
+                background: 'transparent'
+            },
+            tooltip: {
+                theme: isDarkMode ? 'dark' : 'light'
+            }
+        };
+        
+        if(isDarkMode) {
+            options.plotOptions = {
+                radar: {
+                    polygons: {
+                        strokeColors: '#555',
+                        fill: { colors: ['#393939', '#444444'] }
+                    }
+                }
+            };
+        } else {
+             options.plotOptions = {
+                radar: {
+                     polygons: {
+                        strokeColors: '#e9e9e9',
+                        fill: { colors: ['#f8f8f8', '#fff'] }
+                    }
+                }
+            };
+        }
+        return options;
+    }
+
+    function destroyAllCharts() {
+        Object.values(charts).forEach(chart => {
+            if (chart && typeof chart.destroy === 'function') {
+                chart.destroy();
+            }
+        });
+        charts = {};
+    }
+
+    function rerenderAllCharts() {
+        if (!analysisResult) return;
+        destroyAllCharts();
+        renderRadarChart();
+        renderPriceChart();
+    }
+    // === THEME-AWARE CHART LOGIC END ===
+
+
+    // 提交表单进行分析
+    $('#analysis-form').submit(function(e) {
+        e.preventDefault();
+        const stockCode = $('#stock-code').val().trim();
+        const marketType = $('#market-type').val();
+        const period = $('#analysis-period').val();
+
+        if (!stockCode) {
+            showError('请输入股票代码！');
+            return;
+        }
+
+        // 重定向到股票详情页
+        window.location.href = `/stock_detail/${stockCode}?market_type=${marketType}&period=${period}`;
+    });
+
+    // Observe theme changes for charts
+    const observer = new MutationObserver(function(mutations) {
+        mutations.forEach(function(mutation) {
+            if (mutation.attributeName === "data-theme") {
+                rerenderAllCharts();
+            }
+        });
+    });
+
+    observer.observe(document.documentElement, {
+        attributes: true
+    });
+
+
+    // Format AI analysis text
+    function formatAIAnalysis(text) {
+        if (!text) return '';
+
+        // First, make the text safe for HTML
+        const safeText = text
+            .replace(/&/g, '&amp;')
+            .replace(/</g, '&lt;')
+            .replace(/>/g, '&gt;');
+
+        // Replace basic Markdown elements
+        let formatted = safeText
+            // Bold text with ** or __
+            .replace(/\*\*(.*?)\*\*/g, '<strong class="keyword">$1</strong>')
+            .replace(/__(.*?)__/g, '<strong>$1</strong>')
+
+            // Italic text with * or _
+            .replace(/\*(.*?)\*/g, '<em>$1</em>')
+            .replace(/_(.*?)_/g, '<em>$1</em>')
+
+            // Headers
+            .replace(/^# (.*?)$/gm, '<h4 class="mt-3 mb-2">$1</h4>')
+            .replace(/^## (.*?)$/gm, '<h5 class="mt-2 mb-2">$1</h5>')
+
+            // Apply special styling to financial terms
+            .replace(/支撑位/g, '<span class="keyword">支撑位</span>')
+            .replace(/压力位/g, '<span class="keyword">压力位</span>')
+            .replace(/趋势/g, '<span class="keyword">趋势</span>')
+            .replace(/均线/g, '<span class="keyword">均线</span>')
+            .replace(/MACD/g, '<span class="term">MACD</span>')
+            .replace(/RSI/g, '<span class="term">RSI</span>')
+            .replace(/KDJ/g, '<span class="term">KDJ</span>')
+
+            // Highlight price patterns and movements
+            .replace(/([上涨升])/g, '<span class="trend-up">$1</span>')
+            .replace(/([下跌降])/g, '<span class="trend-down">$1</span>')
+            .replace(/(买入|做多|多头|突破)/g, '<span class="trend-up">$1</span>')
+            .replace(/(卖出|做空|空头|跌破)/g, '<span class="trend-down">$1</span>')
+
+            // Highlight price values (matches patterns like 31.25, 120.50)
+            .replace(/(\d+\.\d{2})/g, '<span class="price">$1</span>')
+
+            // Convert line breaks to paragraph tags
+            .replace(/\n\n+/g, '</p><p class="analysis-para">')
+            .replace(/\n/g, '<br>');
+
+        // Wrap in paragraph tags for consistent styling
+        return '<p class="analysis-para">' + formatted + '</p>';
+    }
+
+    // 获取股票数据
+    function fetchStockData(stockCode, marketType, period) {
+        showLoading();
+
+        $.ajax({
+            url: `/api/stock_data?stock_code=${stockCode}&market_type=${marketType}&period=${period}`,
+            type: 'GET',
+            dataType: 'json',
+            success: function(response) {
+
+                // 检查response是否有data属性
+                if (!response.data) {
+                    hideLoading();
+                    showError('响应格式不正确: 缺少data字段');
+                    return;
+                }
+
+                if (response.data.length === 0) {
+                    hideLoading();
+                    showError('未找到股票数据');
+                    return;
+                }
+
+                stockData = response.data;
+
+                // 获取增强分析数据
+                fetchEnhancedAnalysis(stockCode, marketType);
+            },
+            error: function(xhr, status, error) {
+                hideLoading();
+
+                let errorMsg = '获取股票数据失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    // 获取增强分析数据
+    function fetchEnhancedAnalysis(stockCode, marketType) {
+
+        $.ajax({
+            url: '/api/enhanced_analysis?_=' + new Date().getTime(),
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_code: stockCode,
+                market_type: marketType
+            }),
+            success: function(response) {
+
+                if (!response.result) {
+                    hideLoading();
+                    showError('增强分析响应格式不正确');
+                    return;
+                }
+
+                analysisResult = response.result;
+                renderAnalysisResult();
+                hideLoading();
+                $('#analysis-result').show();
+            },
+            error: function(xhr, status, error) {
+                hideLoading();
+
+                let errorMsg = '获取分析数据失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    // 渲染分析结果
+    function renderAnalysisResult() {
+        if (!analysisResult) return;
+
+        // 渲染股票基本信息
+        $('#stock-name').text(analysisResult.basic_info.stock_name + ' (' + analysisResult.basic_info.stock_code + ')');
+        $('#stock-info').text(analysisResult.basic_info.industry + ' | ' + analysisResult.basic_info.analysis_date);
+
+        // 渲染价格信息
+        $('#stock-price').text('¥' + formatNumber(analysisResult.price_data.current_price, 2));
+        const priceChangeClass = analysisResult.price_data.price_change >= 0 ? 'trend-up' : 'trend-down';
+        const priceChangeIcon = analysisResult.price_data.price_change >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+        $('#price-change').html(`<span class="${priceChangeClass}">${priceChangeIcon} ${formatNumber(analysisResult.price_data.price_change_value, 2)} (${formatPercent(analysisResult.price_data.price_change, 2)})</span>`);
+
+        // 渲染评分和建议
+        const scoreClass = getScoreColorClass(analysisResult.scores.total_score);
+        $('#total-score').text(analysisResult.scores.total_score).addClass(scoreClass);
+        $('#recommendation').text(analysisResult.recommendation.action);
+
+        // 渲染技术指标
+        $('#rsi-value').text(formatNumber(analysisResult.technical_analysis.indicators.rsi, 2));
+
+        const maTrendClass = getTrendColorClass(analysisResult.technical_analysis.trend.ma_trend);
+        const maTrendIcon = getTrendIcon(analysisResult.technical_analysis.trend.ma_trend);
+        $('#ma-trend').html(`<span class="${maTrendClass}">${maTrendIcon} ${analysisResult.technical_analysis.trend.ma_status}</span>`);
+
+        const macdSignal = analysisResult.technical_analysis.indicators.macd > analysisResult.technical_analysis.indicators.macd_signal ? 'BUY' : 'SELL';
+        const macdClass = macdSignal === 'BUY' ? 'trend-up' : 'trend-down';
+        const macdIcon = macdSignal === 'BUY' ? '<i class="fas fa-arrow-up"></i>' : '<i class="fas fa-arrow-down"></i>';
+        $('#macd-signal').html(`<span class="${macdClass}">${macdIcon} ${macdSignal}</span>`);
+
+        $('#volatility').text(formatPercent(analysisResult.technical_analysis.indicators.volatility, 2));
+
+        // 渲染支撑压力位
+        let supportResistanceHtml = '';
+
+        // 渲染压力位
+        if (analysisResult.technical_analysis.support_resistance.resistance &&
+            analysisResult.technical_analysis.support_resistance.resistance.short_term &&
+            analysisResult.technical_analysis.support_resistance.resistance.short_term.length > 0) {
+            const resistance = analysisResult.technical_analysis.support_resistance.resistance.short_term[0];
+            const distance = ((resistance - analysisResult.price_data.current_price) / analysisResult.price_data.current_price * 100).toFixed(2);
+            supportResistanceHtml += `
+                <tr>
+                    <td><span class="badge bg-danger">短期压力</span></td>
+                    <td>${formatNumber(resistance, 2)}</td>
+                    <td>+${distance}%</td>
+                </tr>
+            `;
+        }
+
+        if (analysisResult.technical_analysis.support_resistance.resistance &&
+            analysisResult.technical_analysis.support_resistance.resistance.medium_term &&
+            analysisResult.technical_analysis.support_resistance.resistance.medium_term.length > 0) {
+            const resistance = analysisResult.technical_analysis.support_resistance.resistance.medium_term[0];
+            const distance = ((resistance - analysisResult.price_data.current_price) / analysisResult.price_data.current_price * 100).toFixed(2);
+            supportResistanceHtml += `
+                <tr>
+                    <td><span class="badge bg-warning text-dark">中期压力</span></td>
+                    <td>${formatNumber(resistance, 2)}</td>
+                    <td>+${distance}%</td>
+                </tr>
+            `;
+        }
+
+        // 渲染支撑位
+        if (analysisResult.technical_analysis.support_resistance.support &&
+            analysisResult.technical_analysis.support_resistance.support.short_term &&
+            analysisResult.technical_analysis.support_resistance.support.short_term.length > 0) {
+            const support = analysisResult.technical_analysis.support_resistance.support.short_term[0];
+            const distance = ((support - analysisResult.price_data.current_price) / analysisResult.price_data.current_price * 100).toFixed(2);
+            supportResistanceHtml += `
+                <tr>
+                    <td><span class="badge bg-success">短期支撑</span></td>
+                    <td>${formatNumber(support, 2)}</td>
+                    <td>${distance}%</td>
+                </tr>
+            `;
+        }
+
+        if (analysisResult.technical_analysis.support_resistance.support &&
+            analysisResult.technical_analysis.support_resistance.support.medium_term &&
+            analysisResult.technical_analysis.support_resistance.support.medium_term.length > 0) {
+            const support = analysisResult.technical_analysis.support_resistance.support.medium_term[0];
+            const distance = ((support - analysisResult.price_data.current_price) / analysisResult.price_data.current_price * 100).toFixed(2);
+            supportResistanceHtml += `
+                <tr>
+                    <td><span class="badge bg-info">中期支撑</span></td>
+                    <td>${formatNumber(support, 2)}</td>
+                    <td>${distance}%</td>
+                </tr>
+            `;
+        }
+
+        $('#support-resistance-table').html(supportResistanceHtml);
+
+        // 渲染AI分析
+        $('#ai-analysis').html(formatAIAnalysis(analysisResult.ai_analysis));
+
+        // 绘制雷达图
+        renderRadarChart();
+
+        // 绘制价格图表
+        renderPriceChart();
+    }
+
+    // 绘制雷达图
+    function renderRadarChart() {
+        if (!analysisResult) return;
+
+        const options = {
+            series: [{
+                name: '评分',
+                data: [
+                    analysisResult.scores.trend_score || 0,
+                    analysisResult.scores.indicators_score || 0,
+                    analysisResult.scores.support_resistance_score || 0,
+                    analysisResult.scores.volatility_volume_score || 0
+                ]
+            }],
+            chart: {
+                height: 200,
+                type: 'radar',
+                toolbar: {
+                    show: false
+                }
+            },
+            title: {
+                text: '多维度技术分析评分',
+                style: {
+                    fontSize: '14px'
+                }
+            },
+            xaxis: {
+                categories: ['趋势分析', '技术指标', '支撑压力位', '波动与成交量']
+            },
+            yaxis: {
+                max: 10,
+                min: 0
+            },
+            fill: {
+                opacity: 0.5,
+                colors: ['#4e73df']
+            },
+            markers: {
+                size: 4
+            }
+        };
+
+        // 清除旧图表
+        $('#radar-chart').empty();
+
+        const finalOptions = $.extend(true, {}, options, getApexChartThemeOptions());
+        const chart = new ApexCharts(document.querySelector("#radar-chart"), finalOptions);
+        charts.radar = chart;
+        chart.render();
+    }
+
+    // 绘制价格图表
+    function renderPriceChart() {
+        if (!stockData || stockData.length === 0) return;
+
+        // 准备价格数据
+        const seriesData = [];
+
+        // 添加蜡烛图数据
+        const candleData = stockData.map(item => ({
+            x: new Date(item.date),
+            y: [item.open, item.high, item.low, item.close]
+        }));
+        seriesData.push({
+            name: '价格',
+            type: 'candlestick',
+            data: candleData
+        });
+
+        // 添加均线数据
+        const ma5Data = stockData.map(item => ({
+            x: new Date(item.date),
+            y: item.MA5
+        }));
+        seriesData.push({
+            name: 'MA5',
+            type: 'line',
+            data: ma5Data
+        });
+
+        const ma20Data = stockData.map(item => ({
+            x: new Date(item.date),
+            y: item.MA20
+        }));
+        seriesData.push({
+            name: 'MA20',
+            type: 'line',
+            data: ma20Data
+        });
+
+        const ma60Data = stockData.map(item => ({
+            x: new Date(item.date),
+            y: item.MA60
+        }));
+        seriesData.push({
+            name: 'MA60',
+            type: 'line',
+            data: ma60Data
+        });
+
+        // 创建图表
+        const options = {
+            series: seriesData,
+            chart: {
+                height: 400,
+                type: 'candlestick',
+                toolbar: {
+                    show: true,
+                    tools: {
+                        download: true,
+                        selection: true,
+                        zoom: true,
+                        zoomin: true,
+                        zoomout: true,
+                        pan: true,
+                        reset: true
+                    }
+                }
+            },
+            title: {
+                text: `${analysisResult.basic_info.stock_name} (${analysisResult.basic_info.stock_code}) 价格走势`,
+                align: 'left',
+                style: {
+                    fontSize: '14px'
+                }
+            },
+            xaxis: {
+                type: 'datetime'
+            },
+            yaxis: {
+                tooltip: {
+                    enabled: true
+                },
+                labels: {
+                    formatter: function(value) {
+                        return formatNumber(value, 2);  // 统一使用2位小数
+                    }
+                }
+            },
+            tooltip: {
+                shared: true,
+                custom: [
+                    function({ seriesIndex, dataPointIndex, w }) {
+                        if (seriesIndex === 0) {
+                            const o = w.globals.seriesCandleO[seriesIndex][dataPointIndex];
+                            const h = w.globals.seriesCandleH[seriesIndex][dataPointIndex];
+                            const l = w.globals.seriesCandleL[seriesIndex][dataPointIndex];
+                            const c = w.globals.seriesCandleC[seriesIndex][dataPointIndex];
+
+                            return `
+                                <div class="apexcharts-tooltip-candlestick">
+                                    <div>开盘: <span>${formatNumber(o, 2)}</span></div>
+                                    <div>最高: <span>${formatNumber(h, 2)}</span></div>
+                                    <div>最低: <span>${formatNumber(l, 2)}</span></div>
+                                    <div>收盘: <span>${formatNumber(c, 2)}</span></div>
+                                </div>
+                            `;
+                        }
+                        return '';
+                    }
+                ]
+            },
+            plotOptions: {
+                candlestick: {
+                    colors: {
+                        upward: '#3C90EB',
+                        downward: '#DF7D46'
+                    }
+                }
+            }
+        };
+
+        // 清除旧图表
+        $('#price-chart').empty();
+
+        const finalOptions = $.extend(true, {}, options, getApexChartThemeOptions());
+        const chart = new ApexCharts(document.querySelector("#price-chart"), finalOptions);
+        charts.price = chart;
+        chart.render();
+    }
+</script>
+{% endblock %}

app/web/templates/error.html

@@ -0,0 +1,30 @@
+{% extends "layout.html" %}
+
+{% block title %}错误 {{ error_code }} - 股票智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container py-5">
+    <div class="row justify-content-center">
+        <div class="col-md-8">
+            <div class="card">
+                <div class="card-header bg-danger text-white">
+                    <h4 class="mb-0">错误 {{ error_code }}</h4>
+                </div>
+                <div class="card-body text-center py-5">
+                    <i class="fas fa-exclamation-triangle fa-5x text-danger mb-4"></i>
+                    <h2>出现错误</h2>
+                    <p class="lead">{{ message }}</p>
+                    <div class="mt-4">
+                        <a href="/" class="btn btn-primary me-2">
+                            <i class="fas fa-home"></i> 返回首页
+                        </a>
+                        <button class="btn btn-outline-secondary" onclick="history.back()">
+                            <i class="fas fa-arrow-left"></i> 返回上一页
+                        </button>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}

app/web/templates/etf_analysis.html

@@ -0,0 +1,321 @@
+
+{% extends "layout.html" %}
+
+{% block title %}ETF 分析{% endblock %}
+
+{% block content %}
+<div class="container mt-4">
+    <h1 class="mb-4">ETF 综合分析</h1>
+
+    <!-- 输入区域 -->
+    <div class="card mb-4">
+        <div class="card-body">
+            <h5 class="card-title">输入ETF代码</h5>
+            <div class="input-group">
+                <input type="text" id="etf-code-input" class="form-control" placeholder="例如: 510300">
+                <button id="analyze-btn" class="btn btn-primary">开始分析</button>
+            </div>
+            <small class="form-text text-muted">输入您想分析的ETF基金代码，然后点击“开始分析”按钮。</small>
+        </div>
+    </div>
+
+    <!-- 加载与状态显示 -->
+    <div id="status-container" class="mb-4" style="display: none;">
+        <div class="d-flex align-items-center">
+            <strong>正在分析中...</strong>
+            <div class="spinner-border ms-auto" role="status" aria-hidden="true"></div>
+        </div>
+        <div class="progress mt-2">
+            <div id="progress-bar" class="progress-bar" role="progressbar" style="width: 0%;" aria-valuenow="0" aria-valuemin="0" aria-valuemax="100">0%</div>
+        </div>
+    </div>
+    
+    <!-- 错误提示 -->
+    <div id="error-alert" class="alert alert-danger" style="display: none;" role="alert"></div>
+
+    <!-- 结果显示区域 -->
+    <div id="results-container" class="mt-4" style="display: none;">
+        
+        <!-- Tab 导航 -->
+        <ul class="nav nav-tabs" id="analysisTab" role="tablist">
+            <li class="nav-item" role="presentation">
+                <button class="nav-link active" id="summary-tab" data-bs-toggle="tab" data-bs-target="#summary" type="button" role="tab" aria-controls="summary" aria-selected="true">AI总结与概览</button>
+            </li>
+            <li class="nav-item" role="presentation">
+                <button class="nav-link" id="performance-tab" data-bs-toggle="tab" data-bs-target="#performance" type="button" role="tab" aria-controls="performance" aria-selected="false">市场表现</button>
+            </li>
+            <li class="nav-item" role="presentation">
+                <button class="nav-link" id="fund-flow-tab" data-bs-toggle="tab" data-bs-target="#fund-flow" type="button" role="tab" aria-controls="fund-flow" aria-selected="false">资金流向</button>
+            </li>
+            <li class="nav-item" role="presentation">
+                <button class="nav-link" id="risk-tab" data-bs-toggle="tab" data-bs-target="#risk" type="button" role="tab" aria-controls="risk" aria-selected="false">风险与跟踪</button>
+            </li>
+            <li class="nav-item" role="presentation">
+                <button class="nav-link" id="holdings-tab" data-bs-toggle="tab" data-bs-target="#holdings" type="button" role="tab" aria-controls="holdings" aria-selected="false">持仓分析</button>
+            </li>
+            <li class="nav-item" role="presentation">
+                <button class="nav-link" id="sector-tab" data-bs-toggle="tab" data-bs-target="#sector" type="button" role="tab" aria-controls="sector" aria-selected="false">板块分析</button>
+            </li>
+        </ul>
+
+        <!-- Tab 内容 -->
+        <div class="tab-content" id="analysisTabContent">
+            <!-- AI总结与概览 -->
+            <div class="tab-pane fade show active" id="summary" role="tabpanel" aria-labelledby="summary-tab">
+                <div class="row mt-3">
+                    <div class="col-md-8">
+                        <div class="card">
+                            <div class="card-header">AI 综合诊断</div>
+                            <div class="card-body" id="ai-summary-content"><p>AI分析摘要将显示在这里。</p></div>
+                        </div>
+                    </div>
+                    <div class="col-md-4">
+                        <div class="card">
+                            <div class="card-header">基本信息</div>
+                            <div class="card-body" id="basic-info-content"><p>ETF基本信息将显示在这里。</p></div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <!-- 市场表现 -->
+            <div class="tab-pane fade" id="performance" role="tabpanel" aria-labelledby="performance-tab">
+                <div class="card mt-3">
+                    <div class="card-header">市场表现与技术分析</div>
+                    <div class="card-body" id="performance-content"><p>回报率、技术指标图表将显示在这里。</p></div>
+                </div>
+            </div>
+            <!-- 资金流向 -->
+            <div class="tab-pane fade" id="fund-flow" role="tabpanel" aria-labelledby="fund-flow-tab">
+                <div class="card mt-3">
+                    <div class="card-header">资金流向分析</div>
+                    <div class="card-body" id="fund-flow-content"><p>资金流向图表将显��在这里。</p></div>
+                </div>
+            </div>
+            <!-- 风险与跟踪 -->
+            <div class="tab-pane fade" id="risk" role="tabpanel" aria-labelledby="risk-tab">
+                <div class="card mt-3">
+                    <div class="card-header">风险与跟踪能力分析</div>
+                    <div class="card-body" id="risk-content"><p>波动率、跟踪误差、溢价率将显示在这里。</p></div>
+                </div>
+            </div>
+            <!-- 持仓分析 -->
+            <div class="tab-pane fade" id="holdings" role="tabpanel" aria-labelledby="holdings-tab">
+                <div class="card mt-3">
+                    <div class="card-header">持仓分析</div>
+                    <div class="card-body" id="holdings-content"><p>前十大持仓表格将显示在这里。</p></div>
+                </div>
+            </div>
+            <!-- 板块分析 -->
+            <div class="tab-pane fade" id="sector" role="tabpanel" aria-labelledby="sector-tab">
+                <div class="card mt-3">
+                    <div class="card-header">板块深度分析</div>
+                    <div class="card-body" id="sector-content"><p>板块景气度、估值水平将显示在这里。</p></div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+$(document).ready(function() {
+    let taskId = null;
+    let pollingInterval = null;
+
+    // Override showError from layout to use the local error alert
+    function showError(message) {
+        $('#error-alert').text(message).show();
+    }
+
+    $('#analyze-btn').click(function() {
+        const etfCode = $('#etf-code-input').val().trim();
+        if (!etfCode) {
+            showError('请输入有效的ETF代码。');
+            return;
+        }
+
+        // Reset UI
+        $('#results-container').hide();
+        $('#error-alert').hide();
+        $('#status-container').show();
+        $('#progress-bar').css('width', '0%').attr('aria-valuenow', 0).text('0%');
+
+        // Start analysis
+        $.ajax({
+            url: '/api/start_etf_analysis',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({ etf_code: etfCode }),
+            success: function(response) {
+                taskId = response.task_id;
+                if (response.status === 'completed') {
+                    displayResults(response.result);
+                } else {
+                    startPolling();
+                }
+            },
+            error: function(xhr) {
+                const errorMsg = xhr.responseJSON ? xhr.responseJSON.error : '未知错误';
+                showError('启动分析任务失败: ' + errorMsg);
+                $('#status-container').hide();
+            }
+        });
+    });
+
+    function startPolling() {
+        if (pollingInterval) clearInterval(pollingInterval);
+
+        pollingInterval = setInterval(function() {
+            if (!taskId) {
+                clearInterval(pollingInterval);
+                return;
+            }
+
+            $.ajax({
+                url: `/api/etf_analysis_status/${taskId}`,
+                type: 'GET',
+                success: function(response) {
+                    $('#progress-bar').css('width', response.progress + '%').attr('aria-valuenow', response.progress).text(response.progress + '%');
+
+                    if (response.status === 'completed') {
+                        clearInterval(pollingInterval);
+                        pollingInterval = null;
+                        displayResults(response.result);
+                    } else if (response.status === 'failed') {
+                        clearInterval(pollingInterval);
+                        pollingInterval = null;
+                        showError('分析任务失败: ' + response.error);
+                        $('#status-container').hide();
+                    }
+                },
+                error: function(xhr) {
+                    clearInterval(pollingInterval);
+                    pollingInterval = null;
+                    const errorMsg = xhr.responseJSON ? xhr.responseJSON.error : '未知错误';
+                    showError('获取任务状态失败: ' + errorMsg);
+                    $('#status-container').hide();
+                }
+            });
+        }, 2000); // Poll every 2 seconds
+    }
+
+    function displayResults(results) {
+        $('#status-container').hide();
+        $('#results-container').show();
+
+        // 1. AI 总结
+        const summary = results.ai_summary;
+        if (summary && summary.message) {
+             // 使用 marked.js 将 Markdown 转换为 HTML
+            $('#ai-summary-content').html(marked.parse(summary.message));
+        } else {
+            $('#ai-summary-content').html(`<div class="p-3"><p class="text-danger">${summary.error || 'AI总结加载失败'}</p></div>`);
+        }
+
+        // 2. 基本信息
+        const basicInfo = results.basic_info;
+        console.log('ETF anlaysis basic info:', basicInfo);
+        if (basicInfo && !basicInfo.error) {
+            let infoHtml = '<ul class="list-group list-group-flush">';
+            const keyMap = {'基金简称': '简称', '基金代码': '代码', '跟踪标的': '跟踪指数', '基金规模': '规模', '成立日期': '成立日', '基金管理人': '管理人'};
+            for (const [key, value] of Object.entries(keyMap)) {
+                if (basicInfo[key]) {
+                    infoHtml += `<li class="list-group-item d-flex justify-content-between align-items-center">${value} <span class="badge bg-primary rounded-pill">${basicInfo[key]}</span></li>`;
+                }
+            }
+            $('#basic-info-content').html(infoHtml + '</ul>');
+        } else {
+            $('#basic-info-content').html(`<div class="p-3"><p class="text-danger">${basicInfo.error || '信息加载失败'}</p></div>`);
+        }
+        
+        // 3. 市场表现
+        const perf = results.market_performance;
+        if (perf && !perf.error) {
+            let perfHtml = '<div class="row"><div class="col-lg-6"><h6 class="text-center mb-3">回报率 vs. 基准(沪深300)</h6><table class="table table-sm table-hover"><thead><tr><th>周期</th><th>ETF</th><th>基准</th><th>超额</th></tr></thead><tbody>';
+            const returns = perf.returns || {}, ben_returns = perf.benchmark_returns || {}, alpha = perf.alpha || {};
+            for(const period in returns) {
+                const r = returns[period] || 0;
+                const br = ben_returns[period] || 0;
+                const a = alpha[period] || 0;
+                perfHtml += `<tr><td>${period}</td><td class="${r >= 0 ? 'text-success' : 'text-danger'} fw-bold">${r.toFixed(2)}%</td><td class="${br >= 0 ? 'text-success' : 'text-danger'}">${br.toFixed(2)}%</td><td class="${a >= 0 ? 'text-success' : 'text-danger'} fw-bold">${a.toFixed(2)}%</td></tr>`;
+            }
+            perfHtml += '</tbody></table></div><div class="col-lg-3"><h6 class="text-center mb-3">流动性</h6><ul class="list-group">';
+            const liq = perf.liquidity || {};
+            const avg_volume = liq['日均成交额(近一月)'] ? (liq['日均成交额(近一月)']/1e8).toFixed(2) + ' 亿' : 'N/A';
+            const avg_turnover = liq['日均换手率(近一月)'] ? liq['日均换手率(近一月)'].toFixed(2) + ' %' : 'N/A';
+            perfHtml += `<li class="list-group-item">日均成交额<br><strong class="h5">${avg_volume}</strong></li>`;
+            perfHtml += `<li class="list-group-item">日均换手率<br><strong class="h5">${avg_turnover}</strong></li></ul></div>`;
+            perfHtml += '<div class="col-lg-3"><h6 class="text-center mb-3">技术指标</h6><ul class="list-group">';
+            const ind = perf.tech_indicators || {};
+            for(const [name, value] of Object.entries(ind)) {
+                 perfHtml += `<li class="list-group-item d-flex justify-content-between"><span>${name}</span> <span class="fw-bold">${value ? value.toFixed(2) : 'N/A'}</span></li>`;
+            }
+            $('#performance-content').html(perfHtml + '</ul></div></div>');
+        } else {
+             $('#performance-content').html(`<div class="p-3"><p class="text-danger">${perf.error || '表现数据加载失败'}</p></div>`);
+        }
+
+        // 4. 资金流向
+        const flow = results.fund_flow;
+        if (flow && !flow.error) {
+            let flowHtml = '<div class="row"><div class="col-md-4"><h6 class="text-center mb-3">累计资金净流入(估算)</h6><ul class="list-group">';
+            for(const [period, amount] of Object.entries(flow.summary || {})) {
+                flowHtml += `<li class="list-group-item d-flex justify-content-between"><span>${period}</span> <span class="${amount >= 0 ? 'text-success' : 'text-danger'} fw-bold">${(amount / 1e8).toFixed(2)} 亿</span></li>`;
+            }
+            flowHtml += '</ul></div><div class="col-md-8"><div id="fund-flow-chart"></div></div></div>';
+            $('#fund-flow-content').html(flowHtml);
+            if (flow.daily_flow_chart_data && flow.daily_flow_chart_data.data) {
+                const chartData = flow.daily_flow_chart_data.data.map(item => ({ x: item[0], y: item[1] }));
+                const options = { series: [{ name: '资金净流入(亿元)', data: chartData }], chart: { type: 'bar', height: 350, toolbar: { show: true }, zoom: { enabled: true } }, plotOptions: { bar: { colors: { ranges: [{ from: -Infinity, to: 0, color: '#dc3545' }, { from: 0, to: Infinity, color: '#28a745' }] } } }, xaxis: { type: 'datetime' }, yaxis: { title: { text: '资金净流入估算 (亿元)' } }, tooltip: { y: { formatter: (val) => `${val.toFixed(4)} 亿` } }, title: { text: '每日资金净流入估算 (近60日)', align: 'center' } };
+                new ApexCharts(document.querySelector("#fund-flow-chart"), options).render();
+            }
+        } else {
+            $('#fund-flow-content').html(`<div class="p-3"><p class="text-danger">${flow.error || '资金流数据加载失���'}</p></div>`);
+        }
+
+        // 5. 风险与跟踪能力
+        const risk = results.risk_and_tracking;
+        if (risk && !risk.error) {
+            let riskHtml = `<div class="row text-center"><div class="col-md-3"><div class="card p-2"><h6 class="card-title">年化波动率</h6><p class="h4">${(risk.annualized_volatility * 100).toFixed(2)}%</p></div></div>`;
+            riskHtml += `<div class="col-md-2"><div class="card p-2"><h6 class="card-title">Beta</h6><p class="h4">${risk.beta.toFixed(2)}</p></div></div>`;
+            riskHtml += `<div class="col-md-2"><div class="card p-2"><h6 class="card-title">夏普比率</h6><p class="h4">${risk.sharpe_ratio.toFixed(2)}</p></div></div>`;
+            riskHtml += `<div class="col-md-2"><div class="card p-2"><h6 class="card-title">跟踪误差</h6><p class="h4">${(risk.tracking_error * 100).toFixed(2)}%</p></div></div>`;
+            const premium = risk.avg_premium_discount_monthly || 0;
+            riskHtml += `<div class="col-md-3"><div class="card p-2"><h6 class="card-title">月均溢价率</h6><p class="h4 ${premium >= 0 ? 'text-danger' : 'text-success'}">${premium.toFixed(2)}%</p></div></div></div>`; // 溢价为红，折价为绿
+            $('#risk-content').html(riskHtml);
+        } else {
+            $('#risk-content').html(`<div class="p-3"><p class="text-danger">${risk.error || '风险数据加载失败'}</p></div>`);
+        }
+
+        // 6. 持仓分析
+        const holdings = results.holdings;
+        if (holdings && !holdings.error) {
+            let holdingsHtml = `<div class="row"><div class="col-md-8"><h6 class="text-center mb-3">前十大持仓股</h6><table class="table table-sm table-hover"><thead><tr><th>代码</th><th>名称</th><th>持仓市值(元)</th><th>占净值比例(%)</th></tr></thead><tbody>`;
+            for (const stock of holdings.top_10_holdings) {
+                holdingsHtml += `<tr><td>${stock['股票代码']}</td><td>${stock['股票名称']}</td><td>${stock['持仓市值']}</td><td>${stock['占净值比例(%)'].toFixed(2)}</td></tr>`;
+            }
+            holdingsHtml += `</tbody></table></div><div class="col-md-4"><h6 class="text-center mb-3">持仓集中度</h6><div class="card p-3 text-center"><p class="h1">${holdings.concentration.toFixed(2)}%</p><small class="text-muted">前十大持仓占比</small></div></div></div>`;
+            $('#holdings-content').html(holdingsHtml);
+        } else {
+            $('#holdings-content').html(`<div class="p-3"><p class="text-danger">${holdings.error || '持仓数据加载失败'}</p></div>`);
+        }
+
+        // 7. 板块深度分析
+        const sector = results.sector_analysis;
+        if (sector && !sector.error) {
+            let sectorHtml = `<h5 class="text-center mb-4">板块: ${sector.sector_name}</h5><div class="row"><div class="col-md-6"><h6 class="text-center mb-3">板块回报率</h6><ul class="list-group">`;
+            for (const [period, rate] of Object.entries(sector.returns)) {
+                sectorHtml += `<li class="list-group-item d-flex justify-content-between">${period}<span class="${rate >= 0 ? 'text-success' : 'text-danger'} fw-bold">${rate.toFixed(2)}%</span></li>`;
+            }
+            const pe = sector.valuation.current_pe || 0;
+            const percentile = sector.valuation.pe_percentile || 0;
+            sectorHtml += `</ul></div><div class="col-md-6"><h6 class="text-center mb-3">板块估值</h6><div class="card p-3 text-center"><p>当前滚动PE: <strong class="h5">${pe.toFixed(2)}</strong></p><p>位于历史 <strong class="h5">${percentile.toFixed(2)}%</strong> 分位点</p></div></div></div>`;
+            $('#sector-content').html(sectorHtml);
+        } else {
+            $('#sector-content').html(`<div class="p-3"><p class="text-danger">${sector.error || '板块数据加载失败'}</p></div>`);
+        }
+    }
+});
+</script>
+{% endblock %}

app/web/templates/fundamental.html

@@ -0,0 +1,405 @@
+{% extends "layout.html" %}
+
+{% block title %}基本面分析 - {{ stock_code }} - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">基本面分析</h5>
+                </div>
+                <div class="card-body py-2">
+                    <form id="fundamental-form" class="row g-2">
+                        <div class="col-md-4">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">股票代码</span>
+                                <input type="text" class="form-control" id="stock-code" placeholder="例如: 600519" required>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">市场</span>
+                                <select class="form-select" id="market-type">
+                                    <option value="A" selected>A股</option>
+                                    <option value="HK">港股</option>
+                                    <option value="US">美股</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <button type="submit" class="btn btn-primary btn-sm w-100">
+                                <i class="fas fa-search"></i> 分析
+                            </button>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="fundamental-result" style="display: none;">
+        <div class="row g-3 mb-3">
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">财务概况</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row mb-3">
+                            <div class="col-md-7">
+                                <h3 id="stock-name" class="mb-0 fs-4"></h3>
+                                <p id="stock-info" class="text-muted mb-0 small"></p>
+                            </div>
+                            <div class="col-md-5 text-end">
+                                <span id="fundamental-score" class="badge rounded-pill score-pill"></span>
+                            </div>
+                        </div>
+                        <div class="row">
+                            <div class="col-md-6">
+                                <h6>估值指标</h6>
+                                <ul class="list-unstyled mt-1 mb-0 small">
+                                    <li><span class="text-muted">PE(TTM):</span> <span id="pe-ttm"></span></li>
+                                    <li><span class="text-muted">PB:</span> <span id="pb"></span></li>
+                                    <li><span class="text-muted">PS(TTM):</span> <span id="ps-ttm"></span></li>
+                                </ul>
+                            </div>
+                            <div class="col-md-6">
+                                <h6>盈利能力</h6>
+                                <ul class="list-unstyled mt-1 mb-0 small">
+                                    <li><span class="text-muted">ROE:</span> <span id="roe"></span></li>
+                                    <li><span class="text-muted">毛利率:</span> <span id="gross-margin"></span></li>
+                                    <li><span class="text-muted">净利润率:</span> <span id="net-profit-margin"></span></li>
+                                </ul>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-6">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">成长性分析</h5>
+                    </div>
+                    <div class="card-body">
+                        <div class="row">
+                            <div class="col-md-6">
+                                <h6>营收增长</h6>
+                                <ul class="list-unstyled mt-1 mb-0 small">
+                                    <li><span class="text-muted">3年CAGR:</span> <span id="revenue-growth-3y"></span></li>
+                                    <li><span class="text-muted">5年CAGR:</span> <span id="revenue-growth-5y"></span></li>
+                                </ul>
+                            </div>
+                            <div class="col-md-6">
+                                <h6>利润增长</h6>
+                                <ul class="list-unstyled mt-1 mb-0 small">
+                                    <li><span class="text-muted">3年CAGR:</span> <span id="profit-growth-3y"></span></li>
+                                    <li><span class="text-muted">5年CAGR:</span> <span id="profit-growth-5y"></span></li>
+                                </ul>
+                            </div>
+                        </div>
+                        <div class="mt-3">
+                            <div id="growth-chart" style="height: 150px;"></div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="row g-3 mb-3">
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">估值评分</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="valuation-chart" style="height: 200px;"></div>
+                        <p id="valuation-comment" class="small text-muted mt-2"></p>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">财务健康评分</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="financial-chart" style="height: 200px;"></div>
+                        <p id="financial-comment" class="small text-muted mt-2"></p>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4">
+                <div class="card h-100">
+                    <div class="card-header py-2">
+                        <h5 class="mb-0">成长性评分</h5>
+                    </div>
+                    <div class="card-body">
+                        <div id="growth-score-chart" style="height: 200px;"></div>
+                        <p id="growth-comment" class="small text-muted mt-2"></p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    $(document).ready(function() {
+        $('#fundamental-form').submit(function(e) {
+            e.preventDefault();
+            const stockCode = $('#stock-code').val().trim();
+            const marketType = $('#market-type').val();
+
+            if (!stockCode) {
+                showError('请输入股票代码！');
+                return;
+            }
+
+            fetchFundamentalAnalysis(stockCode);
+        });
+    });
+
+    function fetchFundamentalAnalysis(stockCode) {
+        showLoading();
+
+        $.ajax({
+            url: '/api/fundamental_analysis',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_code: stockCode
+            }),
+            success: function(response) {
+                hideLoading();
+                renderFundamentalAnalysis(response, stockCode);
+                $('#fundamental-result').show();
+            },
+            error: function(xhr, status, error) {
+                hideLoading();
+                let errorMsg = '获取基本面分析失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+    function renderFundamentalAnalysis(data, stockCode) {
+        // 设置基本信息
+        $('#stock-name').text(data.details.indicators.stock_name || stockCode);
+        $('#stock-info').text(data.details.indicators.industry || '未知行业');
+
+        // 设置评分
+        const scoreClass = getScoreColorClass(data.total);
+        $('#fundamental-score').text(data.total).addClass(scoreClass);
+
+        // 设置估值指标
+        $('#pe-ttm').text(formatNumber(data.details.indicators.pe_ttm, 2));
+        $('#pb').text(formatNumber(data.details.indicators.pb, 2));
+        $('#ps-ttm').text(formatNumber(data.details.indicators.ps_ttm, 2));
+
+        // 设置盈利能力
+        $('#roe').text(formatPercent(data.details.indicators.roe, 2));
+        $('#gross-margin').text(formatPercent(data.details.indicators.gross_margin, 2));
+        $('#net-profit-margin').text(formatPercent(data.details.indicators.net_profit_margin, 2));
+
+        // 设置成长率
+        $('#revenue-growth-3y').text(formatPercent(data.details.growth.revenue_growth_3y, 2));
+        $('#revenue-growth-5y').text(formatPercent(data.details.growth.revenue_growth_5y, 2));
+        $('#profit-growth-3y').text(formatPercent(data.details.growth.profit_growth_3y, 2));
+        $('#profit-growth-5y').text(formatPercent(data.details.growth.profit_growth_5y, 2));
+
+        // 评论
+        $('#valuation-comment').text("估值处于行业" + (data.valuation > 20 ? "合理水平" : "偏高水平"));
+        $('#financial-comment').text("财务状况" + (data.financial_health > 30 ? "良好" : "一般"));
+        $('#growth-comment').text("成长性" + (data.growth > 20 ? "较好" : "一般"));
+
+        // 渲染图表
+        renderValuationChart(data.valuation);
+        renderFinancialChart(data.financial_health);
+        renderGrowthScoreChart(data.growth);
+        renderGrowthChart(data.details.growth);
+    }
+
+    function renderValuationChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 200,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        name: {
+                            fontSize: '22px',
+                        },
+                        value: {
+                            fontSize: '16px',
+                        },
+                        total: {
+                            show: true,
+                            label: '估值',
+                            formatter: function() {
+                                return score;
+                            }
+                        }
+                    }
+                }
+            },
+            colors: ['#1ab7ea'],
+            labels: ['估值'],
+        };
+
+        const chart = new ApexCharts(document.querySelector("#valuation-chart"), options);
+        chart.render();
+    }
+
+    function renderFinancialChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 200,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        name: {
+                            fontSize: '22px',
+                        },
+                        value: {
+                            fontSize: '16px',
+                        },
+                        total: {
+                            show: true,
+                            label: '财务',
+                            formatter: function() {
+                                return score;
+                            }
+                        }
+                    }
+                }
+            },
+            colors: ['#20E647'],
+            labels: ['财务'],
+        };
+
+        const chart = new ApexCharts(document.querySelector("#financial-chart"), options);
+        chart.render();
+    }
+
+    function renderGrowthScoreChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 200,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        name: {
+                            fontSize: '22px',
+                        },
+                        value: {
+                            fontSize: '16px',
+                        },
+                        total: {
+                            show: true,
+                            label: '成长',
+                            formatter: function() {
+                                return score;
+                            }
+                        }
+                    }
+                }
+            },
+            colors: ['#F9CE1D'],
+            labels: ['成长'],
+        };
+
+        const chart = new ApexCharts(document.querySelector("#growth-score-chart"), options);
+        chart.render();
+    }
+
+    function renderGrowthChart(growthData) {
+        const options = {
+            series: [{
+                name: '营收增长率',
+                data: [
+                    growthData.revenue_growth_3y || 0,
+                    growthData.revenue_growth_5y || 0
+                ]
+            }, {
+                name: '利润增长率',
+                data: [
+                    growthData.profit_growth_3y || 0,
+                    growthData.profit_growth_5y || 0
+                ]
+            }],
+            chart: {
+                type: 'bar',
+                height: 150,
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                bar: {
+                    horizontal: false,
+                    columnWidth: '55%',
+                    endingShape: 'rounded'
+                },
+            },
+            dataLabels: {
+                enabled: false
+            },
+            stroke: {
+                show: true,
+                width: 2,
+                colors: ['transparent']
+            },
+            xaxis: {
+                categories: ['3年CAGR', '5年CAGR'],
+            },
+            yaxis: {
+                title: {
+                    text: '百分比 (%)'
+                }
+            },
+            fill: {
+                opacity: 1
+            },
+            tooltip: {
+                y: {
+                    formatter: function(val) {
+                        return val + "%"
+                    }
+                }
+            }
+        };
+
+        const chart = new ApexCharts(document.querySelector("#growth-chart"), options);
+        chart.render();
+    }
+</script>
+{% endblock %}

app/web/templates/index.html

@@ -0,0 +1,636 @@
+{% extends "layout.html" %}
+
+{% block title %}首页 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="finance-portal-container">
+    <!-- 左侧导航栏 -->
+    <div class="portal-sidebar">
+        <div class="sidebar-menu">
+            <div class="sidebar-header">
+                <h5><i class="fas fa-th-list"></i> 功能导航</h5>
+            </div>
+            <div class="sidebar-content">
+                <ul class="sidebar-nav">
+                    <li><a href="/dashboard"><i class="fas fa-chart-line"></i> 智能仪表盘</a></li>
+                    <li><a href="/fundamental"><i class="fas fa-file-invoice-dollar"></i> 基本面分析</a></li>
+                    <li><a href="/capital_flow"><i class="fas fa-money-bill-wave"></i> 资金流向</a></li>
+                    <li><a href="/market_scan"><i class="fas fa-search"></i> 市场扫描</a></li>
+                    <li><a href="/scenario_predict"><i class="fas fa-lightbulb"></i> 情景预测</a></li>
+                    <li><a href="/portfolio"><i class="fas fa-briefcase"></i> 投资组合</a></li>
+                    <li><a href="/qa"><i class="fas fa-question-circle"></i> 智能问答</a></li>
+                    <li><a href="/risk_monitor"><i class="fas fa-exclamation-triangle"></i> 风险监控</a></li>
+                    <li><a href="/industry_analysis"><i class="fas fa-industry"></i> 行业分析</a></li>
+                    <li><a href="/agent_analysis"><i class="fas fa-robot"></i> 智能体分析</a></li>
+                </ul>
+            </div>
+        </div>
+
+<!--        <div class="quick-search mt-4">-->
+<!--            <div class="card">-->
+<!--                <div class="card-header">-->
+<!--                    <h5 class="mb-0"><i class="fas fa-search"></i> 快速分析</h5>-->
+<!--                </div>-->
+<!--                <div class="card-body">-->
+<!--                    <form id="quick-analysis-form">-->
+<!--                        <div class="mb-3">-->
+<!--                            <label for="quick-stock-code" class="form-label">股票代码</label>-->
+<!--                            <input type="text" class="form-control" id="quick-stock-code" placeholder="例如: 600519">-->
+<!--                        </div>-->
+<!--                        <div class="mb-3">-->
+<!--                            <label for="quick-market-type" class="form-label">市场类型</label>-->
+<!--                            <select class="form-select" id="quick-market-type">-->
+<!--                                <option value="A" selected>A股</option>-->
+<!--                                <option value="HK">港股</option>-->
+<!--                                <option value="US">美股</option>-->
+<!--                            </select>-->
+<!--                        </div>-->
+<!--                        <div class="d-grid">-->
+<!--                            <button type="button" class="btn btn-primary" id="quick-analysis-btn">开始分析</button>-->
+<!--                        </div>-->
+<!--                    </form>-->
+<!--                </div>-->
+<!--            </div>-->
+<!--        </div>-->
+    </div>
+
+    <!-- 中间新闻区域 -->
+    <div class="portal-news">
+        <div class="news-header">
+            <h5><i class="fas fa-newspaper"></i> 实时快讯（来源：财联社）</h5>
+            <div class="news-tools">
+                <button class="btn btn-sm btn-outline-primary refresh-news-btn">
+                    <i class="fas fa-sync-alt"></i>
+                </button>
+                <div class="form-check form-check-inline ms-2">
+                    <input class="form-check-input" type="checkbox" id="only-important">
+                    <label class="form-check-label" for="only-important">只看重要</label>
+                </div>
+            </div>
+        </div>
+        <div class="news-content" id="news-timeline">
+            <div class="text-center py-5">
+                <div class="spinner-border text-primary" role="status">
+                    <span class="visually-hidden">Loading...</span>
+                </div>
+                <p class="mt-2">加载新闻中...</p>
+            </div>
+        </div>
+    </div>
+
+    <!-- 右侧热点区域 -->
+    <div class="portal-hotspot">
+        <div class="hotspot-header">
+            <h5><i class="fas fa-fire"></i> 热点</h5>
+        </div>
+        <div class="hotspot-content" id="hotspot-list">
+            <div class="text-center py-5">
+                <div class="spinner-border text-primary" role="status">
+                    <span class="visually-hidden">Loading...</span>
+                </div>
+                <p class="mt-2">加载热点中...</p>
+            </div>
+        </div>
+    </div>
+
+    <!-- 页脚固定区域 -->
+    <div class="portal-footer">
+        <!-- 页脚市场状态部分 -->
+        <div class="market-status">
+            <div class="market-group">
+                <div class="group-title">亚太市场</div>
+                <div class="status-group">
+                    <div class="status-item" id="china-market">
+                        <i class="fas fa-circle"></i> A股
+                        <span class="status-text">加载中...</span>
+                    </div>
+                    <div class="status-item" id="hk-market">
+                        <i class="fas fa-circle"></i> 港股
+                        <span class="status-text">加载中...</span>
+                    </div>
+                    <div class="status-item" id="taiwan-market">
+                        <i class="fas fa-circle"></i> MSCI中国台湾
+                        <span class="status-text">加载中...</span>
+                    </div>
+                    <div class="status-item" id="japan-market">
+                        <i class="fas fa-circle"></i> 日经225
+                        <span class="status-text">加载中...</span>
+                    </div>
+                </div>
+            </div>
+
+            <div class="market-group">
+                <div class="group-title">欧非中东</div>
+                <div class="status-group">
+                    <div class="status-item" id="uk-market">
+                        <i class="fas fa-circle"></i> 富时100
+                        <span class="status-text">加载中...</span>
+                    </div>
+                    <div class="status-item" id="german-market">
+                        <i class="fas fa-circle"></i> 德国DAX
+                        <span class="status-text">加载中...</span>
+                    </div>
+                    <div class="status-item" id="france-market">
+                        <i class="fas fa-circle"></i> 法国MOEX
+                        <span class="status-text">加载中...</span>
+                    </div>
+                </div>
+            </div>
+
+            <div class="market-group">
+                <div class="group-title">美洲市场</div>
+                <div class="status-group">
+                    <div class="status-item" id="us-market">
+                        <i class="fas fa-circle"></i> 美股
+                        <span class="status-text">加载中...</span>
+                    </div>
+                    <div class="status-item" id="nasdaq-market">
+                        <i class="fas fa-circle"></i> 纳指
+                        <span class="status-text">加载中...</span>
+                    </div>
+                    <div class="status-item" id="brazil-market">
+                        <i class="fas fa-circle"></i> 巴西
+                        <span class="status-text">加载中...</span>
+                    </div>
+                </div>
+            </div>
+
+            <div class="current-time" id="current-time">
+                当前时间: <span>{{ current_time }}</span>
+                <span class="refresh-time" id="refresh-time">刷新: 5:00</span>
+            </div>
+        </div>
+        <div class="ticker-news" id="ticker-container">
+            <div class="ticker-wrapper">
+                <div class="ticker-item">最新消息加载中...</div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+$(document).ready(function() {
+    // 快速分析按钮点击事件
+    $('#quick-analysis-btn').click(function() {
+        const stockCode = $('#quick-stock-code').val().trim();
+        const marketType = $('#quick-market-type').val();
+
+        if (!stockCode) {
+            alert('请输入股票代码');
+            return;
+        }
+
+        // 跳转到股票详情页
+        window.location.href = `/stock_detail/${stockCode}?market_type=${marketType}`;
+    });
+
+    // 回车键提交表单
+    $('#quick-stock-code').keypress(function(e) {
+        if (e.which === 13) {
+            $('#quick-analysis-btn').click();
+            return false;
+        }
+    });
+
+    // 加载最新新闻
+    loadLatestNews();
+
+    // 加载舆情热点
+    loadHotspots();
+
+    // 更新市场状态
+    updateMarketStatus();
+
+    // 启动滚动新闻
+    startTickerNews();
+
+    // 刷新按钮点击事件
+    $('.refresh-news-btn').click(function() {
+        loadLatestNews();
+    });
+
+    // 只看重要切换事件
+    $('#only-important').change(function() {
+        loadLatestNews();
+    });
+
+    // 定时刷新
+    setInterval(function() {
+        updateMarketStatus();
+        // 每5分钟自动刷新新闻
+        loadLatestNews(true); // 静默刷新
+        startTickerNews();
+    }, 300000); // 5分钟
+
+    // 每秒更新当前时间
+    setInterval(function() {
+        updateCurrentTime();
+    }, 1000);
+
+    // 每秒更新一次时间和倒计时
+    setInterval(function() {
+        updateCurrentTime();
+        updateRefreshCountdown();
+    }, 1000);
+
+
+});
+
+// 加载最新新闻函数
+function loadLatestNews(silent = false) {
+    if (!silent) {
+        $('#news-timeline').html(`
+            <div class="text-center py-5">
+                <div class="spinner-border text-primary" role="status">
+                    <span class="visually-hidden">Loading...</span>
+                </div>
+                <p class="mt-2">加载新闻中...</p>
+            </div>
+        `);
+    }
+
+    const onlyImportant = $('#only-important').is(':checked');
+
+    $.ajax({
+        url: '/api/latest_news',
+        method: 'GET',
+        data: {
+            days: 2,
+            limit: 500,
+            important: onlyImportant ? 1 : 0
+        },
+        success: function(response) {
+            if (response.success && response.news && response.news.length > 0) {
+                displayNewsTimeline(response.news);
+            } else {
+                if (!silent) {
+                    $('#news-timeline').html('<div class="alert alert-info">暂无最新新闻</div>');
+                }
+            }
+        },
+        error: function(err) {
+            console.error('获取新闻失败:', err);
+            if (!silent) {
+                $('#news-timeline').html('<div class="alert alert-danger">获取新闻失败，请稍后重试</div>');
+            }
+        }
+    });
+}
+
+// 加载舆情热点函数
+function loadHotspots() {
+    $('#hotspot-list').html(`
+        <div class="text-center py-5">
+            <div class="spinner-border text-primary" role="status">
+                <span class="visually-hidden">Loading...</span>
+            </div>
+            <p class="mt-2">加载热点中...</p>
+        </div>
+    `);
+
+    $.ajax({
+        url: '/api/latest_news',
+        method: 'GET',
+        data: {
+            days: 1,
+            limit: 10,
+            type: 'hotspot'
+        },
+        success: function(response) {
+            if (response.success && response.news && response.news.length > 0) {
+                displayHotspots(response.news);
+            } else {
+                $('#hotspot-list').html('<div class="alert alert-info">暂无舆情热点</div>');
+            }
+        },
+        error: function(err) {
+            console.error('获取热点失败:', err);
+            $('#hotspot-list').html('<div class="alert alert-danger">获取热点失败，请稍后重试</div>');
+        }
+    });
+}
+
+// 显示热点列表
+function displayHotspots(hotspots) {
+    if (hotspots.length === 0) {
+        $('#hotspot-list').html('<div class="alert alert-info">暂无舆情热点</div>');
+        return;
+    }
+
+    let hotspotsHtml = '<div class="hotspot-list">';
+
+    hotspots.forEach((item, index) => {
+        const rankClass = index < 3 ? 'rank-top' : '';
+        hotspotsHtml += `
+            <div class="hotspot-item">
+                <span class="hotspot-rank ${rankClass}">${index + 1}</span>
+                <span class="hotspot-title">${item.title || item.content}</span>
+            </div>
+        `;
+    });
+
+    hotspotsHtml += '</div>';
+
+    $('#hotspot-list').html(hotspotsHtml);
+}
+
+// 替换现有的displayNewsTimeline函数
+function displayNewsTimeline(newsList) {
+    if (newsList.length === 0) {
+        $('#news-timeline').html('<div class="alert alert-info">暂无新闻</div>');
+        return;
+    }
+    
+    let timelineHtml = '<div class="news-timeline-container">';
+    
+    // 首先按完整的日期时间排序，确保最新消息在最前面
+    newsList.sort((a, b) => {
+        // 构建完整的日期时间字符串 (YYYY-MM-DD HH:MM)
+        const dateTimeA = (a.date || '') + ' ' + (a.time || '00:00');
+        const dateTimeB = (b.date || '') + ' ' + (b.time || '00:00');
+        
+        // 转换为Date对象进行比较
+        const timeA = new Date(dateTimeA);
+        const timeB = new Date(dateTimeB);
+        
+        // 返回降序结果（最新的在前）
+        return timeB - timeA;
+    });
+    
+    // 按天和时间点分组
+    const newsGroups = {};
+    newsList.forEach(news => {
+        // 创建格式为"YYYY-MM-DD HH:MM"的键
+        const date = news.date || '';
+        const time = news.time || '00:00';
+        
+        // 显示用的键：日期+时间
+        const displayKey = `${date} ${time.substring(0, 5)}`;
+        
+        if (!newsGroups[displayKey]) {
+            newsGroups[displayKey] = [];
+        }
+        newsGroups[displayKey].push(news);
+    });
+    
+    // 获取并按时间降序排列所有组键
+    const sortedKeys = Object.keys(newsGroups).sort((a, b) => {
+        const timeA = new Date(a);
+        const timeB = new Date(b);
+        return timeB - timeA;
+    });
+    
+    // 生成时间线HTML
+    sortedKeys.forEach(displayKey => {
+        const newsItems = newsGroups[displayKey];
+        const parts = displayKey.split(' ');
+        const date = parts[0];
+        const time = parts[1];
+        
+        // 格式化显示日期（只在新的一天开始时显示）
+        const formattedDate = formatDate(date);
+        
+        timelineHtml += `
+            <div class="time-point">
+                <div class="time-label">${time}</div>
+                <div class="time-date">${formattedDate}</div>
+                <div class="news-items">
+        `;
+        
+        newsItems.forEach(news => {
+            let contentClass = '';
+            // 根据内容中是否含有特定关键词添加样式
+            if (news.content && (news.content.includes('增长') || news.content.includes('上涨') || news.content.includes('利好'))) {
+                contentClass = 'text-success';
+            } else if (news.content && (news.content.includes('下跌') || news.content.includes('下降') || news.content.includes('利空'))) {
+                contentClass = 'text-danger';
+            }
+            
+            timelineHtml += `
+                <div class="news-item">
+                    <div class="news-content ${contentClass}">${news.content || ''}</div>
+                </div>
+            `;
+        });
+        
+        timelineHtml += `
+                </div>
+            </div>
+        `;
+    });
+    
+    timelineHtml += '</div>';
+    
+    // 更新DOM
+    $('#news-timeline').html(timelineHtml);
+}
+
+// 日期格式化辅助函数
+function formatDate(dateStr) {
+    // 检查是否与当天日期相同
+    const today = new Date();
+    const todayStr = today.toISOString().split('T')[0];
+    
+    if (dateStr === todayStr) {
+        return '';
+    }
+    
+    // 昨天
+    const yesterday = new Date(today);
+    yesterday.setDate(yesterday.getDate() - 1);
+    const yesterdayStr = yesterday.toISOString().split('T')[0];
+    
+    if (dateStr === yesterdayStr) {
+        return '昨天';
+    }
+    
+    // 其他日期用中文格式
+    const date = new Date(dateStr);
+    return `${date.getMonth() + 1}月${date.getDate()}日`;
+}
+
+// 添加页面自动刷新功能
+let refreshCountdown = 300; // 5分钟倒计时（秒）
+
+// 更新市场状态
+function updateMarketStatus() {
+    const now = new Date();
+    const hours = now.getHours();
+    const minutes = now.getMinutes();
+    const weekday = now.getDay(); // 0为周日，6为周六
+
+    // 检查是否为工作日
+    const isWeekend = weekday === 0 || weekday === 6;
+
+    // 亚太市场时区
+    // A股状态 (9:30-11:30, 13:00-15:00)
+    let chinaStatus = { open: false, text: '未开市' };
+    if (!isWeekend && ((hours === 9 && minutes >= 30) || hours === 10 || (hours === 11 && minutes <= 30) ||
+        (hours >= 13 && hours < 15))) {
+        chinaStatus = { open: true, text: '交易中' };
+    }
+
+    // 港股状态 (9:30-12:00, 13:00-16:00)
+    let hkStatus = { open: false, text: '未开市' };
+    if (!isWeekend && ((hours === 9 && minutes >= 30) || hours === 10 || hours === 11 ||
+        (hours >= 13 && hours < 16))) {
+        hkStatus = { open: true, text: '交易中' };
+    }
+
+    // 台股状态 (9:00-13:30)
+    let taiwanStatus = { open: false, text: '未开市' };
+    if (!isWeekend && ((hours === 9) || hours === 10 || hours === 11 || hours === 12 ||
+        (hours === 13 && minutes <= 30))) {
+        taiwanStatus = { open: true, text: '交易中' };
+    }
+
+    // 日本股市 (9:00-11:30, 12:30-15:00)
+    let japanStatus = { open: false, text: '未开市' };
+    if (!isWeekend && ((hours === 9) || hours === 10 || (hours === 11 && minutes <= 30) ||
+        (hours === 12 && minutes >= 30) || hours === 13 || hours === 14)) {
+        japanStatus = { open: true, text: '交易中' };
+    }
+
+    // 欧洲市场 - 需要调整时区，这里是基于欧洲夏令时(UTC+2)与北京时间(UTC+8)相差6小时计算
+    // 英国股市 (伦敦，北京时间15:00-23:30)
+    let ukStatus = { open: false, text: '未开市' };
+    if (!isWeekend && ((hours >= 15 && hours < 23) || (hours === 23 && minutes <= 30))) {
+        ukStatus = { open: true, text: '交易中' };
+    }
+
+    // 德国股市 (法兰克福，北京时间15:00-23:30)
+    let germanStatus = { open: false, text: '未开市' };
+    if (!isWeekend && ((hours >= 15 && hours < 23) || (hours === 23 && minutes <= 30))) {
+        germanStatus = { open: true, text: '交易中' };
+    }
+
+    // 法国股市 (巴黎，北京时间15:00-23:30)
+    let franceStatus = { open: false, text: '未开市' };
+    if (!isWeekend && ((hours >= 15 && hours < 23) || (hours === 23 && minutes <= 30))) {
+        franceStatus = { open: true, text: '交易中' };
+    }
+
+    // 美洲市场
+    // 美股状态 (纽约，北京时间21:30-4:00)
+    let usStatus = { open: false, text: '未开市' };
+    if ((hours >= 21 && minutes >= 30) || hours >= 22 || hours < 4) {
+        // 检查美股的工作日 (当北京时间是周六早上，美国还是周五)
+        const usDay = hours < 12 ? (weekday === 6 ? 5 : weekday - 1) : weekday;
+        if (usDay !== 0 && usDay !== 6) {
+            usStatus = { open: true, text: '交易中' };
+        }
+    }
+
+    // 纳斯达克与美股相同
+    let nasdaqStatus = usStatus;
+
+    // 巴西股市 (圣保罗，北京时间20:30-3:00)
+    let brazilStatus = { open: false, text: '未开市' };
+    if ((hours >= 20 && minutes >= 30) || hours >= 21 || hours < 3) {
+        const brazilDay = hours < 12 ? (weekday === 6 ? 5 : weekday - 1) : weekday;
+        if (brazilDay !== 0 && brazilDay !== 6) {
+            brazilStatus = { open: true, text: '交易中' };
+        }
+    }
+
+    // 更新DOM
+    updateMarketStatusUI('china-market', chinaStatus);
+    updateMarketStatusUI('hk-market', hkStatus);
+    updateMarketStatusUI('taiwan-market', taiwanStatus);
+    updateMarketStatusUI('japan-market', japanStatus);
+
+    updateMarketStatusUI('uk-market', ukStatus);
+    updateMarketStatusUI('german-market', germanStatus);
+    updateMarketStatusUI('france-market', franceStatus);
+
+    updateMarketStatusUI('us-market', usStatus);
+    updateMarketStatusUI('nasdaq-market', nasdaqStatus);
+    updateMarketStatusUI('brazil-market', brazilStatus);
+}
+
+// 更新市场状态UI
+function updateMarketStatusUI(elementId, status) {
+    const element = $(`#${elementId}`);
+    const iconElement = element.find('i');
+    const textElement = element.find('.status-text');
+
+    if (status.open) {
+        iconElement.removeClass('status-closed').addClass('status-open');
+    } else {
+        iconElement.removeClass('status-open').addClass('status-closed');
+    }
+
+    textElement.text(status.text);
+}
+
+// 更新倒计时
+function updateRefreshCountdown() {
+    refreshCountdown--;
+
+    if (refreshCountdown <= 0) {
+        // 重新加载页面
+        window.location.reload();
+        return;
+    }
+
+    const minutes = Math.floor(refreshCountdown / 60);
+    const seconds = refreshCountdown % 60;
+    $('#refresh-time').text(`刷新: ${minutes}:${seconds < 10 ? '0' + seconds : seconds}`);
+}
+
+// 更新当前时间
+function updateCurrentTime() {
+    const now = new Date();
+    const timeString = now.toLocaleTimeString('zh-CN', { hour12: false });
+    $('#current-time span').text(timeString);
+}
+
+// 启动滚动新闻
+function startTickerNews() {
+    $.ajax({
+        url: '/api/latest_news',
+        method: 'GET',
+        data: {
+            days: 1,
+            limit: 10
+        },
+        success: function(response) {
+            if (response.success && response.news && response.news.length > 0) {
+                displayTickerNews(response.news);
+            } else {
+                $('#ticker-container .ticker-wrapper').html('<div class="ticker-item">暂无最新消息</div>');
+            }
+        },
+        error: function(err) {
+            console.error('获取滚动新闻失败:', err);
+            $('#ticker-container .ticker-wrapper').html('<div class="ticker-item">获取最新消息失败</div>');
+        }
+    });
+}
+
+// 显示滚动新闻
+function displayTickerNews(newsList) {
+    if (newsList.length === 0) {
+        $('#ticker-container .ticker-wrapper').html('<div class="ticker-item">暂无最新消息</div>');
+        return;
+    }
+
+    let tickerItems = '';
+
+    newsList.forEach(news => {
+        tickerItems += `<div class="ticker-item">${news.content || ''}</div>`;
+    });
+
+    $('#ticker-container .ticker-wrapper').html(tickerItems);
+
+    // 启动滚动效果
+    const tickerWidth = $('#ticker-container').width();
+    const wrapper = $('#ticker-container .ticker-wrapper');
+
+    wrapper.width(tickerWidth * 2);
+    wrapper.css('animation', 'ticker 30s linear infinite');
+}
+</script>
+{% endblock %}

app/web/templates/industry_analysis.html

@@ -0,0 +1,1135 @@
+{% extends "layout.html" %}
+
+{% block title %}行业分析 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-3">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">行业资金流向分析</h5>
+                </div>
+                <div class="card-body py-2">
+                    <form id="industry-form" class="row g-2">
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">周期</span>
+                                <select class="form-select" id="fund-flow-period">
+                                    <option value="即时" selected>即时</option>
+                                    <option value="3日排行">3日排行</option>
+                                    <option value="5日排行">5日排行</option>
+                                    <option value="10日排行">10日排行</option>
+                                    <option value="20日排行">20日排行</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group input-group-sm">
+                                <span class="input-group-text">行业</span>
+                                <select class="form-select" id="industry-selector">
+                                    <option value="">-- 选择行业 --</option>
+                                    <!-- 行业选项将通过JS动态填充 -->
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-2">
+                            <button type="submit" class="btn btn-primary btn-sm w-100">
+                                <i class="fas fa-search"></i> 分析
+                            </button>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="loading-panel" class="text-center py-5" style="display: none;">
+        <div class="spinner-border text-primary" role="status">
+            <span class="visually-hidden">Loading...</span>
+        </div>
+        <p class="mt-3 mb-0">正在获取行业数据...</p>
+    </div>
+
+    <!-- 行业资金流向概览 -->
+    <div id="industry-overview" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2 d-flex justify-content-between align-items-center">
+                    <h5 class="mb-0">行业资金流向概览</h5>
+                    <div class="d-flex">
+                        <span id="period-badge" class="badge bg-primary ms-2">即时</span>
+                        <button class="btn btn-sm btn-outline-primary ms-2" id="export-btn">
+                            <i class="fas fa-download"></i> 导出数据
+                        </button>
+                    </div>
+                </div>
+                <div class="card-body p-0">
+                    <div class="table-responsive">
+                        <table class="table table-sm table-striped table-hover mb-0">
+                            <thead>
+                                <tr>
+                                    <th>序号</th>
+                                    <th>行业</th>
+                                    <th>行业指数</th>
+                                    <th>涨跌幅</th>
+                                    <th>流入资金(亿)</th>
+                                    <th>流出资金(亿)</th>
+                                    <th>净额(亿)</th>
+                                    <th>公司家数</th>
+                                    <th>领涨股</th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="industry-table">
+                                <!-- 行业资金流向数据将在JS中动态填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 行业详细分析 -->
+    <div id="industry-detail" class="row g-3 mb-3" style="display: none;">
+        <div class="col-md-6">
+            <div class="card h-100">
+                <div class="card-header py-2">
+                    <h5 id="industry-name" class="mb-0">行业详情</h5>
+                </div>
+                <div class="card-body">
+                    <div class="row">
+                        <div class="col-md-6">
+                            <h6>行业概况</h6>
+                            <p><span class="text-muted">行业指数:</span> <span id="industry-index" class="fw-bold"></span></p>
+                            <p><span class="text-muted">涨跌幅:</span> <span id="industry-change" class="fw-bold"></span></p>
+                            <p><span class="text-muted">公司家数:</span> <span id="industry-company-count" class="fw-bold"></span></p>
+                        </div>
+                        <div class="col-md-6">
+                            <h6>资金流向</h6>
+                            <p><span class="text-muted">流入资金:</span> <span id="industry-inflow" class="fw-bold"></span></p>
+                            <p><span class="text-muted">流出资金:</span> <span id="industry-outflow" class="fw-bold"></span></p>
+                            <p><span class="text-muted">净额:</span> <span id="industry-net-flow" class="fw-bold"></span></p>
+                        </div>
+                    </div>
+                    <div class="mt-3">
+                        <div id="industry-flow-chart" style="height: 200px;"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <div class="col-md-6">
+            <div class="card h-100">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">行业评分</h5>
+                </div>
+                <div class="card-body">
+                    <div class="row">
+                        <div class="col-md-5">
+                            <div id="industry-score-chart" style="height: 150px;"></div>
+                            <h4 id="industry-score" class="text-center mt-2">--</h4>
+                            <p class="text-muted text-center">综合评分</p>
+                        </div>
+                        <div class="col-md-7">
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>技术面</span>
+                                    <span id="technical-score">--/40</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="technical-progress" class="progress-bar bg-info" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>基本面</span>
+                                    <span id="fundamental-score">--/40</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="fundamental-progress" class="progress-bar bg-success" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>资金面</span>
+                                    <span id="capital-flow-score">--/20</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="capital-flow-progress" class="progress-bar bg-warning" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                    <div class="mt-3">
+                        <h6>投资建议</h6>
+                        <p id="industry-recommendation" class="mb-0"></p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 行业成分股表现 -->
+    <div id="industry-stocks" class="row g-3 mb-3" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">行业成分股表现</h5>
+                </div>
+                <div class="card-body p-0">
+                    <div class="table-responsive">
+                        <table class="table table-sm table-striped table-hover mb-0">
+                            <thead>
+                                <tr>
+                                    <th>代码</th>
+                                    <th>名称</th>
+                                    <th>最新价</th>
+                                    <th>涨跌幅</th>
+                                    <th>成交量</th>
+                                    <th>成交额(万)</th>
+                                    <th>换手率</th>
+                                    <th>评分</th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="industry-stocks-table">
+                                <!-- 行业成分股数据将在JS中动态填充 -->
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- 行业对比分析 -->
+    <div class="row g-3 mb-3">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header py-2">
+                    <h5 class="mb-0">行业对比分析</h5>
+                </div>
+                <div class="card-body">
+                    <ul class="nav nav-tabs" id="industry-compare-tabs" role="tablist">
+                        <li class="nav-item" role="presentation">
+                            <button class="nav-link active" id="fund-flow-tab" data-bs-toggle="tab" data-bs-target="#fund-flow" type="button" role="tab" aria-controls="fund-flow" aria-selected="true">资金流向</button>
+                        </li>
+                        <li class="nav-item" role="presentation">
+                            <button class="nav-link" id="performance-tab" data-bs-toggle="tab" data-bs-target="#performance" type="button" role="tab" aria-controls="performance" aria-selected="false">行业涨跌幅</button>
+                        </li>
+                    </ul>
+                    <div class="tab-content mt-3" id="industry-compare-tabs-content">
+                        <div class="tab-pane fade show active" id="fund-flow" role="tabpanel" aria-labelledby="fund-flow-tab">
+                            <div class="row">
+                                <div class="col-md-6">
+                                    <h6>资金净流入前10行业</h6>
+                                    <div id="top-inflow-chart" style="height: 300px;"></div>
+                                </div>
+                                <div class="col-md-6">
+                                    <h6>资金净流出前10行业</h6>
+                                    <div id="top-outflow-chart" style="height: 300px;"></div>
+                                </div>
+                            </div>
+                        </div>
+                        <div class="tab-pane fade" id="performance" role="tabpanel" aria-labelledby="performance-tab">
+                            <div class="row">
+                                <div class="col-md-6">
+                                    <h6>涨幅前10行业</h6>
+                                    <div id="top-gainers-chart" style="height: 300px;"></div>
+                                </div>
+                                <div class="col-md-6">
+                                    <h6>跌幅前10行业</h6>
+                                    <div id="top-losers-chart" style="height: 300px;"></div>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+
+    $(document).ready(function() {
+    // 加载行业资金流向数据
+    loadIndustryFundFlow();
+
+    $('#industry-form').submit(function(e) {
+        e.preventDefault();
+
+        // 获取选择的行业
+        const industry = $('#industry-selector').val();
+
+        if (!industry) {
+            showError('请选择行业名称');
+            return;
+        }
+
+        // 分析行业
+        loadIndustryDetail(industry);
+    });
+
+    // 资金流向周期切换
+    $('#fund-flow-period').change(function() {
+        const period = $(this).val();
+        loadIndustryFundFlow(period);
+    });
+
+    // 导出按钮点击事件
+    $('#export-btn').click(function() {
+        exportToCSV();
+    });
+    });
+
+    function analyzeIndustry(industry) {
+        $('#loading-panel').show();
+        $('#industry-result').hide();
+
+        // 1. 获取行业详情
+        $.ajax({
+            url: `/api/industry_detail?industry=${encodeURIComponent(industry)}`,
+            type: 'GET',
+            success: function(industryDetail) {
+                console.log("Industry detail loaded successfully:", industryDetail);
+
+                // 2. 获取行业成分股
+                $.ajax({
+                    url: `/api/industry_stocks?industry=${encodeURIComponent(industry)}`,
+                    type: 'GET',
+                    success: function(stocksResponse) {
+                        console.log("Industry stocks loaded successfully:", stocksResponse);
+
+                        $('#loading-panel').hide();
+
+                        // 渲染行业详情和成分股
+                        renderIndustryDetail(industryDetail);
+                        renderIndustryStocks(stocksResponse);
+
+                        $('#industry-detail').show();
+                        $('#industry-stocks').show();
+                    },
+                    error: function(xhr, status, error) {
+                        $('#loading-panel').hide();
+                        console.error("Error loading industry stocks:", error);
+                        showError('获取行业成分股失败: ' + (xhr.responseJSON?.error || error));
+                    }
+                });
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                console.error("Error loading industry detail:", error);
+                showError('获取行业详情失败: ' + (xhr.responseJSON?.error || error));
+            }
+        });
+    }
+
+    // 加载行业资金流向数据
+    function loadIndustryFundFlow(period = '即时') {
+        $('#loading-panel').show();
+        $('#industry-overview').hide();
+        $('#industry-detail').hide();
+        $('#industry-stocks').hide();
+
+        $.ajax({
+            url: `/api/industry_fund_flow?symbol=${encodeURIComponent(period)}`,
+            type: 'GET',
+            dataType: 'json',
+            success: function(response) {
+                if (Array.isArray(response) && response.length > 0) {
+                    renderIndustryFundFlow(response, period);
+                    populateIndustrySelector(response);
+
+                    // 加载行业对比数据
+                    loadIndustryCompare();
+
+                    $('#loading-panel').hide();
+                    $('#industry-overview').show();
+                } else {
+                    showError('获取行业资金流向数据失败：返回数据为空');
+                    $('#loading-panel').hide();
+                }
+            },
+            error: function(xhr, status, error) {
+                $('#loading-panel').hide();
+                let errorMsg = '获取行业资金流向数据失败';
+                if (xhr.responseJSON && xhr.responseJSON.error) {
+                    errorMsg += ': ' + xhr.responseJSON.error;
+                } else if (error) {
+                    errorMsg += ': ' + error;
+                }
+                showError(errorMsg);
+            }
+        });
+    }
+
+
+    // 统一的行业详情加载函数
+    function loadIndustryDetail(industry) {
+        console.log(`Loading industry detail for: ${industry}`);
+        $('#loading-panel').show();
+        $('#industry-overview').hide();
+        $('#industry-detail').hide();
+        $('#industry-stocks').hide();
+
+        // 并行加载行业详情和行业成分股
+        $.when(
+            // 获取行业详情
+            $.ajax({
+                url: `/api/industry_detail?industry=${encodeURIComponent(industry)}`,
+                type: 'GET',
+                dataType: 'json'
+            }),
+            // 获取行业成分股
+            $.ajax({
+                url: `/api/industry_stocks?industry=${encodeURIComponent(industry)}`,
+                type: 'GET',
+                dataType: 'json'
+            })
+        ).done(function(detailResponse, stocksResponse) {
+            // 处理行业详情数据
+            const industryData = detailResponse[0];
+
+            // 处理行业成分股数据
+            const stocksData = stocksResponse[0];
+
+            console.log("Industry detail loaded:", industryData);
+            console.log("Industry stocks loaded:", stocksData);
+
+            renderIndustryDetail(industryData);
+            renderIndustryStocks(stocksData);
+
+            $('#loading-panel').hide();
+            $('#industry-detail').show();
+            $('#industry-stocks').show();
+        }).fail(function(jqXHR, textStatus, errorThrown) {
+            $('#loading-panel').hide();
+            console.error("Error loading industry data:", textStatus, errorThrown);
+            let errorMsg = '获取行业数据失败';
+            try {
+                if (jqXHR.responseJSON && jqXHR.responseJSON.error) {
+                    errorMsg += ': ' + jqXHR.responseJSON.error;
+                } else if (errorThrown) {
+                    errorMsg += ': ' + errorThrown;
+                }
+            } catch (e) {
+                console.error("Error parsing error response:", e);
+            }
+            showError(errorMsg);
+        });
+    }
+
+    // 加载行业对比数据
+    function loadIndustryCompare() {
+        $.ajax({
+            url: '/api/industry_compare',
+            type: 'GET',
+            dataType: 'json',
+            success: function(response) {
+                try {
+                    if (response && response.results) {
+                        // 按资金净流入排序
+                        const sortedByNetFlow = [...response.results]
+                            .filter(item => item.netFlow !== undefined)
+                            .sort((a, b) => parseFloat(b.netFlow || 0) - parseFloat(a.netFlow || 0));
+
+                        // 按涨跌幅排序
+                        const sortedByChange = [...response.results]
+                            .filter(item => item.change !== undefined)
+                            .sort((a, b) => parseFloat(b.change || 0) - parseFloat(a.change || 0));
+
+                        // 资金净流入前10行业
+                        const topInflow = sortedByNetFlow.slice(0, 10);
+                        renderBarChart('top-inflow-chart',
+                                    topInflow.map(item => item.industry),
+                                    topInflow.map(item => parseFloat(item.netFlow || 0)),
+                                    '资金净流入(亿)',
+                                    '#00E396');
+
+                        // 资金净流出前10行业
+                        const bottomInflow = [...sortedByNetFlow].reverse().slice(0, 10);
+                        renderBarChart('top-outflow-chart',
+                                    bottomInflow.map(item => item.industry),
+                                    bottomInflow.map(item => Math.abs(parseFloat(item.netFlow || 0))),
+                                    '资金净流出(亿)',
+                                    '#FF4560');
+
+                        // 涨幅前10行业
+                        const topGainers = sortedByChange.slice(0, 10);
+                        renderBarChart('top-gainers-chart',
+                                    topGainers.map(item => item.industry),
+                                    topGainers.map(item => parseFloat(item.change || 0)),
+                                    '涨幅(%)',
+                                    '#00E396');
+
+                        // 跌幅前10行业
+                        const topLosers = [...sortedByChange].reverse().slice(0, 10);
+                        renderBarChart('top-losers-chart',
+                                    topLosers.map(item => item.industry),
+                                    topLosers.map(item => Math.abs(parseFloat(item.change || 0))),
+                                    '跌幅(%)',
+                                    '#FF4560');
+                    }
+                } catch (e) {
+                    console.error("Error processing industry comparison data:", e);
+                }
+            },
+            error: function(xhr, status, error) {
+                console.error('获取行业对比数据失败:', error);
+            }
+        });
+    }
+
+    // 渲染行业资金流向表格
+    function renderIndustryFundFlow(data, period) {
+        $('#period-badge').text(period);
+
+        let html = '';
+        if (data.length === 0) {
+            html = '<tr><td colspan="10" class="text-center">暂无数据</td></tr>';
+        } else {
+            data.forEach((item, index) => {
+                const changeClass = parseFloat(item.change) >= 0 ? 'trend-up' : 'trend-down';
+                const changeIcon = parseFloat(item.change) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                const netFlowClass = parseFloat(item.netFlow) >= 0 ? 'trend-up' : 'trend-down';
+                const netFlowIcon = parseFloat(item.netFlow) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${item.rank}</td>
+                        <td>
+                            <a href="javascript:void(0)" onclick="loadIndustryDetail('${item.industry}')" class="industry-link">
+                                ${item.industry}
+                            </a>
+                        </td>
+                        <td>${formatNumber(item.index, 2)}</td>
+                        <td class="${changeClass}">${changeIcon} ${item.change}%</td>
+                        <td>${formatNumber(item.inflow, 2)}</td>
+                        <td>${formatNumber(item.outflow, 2)}</td>
+                        <td class="${netFlowClass}">${netFlowIcon} ${formatNumber(item.netFlow, 2)}</td>
+                        <td>${item.companyCount}</td>
+                        <td>${item.leadingStock || '-'}</td>
+                        <td>
+                            <button class="btn btn-sm btn-outline-primary" onclick="loadIndustryDetail('${item.industry}')">
+                                <i class="fas fa-search"></i>
+                            </button>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#industry-table').html(html);
+    }
+
+    // 渲染行业详情
+    function renderIndustryDetail(data) {
+        if (!data) {
+            console.error("renderIndustryDetail: No data provided");
+            return;
+        }
+
+        console.log("Rendering industry detail:", data);
+
+        // 设置基本信息
+        $('#industry-name').text(data.industry);
+
+        // 设置行业评分
+        const scoreClass = getScoreColorClass(data.score);
+        $('#industry-score').text(data.score).removeClass().addClass(scoreClass);
+
+        // 设置技术面、基本面、资金面分数 (模拟分数)
+        const technicalScore = Math.round(data.score * 0.4);
+        const fundamentalScore = Math.round(data.score * 0.4);
+        const capitalFlowScore = Math.round(data.score * 0.2);
+
+        $('#technical-score').text(`${technicalScore}/40`);
+        $('#fundamental-score').text(`${fundamentalScore}/40`);
+        $('#capital-flow-score').text(`${capitalFlowScore}/20`);
+
+        $('#technical-progress').css('width', `${technicalScore / 40 * 100}%`);
+        $('#fundamental-progress').css('width', `${fundamentalScore / 40 * 100}%`);
+        $('#capital-flow-progress').css('width', `${capitalFlowScore / 20 * 100}%`);
+
+        // 设置行业基本信息
+        $('#industry-index').text(formatNumber(data.index, 2));
+        $('#industry-company-count').text(data.companyCount);
+
+        // 设置涨跌幅
+        const changeClass = parseFloat(data.change) >= 0 ? 'trend-up' : 'trend-down';
+        const changeIcon = parseFloat(data.change) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+        $('#industry-change').html(`<span class="${changeClass}">${changeIcon} ${data.change}%</span>`);
+
+        // 设置资金流向
+        $('#industry-inflow').text(formatNumber(data.inflow, 2) + ' 亿');
+        $('#industry-outflow').text(formatNumber(data.outflow, 2) + ' 亿');
+
+        const netFlowClass = parseFloat(data.netFlow) >= 0 ? 'trend-up' : 'trend-down';
+        const netFlowIcon = parseFloat(data.netFlow) >= 0 ? '<i class="fas fa-arrow-up"></i>' : '<i class="fas fa-arrow-down"></i>';
+        $('#industry-net-flow').html(`<span class="${netFlowClass}">${netFlowIcon} ${formatNumber(data.netFlow, 2)} 亿</span>`);
+
+        // 设置投资建议
+        $('#industry-recommendation').text(data.recommendation);
+
+        // 绘制行业评分图表
+        renderIndustryScoreChart(data.score);
+
+        // 绘制资金流向图表
+        renderIndustryFlowChart(data.flowHistory);
+    }
+
+
+    // 渲染行业成分股表格
+    function renderIndustryStocks(data) {
+        if (!data) {
+            console.error("renderIndustryStocks: No data provided");
+            return;
+        }
+
+        console.log("Rendering industry stocks:", data);
+
+        let html = '';
+
+        if (!Array.isArray(data) || data.length === 0) {
+            html = '<tr><td colspan="9" class="text-center">暂无成分股数据</td></tr>';
+        } else {
+            data.forEach(stock => {
+                const changeClass = parseFloat(stock.change) >= 0 ? 'trend-up' : 'trend-down';
+                const changeIcon = parseFloat(stock.change) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${stock.code}</td>
+                        <td>${stock.name}</td>
+                        <td>${formatNumber(stock.price, 2)}</td>
+                        <td class="${changeClass}">${changeIcon} ${formatNumber(stock.change, 2)}%</td>
+                        <td>${formatNumber(stock.volume, 0)}</td>
+                        <td>${formatMoney(stock.turnover)}</td>
+                        <td>${formatNumber(stock.turnover_rate || stock.turnoverRate, 2)}%</td>
+                        <td>${stock.score ? formatNumber(stock.score, 0) : '-'}</td>
+                        <td>
+                            <a href="/stock_detail/${stock.code}" class="btn btn-sm btn-outline-primary">
+                                <i class="fas fa-chart-line"></i>
+                            </a>
+                        </td>
+                    </tr>
+                `;
+            });
+        }
+
+        $('#industry-stocks-table').html(html);
+    }
+
+    function renderCapitalFlowChart(flowHistory) {
+
+        // 添加数据检查
+        if (!flowHistory || !Array.isArray(flowHistory) || flowHistory.length === 0) {
+            // 如果没有历史数据，显示提示信息
+            document.querySelector("#industry-flow-chart").innerHTML =
+                '<div class="text-center text-muted py-5">暂无资金流向历史数据</div>';
+            return;
+        }
+
+        const dates = flowHistory.map(item => item.date);
+        const netFlows = flowHistory.map(item => parseFloat(item.netFlow));
+        const changes = flowHistory.map(item => parseFloat(item.change));
+
+        // 确保所有数组都有值
+        if (dates.length === 0 || netFlows.length === 0 || changes.length === 0) {
+            document.querySelector("#industry-flow-chart").innerHTML =
+                '<div class="text-center text-muted py-5">资金流向数据格式不正确</div>';
+            return;
+        }
+
+        const options = {
+            series: [
+                {
+                    name: '净流入(亿)',
+                    type: 'column',
+                    data: netFlows
+                },
+                {
+                    name: '涨跌幅(%)',
+                    type: 'line',
+                    data: changes
+                }
+            ],
+            chart: {
+                height: 265,
+                type: 'line',
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                bar: {
+                    borderRadius: 2,
+                    dataLabels: {
+                        position: 'top'
+                    }
+                }
+            },
+            dataLabels: {
+                enabled: false
+            },
+            stroke: {
+                width: [0, 3]
+            },
+            colors: ['#0d6efd', '#dc3545'],
+            xaxis: {
+                categories: dates,
+                labels: {
+                    formatter: function(value) {
+                        return value.slice(5); // 只显示月-日
+                    }
+                }
+            },
+            yaxis: [
+                {
+                    title: {
+                        text: '净流入(亿)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            return val.toFixed(2);
+                        }
+                    }
+                },
+                {
+                    opposite: true,
+                    title: {
+                        text: '涨跌幅(%)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            return val.toFixed(2);
+                        }
+                    }
+                }
+            ],
+            tooltip: {
+                shared: true,
+                intersect: false,
+                y: {
+                    formatter: function(value, { seriesIndex }) {
+                        if (seriesIndex === 0) {
+                            return value.toFixed(2) + ' 亿';
+                        }
+                        return value.toFixed(2) + '%';
+                    }
+                }
+            },
+            legend: {
+                position: 'top'
+            }
+        };
+
+        // 清除任何现有图表
+        document.querySelector("#industry-flow-chart").innerHTML = '';
+        const chart = new ApexCharts(document.querySelector("#industry-flow-chart"), options);
+        chart.render();
+    }
+
+    // 填充行业选择器
+    function populateIndustrySelector(data) {
+        let options = '<option value="">-- 选择行业 --</option>';
+        const industries = data.map(item => item.industry);
+
+        industries.forEach(industry => {
+            options += `<option value="${industry}">${industry}</option>`;
+        });
+
+        $('#industry-selector').html(options);
+    }
+
+    // 评分颜色类
+    function getScoreColorClass(score) {
+        if (score >= 80) return 'badge rounded-pill bg-success';
+        if (score >= 60) return 'badge rounded-pill bg-primary';
+        if (score >= 40) return 'badge rounded-pill bg-warning text-dark';
+        return 'badge rounded-pill bg-danger';
+    }
+
+    // 获取评分颜色
+    function getScoreColor(score) {
+        if (score >= 80) return '#28a745'; // 绿色
+        if (score >= 60) return '#007bff'; // 蓝色
+        if (score >= 40) return '#ffc107'; // 黄色
+        return '#dc3545'; // 红色
+    }
+
+    // 格式化金额（单位：万元）
+    function formatMoney(value) {
+        if (value === undefined || value === null) {
+            return '--';
+        }
+
+        value = parseFloat(value);
+        if (isNaN(value)) {
+            return '--';
+        }
+
+        if (value >= 100000000) {
+            return (value / 100000000).toFixed(2) + ' 亿';
+        } else if (value >= 10000) {
+            return (value / 10000).toFixed(2) + ' 万';
+        } else {
+            return value.toFixed(2);
+        }
+    }
+
+    // 渲染行业资金流向图表
+    function renderIndustryFlowChart(data) {
+        const options = {
+            series: [
+                {
+                    name: '流入资金',
+                    data: data.flowHistory.map(item => item.inflow)
+                },
+                {
+                    name: '流出资金',
+                    data: data.flowHistory.map(item => item.outflow)
+                },
+                {
+                    name: '净流入',
+                    data: data.flowHistory.map(item => item.netFlow)
+                }
+            ],
+            chart: {
+                type: 'bar',
+                height: 200,
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                bar: {
+                    horizontal: false,
+                    columnWidth: '55%',
+                    endingShape: 'rounded'
+                },
+            },
+            dataLabels: {
+                enabled: false
+            },
+            stroke: {
+                show: true,
+                width: 2,
+                colors: ['transparent']
+            },
+            xaxis: {
+                categories: data.flowHistory.map(item => item.date)
+            },
+            yaxis: {
+                title: {
+                    text: '亿元'
+                }
+            },
+            fill: {
+                opacity: 1
+            },
+            tooltip: {
+                y: {
+                    formatter: function(val) {
+                        return val + " 亿元";
+                    }
+                }
+            },
+            colors: ['#00E396', '#FF4560', '#008FFB']
+        };
+
+        const chart = new ApexCharts(document.querySelector("#industry-flow-chart"), options);
+        chart.render();
+    }
+
+    // 绘制行业评分图表
+    function renderIndustryScoreChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 150,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        show: false
+                    }
+                }
+            },
+            colors: [getScoreColor(score)],
+            stroke: {
+                lineCap: 'round'
+            }
+        };
+
+        // 清除旧图表并创建新图表
+        $('#industry-score-chart').empty();
+        const chart = new ApexCharts(document.querySelector("#industry-score-chart"), options);
+        chart.render();
+    }
+
+    // 绘制行业资金流向图表
+    function renderIndustryFlowChart(flowHistory) {
+        if (!flowHistory || !Array.isArray(flowHistory) || flowHistory.length === 0) {
+            console.error("renderIndustryFlowChart: Invalid flow history data");
+            return;
+        }
+
+        console.log("Rendering flow chart with data:", flowHistory);
+
+        const dates = flowHistory.map(item => item.date);
+        const netFlows = flowHistory.map(item => parseFloat(item.netFlow));
+        const changes = flowHistory.map(item => parseFloat(item.change));
+
+        const options = {
+            series: [
+                {
+                    name: '净流入(亿)',
+                    type: 'column',
+                    data: netFlows
+                },
+                {
+                    name: '涨跌幅(%)',
+                    type: 'line',
+                    data: changes
+                }
+            ],
+            chart: {
+                height: 200,
+                type: 'line',
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                bar: {
+                    borderRadius: 2,
+                    dataLabels: {
+                        position: 'top'
+                    }
+                }
+            },
+            dataLabels: {
+                enabled: false
+            },
+            stroke: {
+                width: [0, 3]
+            },
+            colors: ['#0d6efd', '#dc3545'],
+            xaxis: {
+                categories: dates,
+                labels: {
+                    formatter: function(value) {
+                        // Only show month-day if it's a date string
+                        if (typeof value === 'string' && value.includes('-')) {
+                            return value.slice(5); // 只显示月-日
+                        }
+                        return value;
+                    }
+                }
+            },
+            yaxis: [
+                {
+                    title: {
+                        text: '净流入(亿)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            return val.toFixed(2);
+                        }
+                    }
+                },
+                {
+                    opposite: true,
+                    title: {
+                        text: '涨跌幅(%)',
+                        style: {
+                            fontSize: '12px'
+                        }
+                    },
+                    labels: {
+                        formatter: function(val) {
+                            return val.toFixed(2);
+                        }
+                    }
+                }
+            ],
+            tooltip: {
+                shared: true,
+                intersect: false,
+                y: {
+                    formatter: function(value, { seriesIndex }) {
+                        if (seriesIndex === 0) {
+                            return value.toFixed(2) + ' 亿';
+                        }
+                        return value.toFixed(2) + '%';
+                    }
+                }
+            },
+            legend: {
+                position: 'top'
+            }
+        };
+
+        // 清除旧图表并创建新图表
+        $('#industry-flow-chart').empty();
+        try {
+            const chart = new ApexCharts(document.querySelector("#industry-flow-chart"), options);
+            chart.render();
+        } catch (e) {
+            console.error("Error rendering flow chart:", e);
+        }
+    }
+
+    // 渲染行业对比图表
+    function renderIndustryCompareCharts(data) {
+        // 按资金净流入排序
+        const sortedByNetFlow = [...data].sort((a, b) => b.netFlow - a.netFlow);
+
+        // 资金净流入前10
+        const topInflow = sortedByNetFlow.slice(0, 10);
+        renderBarChart('top-inflow-chart', topInflow.map(item => item.industry), topInflow.map(item => item.netFlow), '资金净流入(亿元)', '#00E396');
+
+        // 资金净流出前10
+        const bottomInflow = [...sortedByNetFlow].reverse().slice(0, 10);
+        renderBarChart('top-outflow-chart', bottomInflow.map(item => item.industry), bottomInflow.map(item => Math.abs(item.netFlow)), '资金净流出(亿元)', '#FF4560');
+
+        // 按涨跌幅排序
+        const sortedByChange = [...data].sort((a, b) => parseFloat(b.change) - parseFloat(a.change));
+
+        // 涨幅前10
+        const topGainers = sortedByChange.slice(0, 10);
+        renderBarChart('top-gainers-chart', topGainers.map(item => item.industry), topGainers.map(item => parseFloat(item.change)), '涨幅(%)', '#00E396');
+
+        // 跌幅前10
+        const topLosers = [...sortedByChange].reverse().slice(0, 10);
+        renderBarChart('top-losers-chart', topLosers.map(item => item.industry), topLosers.map(item => Math.abs(parseFloat(item.change))), '跌幅(%)', '#FF4560');
+    }
+
+    // 通用水平条形图
+    function renderBarChart(elementId, categories, data, title, color) {
+        if (!categories || !data || categories.length === 0 || data.length === 0) {
+            console.error(`renderBarChart: Invalid data for ${elementId}`);
+            return;
+        }
+
+        const options = {
+            series: [{
+                name: title,
+                data: data
+            }],
+            chart: {
+                type: 'bar',
+                height: 300,
+                toolbar: {
+                    show: false
+                }
+            },
+            plotOptions: {
+                bar: {
+                    horizontal: true,
+                    dataLabels: {
+                        position: 'top',
+                    },
+                }
+            },
+            dataLabels: {
+                enabled: true,
+                offsetX: -6,
+                style: {
+                    fontSize: '12px',
+                    colors: ['#fff']
+                },
+                formatter: function(val) {
+                    return val.toFixed(2);
+                }
+            },
+            stroke: {
+                show: true,
+                width: 1,
+                colors: ['#fff']
+            },
+            xaxis: {
+                categories: categories
+            },
+            yaxis: {
+                title: {
+                    text: title
+                }
+            },
+            fill: {
+                opacity: 1
+            },
+            colors: [color]
+        };
+
+        // 清除旧图表并创建新图表
+        $(`#${elementId}`).empty();
+        try {
+            const chart = new ApexCharts(document.querySelector(`#${elementId}`), options);
+            chart.render();
+        } catch (e) {
+            console.error(`Error rendering chart ${elementId}:`, e);
+        }
+    }
+
+    // 导出CSV
+    function exportToCSV() {
+        // 获取表格数据
+        const table = document.querySelector('#industry-overview table');
+        let csv = [];
+        let rows = table.querySelectorAll('tr');
+
+        for (let i = 0; i < rows.length; i++) {
+            let row = [], cols = rows[i].querySelectorAll('td, th');
+
+            for (let j = 0; j < cols.length - 1; j++) { // 跳过最后一列（操作列）
+                // 获取单元格的文本内容，去除HTML标签
+                let text = cols[j].innerText.replace(/(\r\n|\n|\r)/gm, '').replace(/,/g, '，');
+                row.push(text);
+            }
+
+            csv.push(row.join(','));
+        }
+
+        // 下载CSV文件
+        const period = $('#period-badge').text();
+        const csvString = csv.join('\n');
+        const filename = `行业资金流向_${period}_${new Date().toISOString().slice(0, 10)}.csv`;
+
+        const blob = new Blob(['\uFEFF' + csvString], { type: 'text/csv;charset=utf-8;' });
+        const link = document.createElement('a');
+        link.href = URL.createObjectURL(blob);
+        link.download = filename;
+
+        link.style.display = 'none';
+        document.body.appendChild(link);
+
+        link.click();
+
+        document.body.removeChild(link);
+    }
+
+</script>
+{% endblock %}

app/web/templates/layout.html

@@ -0,0 +1,1091 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <link rel="icon" href="/static/favicon.ico" type="image/x-icon">
+    <link rel="shortcut icon" href="/static/favicon.ico" type="image/x-icon">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>{% block title %}智能分析系统{% endblock %}</title>
+    <!-- Bootstrap CSS -->
+    <link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet">
+    <!-- Font Awesome -->
+        <link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css" rel="stylesheet">
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/theme.css') }}">
+    <!-- ApexCharts -->
+    <link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/apexcharts.min.css" rel="stylesheet">
+    <!-- Download PDF -->
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/html2pdf.js/0.10.1/html2pdf.bundle.min.js"></script>
+
+    <!-- Custom CSS -->
+    <style>
+        body {
+            font-family: 'Helvetica Neue', Arial, sans-serif;
+            background-color: var(--bg-color);
+            color: var(--text-color);
+        }
+        .navbar-brand {
+            font-weight: bold;
+        }
+        .nav-item {
+            margin-left: 10px;
+        }
+        .sidebar {
+            background-color: var(--sidebar-bg-color);
+            color: white;
+            min-height: calc(100vh - 56px);
+        }
+        .sidebar .nav-link {
+            color: var(--sidebar-text-color);
+            padding: 0.75rem 1rem;
+        }
+        .sidebar .nav-link:hover {
+            color: #fff;
+            background-color: var(--sidebar-hover-bg-color);
+        }
+        .sidebar .nav-link.active {
+            color: #fff;
+            background-color: var(--sidebar-active-bg-color);
+        }
+        .sidebar .nav-link i {
+            margin-right: 10px;
+            width: 20px;
+            text-align: center;
+        }
+        .main-content {
+            padding: 20px;
+        }
+        .card {
+            margin-bottom: 20px;
+            box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);
+            overflow: hidden; /* Prevent content from stretching container */
+        }
+
+        .card-header {
+            padding: 0.5rem 1rem;
+            height: auto !important;
+            max-height: 50px;
+        }
+
+        .form-control, .form-select, .input-group-text {
+            font-size: 0.875rem;
+        }
+
+        .input-group-sm .input-group-text {
+            padding: 0.25rem 0.5rem;
+        }
+
+        .card-body.py-2 {
+            padding-top: 0.5rem;
+            padding-bottom: 0.5rem;
+        }
+
+
+        .card-body {
+            padding: 1.25rem;
+            overflow: hidden; /* Prevent content from stretching container */
+        }
+        .loading {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            height: 200px;
+        }
+        .spinner-border {
+            width: 3rem;
+            height: 3rem;
+        }
+        .badge-success {
+            background-color: #28a745;
+        }
+        .badge-danger {
+            background-color: #dc3545;
+        }
+        .badge-warning {
+            background-color: #ffc107;
+            color: #212529;
+        }
+        .score-pill {
+            font-size: 1.2rem;
+            padding: 0.5rem 1rem;
+        }
+        #loading-overlay {
+            position: fixed;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 100%;
+            background-color: rgba(255, 255, 255, 0.8);
+            display: none;
+            justify-content: center;
+            align-items: center;
+            z-index: 9999;
+        }
+        .text-strong {
+            font-weight: bold;
+        }
+        .text-larger {
+            font-size: 1.1em;
+        }
+        .trend-up {
+            color: #28a745;
+        }
+        .trend-down {
+            color: #dc3545;
+        }
+        .analysis-section {
+            margin-bottom: 1.5rem;
+        }
+
+        /* Fix for chart container heights */
+        #price-chart {
+            height: 400px !important;
+            max-height: 400px;
+        }
+
+        /* Fix for indicators chart container */
+        #indicators-chart {
+            height: 350px !important;
+            max-height: 350px;
+        }
+
+        /* Fix chart containers */
+        .apexcharts-canvas {
+            overflow: visible !important;
+        }
+
+        /* Fix for radar chart */
+        #radar-chart {
+            height: 200px !important;
+            max-height: 200px;
+        }
+
+        /* Fix for score chart */
+        #score-chart {
+            height: 200px !important;
+            max-height: 200px;
+        }
+
+        /* Fix header alignment */
+        .card-header h5 {
+            margin-bottom: 0;
+            display: flex;
+            align-items: center;
+        }
+
+        .apexcharts-tooltip {
+            background: #fff !important;
+            border: 1px solid #e3e3e3 !important;
+            box-shadow: 0 2px 5px rgba(0,0,0,0.1) !important;
+            border-radius: 4px !important;
+            padding: 8px !important;
+            font-size: 13px !important;
+        }
+
+        .apexcharts-tooltip-title {
+            background: #f8f9fa !important;
+            border-bottom: 1px solid #e3e3e3 !important;
+            padding: 6px 8px !important;
+            margin-bottom: 4px !important;
+            font-weight: 600 !important;
+        }
+
+        .apexcharts-tooltip-y-group {
+            padding: 3px 0 !important;
+        }
+
+        .apexcharts-tooltip-candlestick {
+            padding: 5px 8px !important;
+        }
+
+        .apexcharts-tooltip-candlestick div {
+            margin: 3px 0 !important;
+        }
+
+        .apexcharts-tooltip-candlestick span {
+            font-weight: 600 !important;
+        }
+
+        .apexcharts-crosshairs {
+            stroke-width: 1px !important;
+            stroke: #90A4AE !important;
+            stroke-dasharray: 0 !important;
+            opacity: 0.8 !important;
+        }
+
+        .apexcharts-tooltip-marker {
+            width: 10px !important;
+            height: 10px !important;
+            display: inline-block !important;
+            margin-right: 5px !important;
+            border-radius: 50% !important;
+        }
+
+        .apexcharts-tooltip-series-group {
+            padding: 4px 8px !important;
+            border-bottom: 1px solid #eee !important;
+        }
+
+        .apexcharts-tooltip-series-group:last-child {
+            border-bottom: none !important;
+        }
+
+        .apexcharts-tooltip-text-y-value {
+            font-weight: 600 !important;
+        }
+
+        .apexcharts-xaxistooltip {
+            background: #fff !important;
+            border: 1px solid #e3e3e3 !important;
+            border-radius: 2px !important;
+            box-shadow: 0 2px 5px rgba(0,0,0,0.1) !important;
+            padding: 4px 8px !important;
+            font-size: 12px !important;
+            color: #333 !important;
+        }
+
+        .apexcharts-yaxistooltip {
+            background: #fff !important;
+            border: 1px solid #e3e3e3 !important;
+            border-radius: 2px !important;
+            box-shadow: 0 2px 5px rgba(0,0,0,0.1) !important;
+            padding: 4px 8px !important;
+            font-size: 12px !important;
+            color: #333 !important;
+        }
+
+        /* AI分析样式 */
+        .analysis-para {
+          line-height: 1.8;
+          margin-bottom: 1.2rem;
+          color: #333;
+        }
+
+        .keyword {
+          color: #2c7be5;
+          font-weight: 600;
+        }
+
+        .term {
+          color: #d6336c;
+          font-weight: 500;
+          padding: 0 2px;
+        }
+
+        .price {
+          color: #00a47c;
+          font-family: 'Roboto Mono', monospace;
+          background: #f3faf8;
+          padding: 2px 4px;
+          border-radius: 3px;
+        }
+
+        .date {
+          color: #6c757d;
+          font-family: 'Roboto Mono', monospace;
+        }
+
+        strong.keyword {
+          border-bottom: 2px solid #2c7be5;
+        }
+
+        .table-info {
+            position: relative;
+        }
+
+        .table-info:after {
+            content: '';
+            position: absolute;
+            top: 0;
+            left: 0;
+            right: 0;
+            bottom: 0;
+            background: rgba(0, 123, 255, 0.1);
+            animation: pulse 1.5s infinite;
+        }
+
+        @keyframes pulse {
+            0% { opacity: 0.5; }
+            50% { opacity: 0.3; }
+            100% { opacity: 0.5; }
+        }
+
+    /* 财经门户样式 */
+    .finance-portal-container {
+        display: grid;
+        grid-template-columns: 250px 1fr 300px;
+        grid-template-rows: 1fr 80px;
+        grid-template-areas:
+            "sidebar news hotspot"
+            "footer footer footer";
+        height: calc(100vh - 56px);
+        overflow: hidden;
+        gap: 15px;
+        padding: 15px;
+        background-color: #f5f7fa;
+    }
+
+    /* 左侧栏样式 */
+    .portal-sidebar {
+        grid-area: sidebar;
+        display: flex;
+        flex-direction: column;
+        background-color: #fff;
+        border-radius: 8px;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+        overflow-y: auto;
+        padding: 15px;
+    }
+
+    .sidebar-header {
+        padding: 10px 0;
+        border-bottom: 1px solid #eee;
+    }
+
+    .sidebar-header h5 {
+        margin: 0;
+        color: #333;
+        font-size: 16px;
+    }
+
+    .sidebar-nav {
+        list-style: none;
+        padding: 0;
+        margin: 10px 0;
+    }
+
+    .sidebar-nav li {
+        margin-bottom: 5px;
+    }
+
+    .sidebar-nav a {
+        display: block;
+        padding: 10px 15px;
+        color: #444;
+        text-decoration: none;
+        border-radius: 5px;
+        transition: all 0.2s;
+    }
+
+    .sidebar-nav a:hover {
+        background-color: #f0f5ff;
+        color: #1a73e8;
+    }
+
+    .sidebar-nav i {
+        width: 20px;
+        margin-right: 8px;
+        text-align: center;
+    }
+
+    /* 中间新闻区域样式 */
+    .portal-news {
+        grid-area: news;
+        display: flex;
+        flex-direction: column;
+        background-color: #fff;
+        border-radius: 8px;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+        overflow: hidden;
+    }
+
+    .news-header {
+        display: flex;
+        justify-content: space-between;
+        align-items: center;
+        padding: 12px 15px;
+        border-bottom: 1px solid #eee;
+        background-color: #fff;
+    }
+
+    .news-header h5 {
+        margin: 0;
+        color: #333;
+        font-size: 16px;
+    }
+
+    .news-tools {
+        display: flex;
+        align-items: center;
+    }
+
+    .news-content {
+        flex: 1;
+        overflow-y: auto;
+        padding: 0;
+    }
+
+    /* 新闻时间线改进样式 */
+    .news-timeline-container {
+        padding: 15px;
+    }
+
+    .time-point {
+        position: relative;
+        padding: 0 0 15px 65px;
+        min-height: 50px;
+    }
+
+    .time-point:before {
+        content: '';
+        position: absolute;
+        left: 40px;
+        top: 8px;
+        width: 12px;
+        height: 12px;
+        border-radius: 50%;
+        background-color: #1a73e8;
+        z-index: 1;
+    }
+
+    .time-point:after {
+        content: '';
+        position: absolute;
+        left: 45px;
+        top: 15px;
+        width: 2px;
+        height: calc(100% - 8px);
+        background-color: #e3e6ea;
+    }
+
+    .time-point:last-child:after {
+        display: none;
+    }
+
+    .time-label {
+        position: absolute;
+        left: 0;
+        top: 5px;
+        width: 35px;
+        text-align: right;
+        font-weight: bold;
+        font-size: 13px;
+        color: #444;
+    }
+
+    .news-items {
+        background-color: #f9f9f9;
+        border-radius: 8px;
+        box-shadow: 0 1px 3px rgba(0,0,0,0.03);
+    }
+
+    .news-item {
+        padding: 10px 15px;
+        border-bottom: 1px solid #eee;
+    }
+
+    .news-item:last-child {
+        border-bottom: none;
+    }
+
+    .news-content {
+        font-size: 14px;
+        line-height: 1.6;
+        color: #333;
+    }
+
+    /* 右侧热点区域样式 */
+    .portal-hotspot {
+        grid-area: hotspot;
+        display: flex;
+        flex-direction: column;
+        background-color: #fff;
+        border-radius: 8px;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+        overflow: hidden;
+    }
+
+    .hotspot-header {
+        padding: 12px 15px;
+        border-bottom: 1px solid #eee;
+        background-color: #fff;
+    }
+
+    .hotspot-header h5 {
+        margin: 0;
+        color: #333;
+        font-size: 16px;
+    }
+
+    .hotspot-content {
+        overflow-y: auto;
+        padding: 10px 15px;
+        flex: 1;
+    }
+
+    .hotspot-list {
+        display: flex;
+        flex-direction: column;
+        gap: 10px;
+    }
+
+    .hotspot-item {
+        display: flex;
+        align-items: flex-start;
+        gap: 10px;
+        padding: 8px 0;
+        border-bottom: 1px solid #f0f0f0;
+    }
+
+    .hotspot-item:last-child {
+        border-bottom: none;
+    }
+
+    .hotspot-rank {
+        display: inline-flex;
+        align-items: center;
+        justify-content: center;
+        width: 20px;
+        height: 20px;
+        border-radius: 4px;
+        background-color: #e9ecef;
+        color: #666;
+        font-size: 12px;
+        font-weight: bold;
+    }
+
+    .hotspot-rank.rank-top {
+        background-color: #fb6340;
+        color: #fff;
+    }
+
+    .hotspot-title {
+        flex: 1;
+        font-size: 14px;
+        line-height: 1.4;
+        color: #333;
+    }
+
+    /* 页脚区域样式 */
+    .portal-footer {
+        grid-area: footer;
+        display: flex;
+        flex-direction: column;
+        background-color: #fff;
+        border-radius: 8px;
+        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
+    }
+
+    /* 修改后的市场状态样式 */
+    .market-status {
+        display: flex;
+        justify-content: space-between;
+        align-items: center;
+        height: 40px;
+        border-bottom: 1px solid #eee;
+        padding: 0 10px;
+    }
+
+    .market-group {
+        display: flex;
+        align-items: center;
+        gap: 10px;
+    }
+
+    .group-title {
+        font-size: 12px;
+        font-weight: bold;
+        color: #666;
+        white-space: nowrap;
+    }
+
+    .status-group {
+        display: flex;
+        gap: 15px;
+    }
+
+    .status-item {
+        display: flex;
+        align-items: center;
+        gap: 4px;
+        font-size: 12px;
+        white-space: nowrap;
+    }
+
+    .status-item i {
+        font-size: 10px;
+    }
+
+    .current-time {
+        display: flex;
+        align-items: center;
+        gap: 15px;
+        color: #666;
+        font-size: 12px;
+    }
+
+    .refresh-time {
+        color: #888;
+    }
+
+    i.status-open {
+        color: #2dce89;
+    }
+
+    i.status-closed {
+        color: #8898aa;
+    }
+
+    .ticker-news {
+        height: 40px;
+        overflow: hidden;
+        position: relative;
+        background-color: #f8f9fa;
+    }
+
+    .ticker-wrapper {
+        display: flex;
+        position: absolute;
+        white-space: nowrap;
+    }
+
+    .ticker-item {
+        padding: 0 30px;
+        line-height: 40px;
+        color: #333;
+    }
+
+    @keyframes ticker {
+        0% {
+            transform: translate3d(0, 0, 0);
+        }
+        100% {
+            transform: translate3d(-50%, 0, 0);
+        }
+    }
+
+    /* 响应式调整 */
+    @media (max-width: 1200px) {
+        .finance-portal-container {
+            grid-template-columns: 200px 1fr 250px;
+        }
+    }
+
+    @media (max-width: 992px) {
+        .finance-portal-container {
+            grid-template-columns: 1fr;
+            grid-template-rows: auto 1fr auto auto;
+            grid-template-areas:
+                "sidebar"
+                "news"
+                "hotspot"
+                "footer";
+            height: auto;
+            overflow: auto;
+        }
+
+        .portal-news, .portal-hotspot {
+            height: 500px;
+        }
+
+        .portal-footer {
+            position: fixed;
+            bottom: 0;
+            left: 0;
+            right: 0;
+            z-index: 1000;
+            border-radius: 0;
+        }
+    }
+    
+    .time-date {
+        position: absolute;
+        left: 0;
+        top: 25px;
+        width: 35px;
+        text-align: right;
+        font-size: 11px;
+        color: #666;
+        font-weight: normal;
+    }
+    
+        /* Global Task Monitor Styles */
+        #task-monitor {
+            position: fixed;
+            bottom: 20px;
+            right: 20px;
+            width: 350px;
+            max-height: 400px;
+            z-index: 1050;
+            display: none; /* Initially hidden */
+        }
+        #task-monitor .card-header {
+            cursor: pointer;
+        }
+        #task-monitor .task-item {
+            padding: 8px 12px;
+            border-bottom: 1px solid #eee;
+        }
+        #task-monitor .task-item:last-child {
+            border-bottom: none;
+        }
+        #task-monitor .task-item a {
+            text-decoration: none;
+            color: inherit;
+        }
+        #task-monitor .task-item small {
+            display: block;
+            color: #6c757d;
+        }
+        #task-monitor .progress {
+            height: 8px;
+            margin-top: 5px;
+        }
+
+    /* 调整时间点样式，为日期留出空间 */
+    .time-point {
+        position: relative;
+        padding: 0 0 15px 65px;
+        min-height: 60px; /* 增加高度 */
+    }
+
+    </style>
+            <script>
+            // Immediately apply the theme on page load to prevent FOUC
+            (function() {
+                const theme = localStorage.getItem('theme') || 'light';
+                if (theme === 'dark') {
+                    document.documentElement.setAttribute('data-theme', 'dark');
+                }
+            })();
+        </script>
+    {% block head %}{% endblock %}
+</head>
+<body>
+    <!-- Loading Overlay -->
+    <div id="loading-overlay">
+        <div class="spinner-border text-primary" role="status">
+            <span class="visually-hidden">Loading...</span>
+        </div>
+    </div>
+
+    <!-- Top Navigation -->
+    <nav class="navbar navbar-expand-lg navbar-dark bg-primary">
+        <div class="container-fluid">
+            <a class="navbar-brand" href="/">智能分析系统</a>
+            <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
+                <span class="navbar-toggler-icon"></span>
+            </button>
+            <!-- 在layout.html的导航栏部分修改 -->
+            <div class="collapse navbar-collapse" id="navbarNav">
+                <ul class="navbar-nav me-auto">
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/' %}active{% endif %}" href="/"><i class="fas fa-home"></i> 主页</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/dashboard' %}active{% endif %}" href="/dashboard"><i class="fas fa-chart-line"></i> 智能仪表盘</a>
+                    </li>
+                    <!-- 新增菜单项 - 基本面分析 -->
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path.startswith('/fundamental') %}active{% endif %}" href="/fundamental"><i class="fas fa-file-invoice-dollar"></i> 基本面分析</a>
+                    </li>
+                    <!-- 新增菜单项 - 资金流向 -->
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path.startswith('/capital_flow') %}active{% endif %}" href="/capital_flow"><i class="fas fa-money-bill-wave"></i> 资金流向</a>
+                    </li>
+                    <!-- 新增菜单项 - 情景预测 -->
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path.startswith('/scenario') %}active{% endif %}" href="/scenario_predict"><i class="fas fa-lightbulb"></i> 情景预测</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/market_scan' %}active{% endif %}" href="/market_scan"><i class="fas fa-search"></i> 市场扫描</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/portfolio' %}active{% endif %}" href="/portfolio"><i class="fas fa-briefcase"></i> 投资组合</a>
+                    </li>
+                    <!-- 新增菜单项 - 风险监控 -->
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path.startswith('/risk') %}active{% endif %}" href="/risk_monitor"><i class="fas fa-exclamation-triangle"></i> 风险监控</a>
+                    </li>
+                    <!-- 新增菜单项 - 智能问答 -->
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/qa' %}active{% endif %}" href="/qa"><i class="fas fa-question-circle"></i> 智能问答</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/agent_analysis' %}active{% endif %}" href="/agent_analysis"><i class="fas fa-robot"></i> 智能体分析</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link {% if request.path == '/etf_analysis' %}active{% endif %}" href="/etf_analysis"><i class="fas fa-chart-pie"></i> ETF分析</a>
+                    </li>
+                </ul>
+                <div class="d-flex align-items-center">
+                    <button class="btn text-white me-2" id="theme-toggle" title="切换主题">
+                        <i class="fas fa-moon"></i>
+                    </button>
+                    <div class="input-group">
+                        <input type="text" id="search-stock" class="form-control" placeholder="搜索股票代码/名称" aria-label="搜索股票">
+                        <button class="btn btn-light" type="button" id="search-button">
+                            <i class="fas fa-search"></i>
+                        </button>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </nav>
+
+    <div class="container-fluid">
+        <div class="row">
+            {% block sidebar %}{% endblock %}
+
+            <main class="{% if self.sidebar()|trim %}col-md-9 ms-sm-auto col-lg-10 px-md-4{% else %}col-12{% endif %} main-content">
+                <div id="alerts-container"></div>
+                {% block content %}{% endblock %}
+            </main>
+        </div>
+    </div>
+
+    <!-- Global Task Monitor -->
+    <div id="task-monitor" class="card shadow-lg">
+        <div class="card-header bg-primary text-white d-flex justify-content-between align-items-center py-2" data-bs-toggle="collapse" href="#task-monitor-body">
+            <h6 class="mb-0"><i class="fas fa-tasks me-2"></i>后台分析任务</h6>
+            <span class="badge bg-light text-primary" id="task-count">0</span>
+        </div>
+        <div class="collapse show" id="task-monitor-body">
+            <div class="card-body p-0" id="task-list" style="max-height: 300px; overflow-y: auto;">
+                <!-- Active tasks will be injected here -->
+            </div>
+        </div>
+    </div>
+
+    <!-- Bootstrap JS with Popper -->
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"></script>
+    <!-- jQuery -->
+    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
+    <!-- ApexCharts -->
+    <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/apexcharts.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/mermaid@10/dist/mermaid.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
+    <!-- Common JS -->
+    <script>
+        // 显示加载中覆盖层
+        function showLoading() {
+            $('#loading-overlay').css('display', 'flex');
+        }
+
+        // 隐藏加载中覆盖层
+        function hideLoading() {
+            $('#loading-overlay').css('display', 'none');
+        }
+
+        // 显示错误提示
+        function showError(message) {
+            const alertHtml = `
+                <div class="alert alert-danger alert-dismissible fade show" role="alert">
+                    ${message}
+                    <button type="button" class="btn-close" data-bs-dismiss="alert" aria-label="Close"></button>
+                </div>
+            `;
+            $('#alerts-container').html(alertHtml);
+        }
+
+    // 显示信息提示
+    function showInfo(message) {
+        const alertHtml = `
+            <div class="alert alert-info alert-dismissible fade show" role="alert">
+                <i class="fas fa-info-circle me-2"></i>${message}
+                <button type="button" class="btn-close" data-bs-dismiss="alert" aria-label="Close"></button>
+            </div>
+        `;
+        $('#alerts-container').html(alertHtml);
+    }
+
+        // 显示成功提示
+        function showSuccess(message) {
+            const alertHtml = `
+                <div class="alert alert-success alert-dismissible fade show" role="alert">
+                    ${message}
+                    <button type="button" class="btn-close" data-bs-dismiss="alert" aria-label="Close"></button>
+                </div>
+            `;
+            $('#alerts-container').html(alertHtml);
+        }
+
+        // 搜索股票并跳转到详情页
+        $('#search-button').click(function() {
+            const stockCode = $('#search-stock').val().trim();
+            if (stockCode) {
+                window.location.href = `/stock_detail/${stockCode}`;
+            }
+        });
+
+        // 回车键搜索
+        $('#search-stock').keypress(function(e) {
+            if (e.which === 13) {
+                $('#search-button').click();
+            }
+        });
+
+        // Theme Toggle Logic
+        (function() {
+            const themeToggle = document.getElementById('theme-toggle');
+            if (!themeToggle) return;
+
+            const themeIcon = themeToggle.querySelector('i');
+            const htmlElement = document.documentElement;
+
+            // Set initial icon based on theme
+            if (htmlElement.getAttribute('data-theme') === 'dark') {
+                themeIcon.classList.remove('fa-moon');
+                themeIcon.classList.add('fa-sun');
+            }
+
+            themeToggle.addEventListener('click', function() {
+                const isDark = htmlElement.hasAttribute('data-theme');
+                if (isDark) {
+                    htmlElement.removeAttribute('data-theme');
+                    localStorage.setItem('theme', 'light');
+                    themeIcon.classList.remove('fa-sun');
+                    themeIcon.classList.add('fa-moon');
+                } else {
+                    htmlElement.setAttribute('data-theme', 'dark');
+                    localStorage.setItem('theme', 'dark');
+                    themeIcon.classList.remove('fa-moon');
+                    themeIcon.classList.add('fa-sun');
+                }
+            });
+        })();
+
+
+        // 格式化数字 - 增强版
+        function formatNumber(num, digits = 2) {
+            console.log('formatNumber called with:', num);
+            if (num === null || num === undefined) return '-';
+            return parseFloat(num).toFixed(digits);
+        }
+
+        // 格式化技术指标 - 新增函数
+        function formatIndicator(value, indicatorType) {
+            if (value === null || value === undefined) return '-';
+
+            // 根据指标类型使用不同的小数位数
+            if (indicatorType === 'MACD' || indicatorType === 'Signal' || indicatorType === 'Histogram') {
+                return parseFloat(value).toFixed(3);  // MACD相关指标使用3位小数
+            } else if (indicatorType === 'RSI') {
+                return parseFloat(value).toFixed(2);  // RSI使用2位小数
+            } else {
+                return parseFloat(value).toFixed(2);  // 默认使用2位小数
+            }
+        }
+
+        // 格式化百分比
+        function formatPercent(num, digits = 2) {
+            console.log('formatPercent called with:', num);
+            if (num === null || num === undefined) return '-';
+            return parseFloat(num).toFixed(digits) + '%';
+        }
+
+        // 根据评分获取颜色类
+        function getScoreColorClass(score) {
+            if (score >= 80) return 'bg-success';
+            if (score >= 60) return 'bg-primary';
+            if (score >= 40) return 'bg-warning';
+            return 'bg-danger';
+        }
+
+        // 根据趋势获取颜色类
+        function getTrendColorClass(trend) {
+            return trend === 'UP' ? 'trend-up' : 'trend-down';
+        }
+
+        // 根据趋势获取图标
+        function getTrendIcon(trend) {
+            return trend === 'UP' ? '<i class="fas fa-arrow-up"></i>' : '<i class="fas fa-arrow-down"></i>';
+        }
+        // Global Task Monitor Logic
+        $(document).ready(function() {
+            const monitor = $('#task-monitor');
+            const taskList = $('#task-list');
+            const taskCount = $('#task-count');
+
+            function checkActiveTasks() {
+                $.ajax({
+                    url: '/api/active_tasks',
+                    type: 'GET',
+                    success: function(response) {
+                        const tasks = response.active_tasks;
+                        
+                        if (tasks && tasks.length > 0) {
+                            taskList.empty();
+                            taskCount.text(tasks.length);
+
+                            tasks.forEach(task => {
+                                const taskHtml = `
+                                    <div class="task-item d-flex justify-content-between align-items-center">
+                                        <a href="/agent_analysis?task_id=${task.task_id}" class="text-decoration-none text-reset flex-grow-1">
+                                            <strong>股票: ${task.stock_code}</strong>
+                                            <small class="d-block">${task.current_step}</small>
+                                            <div class="progress">
+                                                <div class="progress-bar progress-bar-striped progress-bar-animated" role="progressbar" style="width: ${task.progress}%;" aria-valuenow="${task.progress}" aria-valuemin="0" aria-valuemax="100"></div>
+                                            </div>
+                                        </a>
+                                        <button class="btn btn-sm btn-outline-danger delete-task-btn ms-2 flex-shrink-0" data-task-id="${task.task_id}" title="删除任务" style="border: none;">
+                                            <i class="fas fa-times"></i>
+                                        </button>
+                                    </div>
+                                `;
+                                taskList.append(taskHtml);
+                            });
+                            monitor.fadeIn();
+                        } else {
+                            monitor.fadeOut();
+                        }
+                    },
+                    error: function(xhr) {
+                        // Hide monitor on error to avoid confusion
+                        monitor.fadeOut();
+                        console.error("Failed to fetch active tasks:", xhr.responseText);
+                    }
+                });
+            }
+
+            // Check for active tasks every 15 seconds
+            setInterval(checkActiveTasks, 15000);
+            // Initial check on page load
+            checkActiveTasks();
+
+            // Handle task deletion
+            $(document).on('click', '#task-list .delete-task-btn', function(e) {
+                e.preventDefault();
+                e.stopPropagation();
+                const button = $(this);
+                const taskId = button.data('task-id');
+                const monitor = $('#task-monitor');
+                const taskCount = $('#task-count');
+
+                if (!taskId) {
+                    console.error("Delete button clicked, but no task-id found.");
+                    return;
+                }
+                
+                if (confirm(`确定要删除任务 ${taskId} 吗？`)) {
+                    $.ajax({
+                        url: '/api/delete_agent_analysis',
+                        type: 'POST',
+                        contentType: 'application/json',
+                        data: JSON.stringify({ task_ids: [taskId] }),
+                        success: function(response) {
+                            if (response.success) {
+                                button.closest('.task-item').fadeOut(300, function() { 
+                                    $(this).remove(); 
+                                    const currentCount = $('#task-list .task-item').length;
+                                    taskCount.text(currentCount);
+                                    if (currentCount === 0) {
+                                        monitor.fadeOut();
+                                    }
+                                });
+                                showSuccess(`任务 ${taskId} 已成功删除。`);
+                            } else {
+                                showError(response.error || '删除任务失败。');
+                            }
+                        },
+                        error: function(xhr) {
+                            showError('删除任务时连接服务器失败。');
+                            console.error("Failed to delete task:", xhr.responseText);
+                        }
+                    });
+                }
+            });
+        });
+    </script>
+    {% block scripts %}{% endblock %}
+</body>
+</html>

app/web/templates/market_scan.html

@@ -0,0 +1,591 @@
+{% extends "layout.html" %}
+
+{% block title %}市场扫描 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-4">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-4">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header d-flex justify-content-between">
+                    <h5 class="mb-0">市场扫描</h5>
+                </div>
+                <div class="card-body">
+                    <form id="scan-form" class="row g-3">
+                        <div class="col-md-3">
+                            <div class="input-group">
+                                <span class="input-group-text">选择指数</span>
+                                <select class="form-select" id="index-selector">
+                                    <option value="">-- 选择指数 --</option>
+                                    <option value="000300">沪深300</option>
+                                    <option value="000905">中证500</option>
+                                    <option value="000852">中证1000</option>
+                                    <option value="000001">上证指数</option>
+                                </select>
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group">
+                                <span class="input-group-text">选择行业</span>
+                                <select class="form-select" id="industry-selector">
+                                    <option value="">-- 选择行业 --</option>
+                                    <option value="保险">保险</option>
+                                    <option value="食品饮料">食品饮料</option>
+                                    <option value="多元金融">多元金融</option>
+                                    <option value="游戏">游戏</option>
+                                    <option value="酿酒行业">酿酒行业</option>
+                                    <option value="商业百货">商业百货</option>
+                                    <option value="证券">证券</option>
+                                    <option value="船舶制造">船舶制造</option>
+                                    <option value="家用轻工">家用轻工</option>
+                                    <option value="旅游酒店">旅游酒店</option>
+                                    <option value="美容护理">美容护理</option>
+                                    <option value="医疗服务">医疗服务</option>
+                                    <option value="软件开发">软件开发</option>
+                                    <option value="化学制药">化学制药</option>
+                                    <option value="医疗器械">医疗器械</option>
+                                    <option value="家电行业">家电行业</option>
+                                    <option value="汽车服务">汽车服务</option>
+                                    <option value="造纸印刷">造纸印刷</option>
+                                    <option value="纺织服装">纺织服装</option>
+                                    <option value="光伏设备">光伏设备</option>
+                                    <option value="房地产服务">房地产服务</option>
+                                    <option value="文化传媒">文化传媒</option>
+                                    <option value="医药商业">医药商业</option>
+                                    <option value="中药">中药</option>
+                                    <option value="专业服务">专业服务</option>
+                                    <option value="生物制品">生物制品</option>
+                                    <option value="仪器仪表">仪器仪表</option>
+                                    <option value="房地产开发">房地产开发</option>
+                                    <option value="教育">教育</option>
+                                    <option value="半导体">半导体</option>
+                                    <option value="玻璃玻纤">玻璃玻纤</option>
+                                    <option value="汽车整车">汽车整车</option>
+                                    <option value="消费电子">消费电子</option>
+                                    <option value="贸易行业">贸易行业</option>
+                                    <option value="包装材料">包装材料</option>
+                                    <option value="汽车零部件">汽车零部件</option>
+                                    <option value="电子化学品">电子化学品</option>
+                                    <option value="电子元件">电子元件</option>
+                                    <option value="装修建材">装修建材</option>
+                                    <option value="交运设备">交运设备</option>
+                                    <option value="农牧饲渔">农牧饲渔</option>
+                                    <option value="塑料制品">塑料制品</option>
+                                    <option value="珠宝首饰">珠宝首饰</option>
+                                    <option value="贵金属">贵金属</option>
+                                    <option value="非金属材料">非金属材料</option>
+                                    <option value="装修装饰">装修装饰</option>
+                                    <option value="风电设备">风电设备</option>
+                                    <option value="工程咨询服务">工程咨询服务</option>
+                                    <option value="专用设备">专用设备</option>
+                                    <option value="光学光电子">光学光电子</option>
+                                    <option value="航空机场">航空机场</option>
+                                    <option value="小金属">小金属</option>
+                                    <option value="物流行业">物流行业</option>
+                                    <option value="通用设备">通用设备</option>
+                                    <option value="计算机设备">计算机设备</option>
+                                    <option value="环保行业">环保行业</option>
+                                    <option value="航运港口">航运港口</option>
+                                    <option value="通信设备">通信设备</option>
+                                    <option value="水泥建材">水泥建材</option>
+                                    <option value="电池">电池</option>
+                                    <option value="化肥行业">化肥行业</option>
+                                    <option value="互联网服务">互联网服务</option>
+                                    <option value="工程建设">工程建设</option>
+                                    <option value="橡胶制品">橡胶制品</option>
+                                    <option value="化学原料">化学原料</option>
+                                    <option value="化纤行业">化纤行业</option>
+                                    <option value="农药兽药">农药兽药</option>
+                                    <option value="化学制品">化学制品</option>
+                                    <option value="能源金属">能源金属</option>
+                                    <option value="有色金属">有色金属</option>
+                                    <option value="采掘行业">采掘行业</option>
+                                    <option value="燃气">燃气</option>
+                                    <option value="综合行业">综合行业</option>
+                                    <option value="工程机械">工程机械</option>
+                                    <option value="银行">银行</option>
+                                    <option value="铁路公路">铁路公路</option>
+                                    <option value="石油行业">石油行业</option>
+                                    <option value="公用事业">公用事业</option>
+                                    <option value="电机">电机</option>
+                                    <option value="通信服务">通信服务</option>
+                                    <option value="钢铁行业">钢铁行业</option>
+                                    <option value="电力行业">电力行业</option>
+                                    <option value="电网设备">电网设备</option>
+                                    <option value="煤炭行业">煤炭行业</option>
+                                    <option value="电源设备">电源设备</option>
+                                    <option value="航天航空">航天航空</option>
+                                </select>                                
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group">
+                                <span class="input-group-text">自定义股票</span>
+                                <input type="text" class="form-control" id="custom-stocks" placeholder="多个股票代码用逗号分隔">
+                            </div>
+                        </div>
+                        <div class="col-md-3">
+                            <div class="input-group">
+                                <span class="input-group-text">最低分数</span>
+                                <input type="number" class="form-control" id="min-score" value="60" min="0" max="100">
+                                <button type="submit" class="btn btn-primary">
+                                    <i class="fas fa-search"></i> 扫描
+                                </button>
+                            </div>
+                        </div>
+                    </form>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div class="row mb-4">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header d-flex justify-content-between">
+                    <h5 class="mb-0">扫描结果</h5>
+                    <div>
+                        <span class="badge bg-primary ms-2" id="result-count">0</span>
+                        <button class="btn btn-sm btn-outline-primary ms-2" id="export-btn" style="display: none;">
+                            <i class="fas fa-download"></i> 导出结果
+                        </button>
+                    </div>
+                </div>
+                <div class="card-body">
+                    <div id="scan-loading" class="text-center py-5" style="display: none;">
+                        <div class="spinner-border text-primary" role="status">
+                            <span class="visually-hidden">Loading...</span>
+                        </div>
+                        <p class="mt-2" id="scan-message">正在扫描市场，请稍候...</p>
+                        <div class="progress mt-3" style="height: 5px;">
+                            <div class="progress-bar progress-bar-striped progress-bar-animated" role="progressbar" style="width: 100%"></div>
+                        </div>
+                        <button id="cancel-scan-btn" class="btn btn-outline-secondary mt-3">
+                            <i class="fas fa-times"></i> 取消扫描
+                        </button>
+                    </div>
+
+                    <!-- 添加错误重试区域 -->
+                    <div id="scan-error-retry" class="text-center mt-3" style="display: none;">
+                        <button id="scan-retry-button" class="btn btn-primary mt-2">
+                            <i class="fas fa-sync-alt"></i> 重试扫描
+                        </button>
+                        <p class="text-muted small mt-2">
+                            已超负载
+                        </p>
+                    </div>
+
+                    <div id="scan-results">
+                        <table class="table table-hover">
+                            <thead>
+                                <tr>
+                                    <th>代码</th>
+                                    <th>名称</th>
+                                    <th>行业</th>
+                                    <th>得分</th>
+                                    <th>价格</th>
+                                    <th>涨跌幅</th>
+                                    <th>RSI</th>
+                                    <th>MA趋势</th>
+                                    <th>成交量</th>
+                                    <th>建议</th>
+                                    <th>操作</th>
+                                </tr>
+                            </thead>
+                            <tbody id="results-table">
+                                <tr>
+                                    <td colspan="11" class="text-center">暂无数据，请开始扫描</td>
+                                </tr>
+                            </tbody>
+                        </table>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    $(document).ready(function() {
+        // 表单提交
+        $('#scan-form').submit(function(e) {
+            e.preventDefault();
+
+            // 获取股票列表
+            let stockList = [];
+
+            // 获取指数股票
+            const indexCode = $('#index-selector').val();
+            if (indexCode) {
+                fetchIndexStocks(indexCode);
+                return;
+            }
+
+            // 获取行业股票
+            const industry = $('#industry-selector').val();
+            if (industry) {
+                fetchIndustryStocks(industry);
+                return;
+            }
+
+            // 获取自定义股票
+            const customStocks = $('#custom-stocks').val().trim();
+            if (customStocks) {
+                stockList = customStocks.split(',').map(s => s.trim());
+                scanMarket(stockList);
+            } else {
+                showError('请至少选择一种方式获取股票列表');
+            }
+        });
+
+        // 指数选择变化
+        $('#index-selector').change(function() {
+            if ($(this).val()) {
+                $('#industry-selector').val('');
+            }
+        });
+
+        // 行业选择变化
+        $('#industry-selector').change(function() {
+            if ($(this).val()) {
+                $('#index-selector').val('');
+            }
+        });
+
+        // 导出结果
+        $('#export-btn').click(function() {
+            exportToCSV();
+        });
+
+        // 获取指数成分股
+        function fetchIndexStocks(indexCode) {
+            $('#scan-loading').show();
+            $('#scan-results').hide();
+
+            $.ajax({
+                url: `/api/index_stocks?index_code=${indexCode}`,
+                type: 'GET',
+                dataType: 'json',
+                success: function(response) {
+                    const stockList = response.stock_list;
+                    if (stockList && stockList.length > 0) {
+                        // 保存最近的扫描列表用于重试
+                        window.lastScanList = stockList;
+
+                        scanMarket(stockList);
+                    } else {
+                        $('#scan-loading').hide();
+                        $('#scan-results').show();
+                        showError('获取指数成分股失败，或成分股列表为空');
+                    }
+                },
+                error: function(error) {
+                    $('#scan-loading').hide();
+                    $('#scan-results').show();
+                    showError('获取指数成分股失败: ' + (error.responseJSON ? error.responseJSON.error : error.statusText));
+                }
+            });
+        }
+
+        // 获取行业成分股
+        function fetchIndustryStocks(industry) {
+            $('#scan-loading').show();
+            $('#scan-results').hide();
+
+            $.ajax({
+                url: `/api/industry_stocks?industry=${encodeURIComponent(industry)}`,
+                type: 'GET',
+                dataType: 'json',
+                success: function(response) {
+                    const stockList = response.stock_list;
+                    if (stockList && stockList.length > 0) {
+                        // 保存最近的扫描列表用于重试
+                        window.lastScanList = stockList;
+
+                        scanMarket(stockList);
+                    } else {
+                        $('#scan-loading').hide();
+                        $('#scan-results').show();
+                        showError('获取行业成分股失败，或成分股列表为空');
+                    }
+                },
+                error: function(error) {
+                    $('#scan-loading').hide();
+                    $('#scan-results').show();
+                    showError('获取行业成分股失败: ' + (error.responseJSON ? error.responseJSON.error : error.statusText));
+                }
+            });
+        }
+
+        // 扫描市场
+        function scanMarket(stockList) {
+            $('#scan-loading').show();
+            $('#scan-results').hide();
+            $('#scan-error-retry').hide();
+
+            // 添加处理时间计数器
+            let processingTime = 0;
+            let stockCount = stockList.length;
+
+            // 保存上次扫描列表
+            window.lastScanList = stockList;
+
+            // 更新扫描提示消息
+            $('#scan-message').html(`正在准备扫描${stockCount}只股票，请稍候...`);
+
+            const minScore = parseInt($('#min-score').val() || 60);
+
+            // 第一步：启动扫描任务
+            $.ajax({
+                url: '/api/start_market_scan',
+                type: 'POST',
+                contentType: 'application/json',
+                data: JSON.stringify({
+                    stock_list: stockList,
+                    min_score: minScore,
+                    market_type: 'A'
+                }),
+                success: function(response) {
+                    const taskId = response.task_id;
+
+                    if (!taskId) {
+                        showError('启动扫描任务失败：未获取到任务ID');
+                        $('#scan-loading').hide();
+                        $('#scan-results').show();
+                        $('#scan-error-retry').show();
+                        return;
+                    }
+
+                    // 启动轮询任务状态
+                    pollScanStatus(taskId, processingTime);
+                },
+                error: function(xhr, status, error) {
+                    $('#scan-loading').hide();
+                    $('#scan-results').show();
+
+                    let errorMsg = '启动扫描任务失败';
+                    if (xhr.responseJSON && xhr.responseJSON.error) {
+                        errorMsg += ': ' + xhr.responseJSON.error;
+                    } else if (error) {
+                        errorMsg += ': ' + error;
+                    }
+
+                    showError(errorMsg);
+                    $('#scan-error-retry').show();
+                }
+            });
+        }
+
+        // 轮询扫描任务状态
+        function pollScanStatus(taskId, startTime) {
+            let elapsedTime = startTime || 0;
+            let pollInterval;
+
+            // 立即执行一次，然后设置定时器
+            checkStatus();
+
+            function checkStatus() {
+                $.ajax({
+                    url: `/api/scan_status/${taskId}`,
+                    type: 'GET',
+                    success: function(response) {
+                        // 更新计时和进度
+                        elapsedTime++;
+                        const progress = response.progress || 0;
+
+                        // 更新进度消息
+                        $('#scan-message').html(`正在扫描市场...<br>
+                            进度: ${progress}% 完成<br>
+                            已处理 ${Math.round(response.total * progress / 100)} / ${response.total} 只股票<br>
+                            耗时: ${elapsedTime}秒`);
+
+                        // 检查任务状态
+                        if (response.status === 'completed') {
+                            // 扫描完成，停止轮询
+                            clearInterval(pollInterval);
+
+                            // 显示结果
+                            renderResults(response.result || []);
+                            $('#scan-loading').hide();
+                            $('#scan-results').show();
+
+                            // 如果结果为空，显示提示
+                            if (!response.result || response.result.length === 0) {
+                                $('#results-table').html('<tr><td colspan="11" class="text-center">未找到符合条件的股票</td></tr>');
+                                $('#result-count').text('0');
+                                $('#export-btn').hide();
+                            }
+
+                        } else if (response.status === 'failed') {
+                            // 扫描失败，停止轮询
+                            clearInterval(pollInterval);
+
+                            $('#scan-loading').hide();
+                            $('#scan-results').show();
+
+                            showError('扫描任务失败: ' + (response.error || '未知错误'));
+                            $('#scan-error-retry').show();
+
+                        } else {
+                            // 任务仍在进行中，继续轮询
+                            // 轮询间隔根据进度动态调整
+                            if (!pollInterval) {
+                                pollInterval = setInterval(checkStatus, 2000);
+                            }
+                        }
+                    },
+                    error: function(xhr, status, error) {
+
+                        // 尝试继续轮询
+                        if (!pollInterval) {
+                            pollInterval = setInterval(checkStatus, 3000);
+                        }
+
+                        // 更新进度消息
+                        $('#scan-message').html(`正在扫描市场...<br>
+                            无法获取最新进度<br>
+                            耗时: ${elapsedTime}秒`);
+                    }
+                });
+            }
+        }
+
+        // 取消扫描任务
+        function cancelScan(taskId) {
+            $.ajax({
+                url: `/api/cancel_scan/${taskId}`,
+                type: 'POST',
+                success: function(response) {
+                    $('#scan-loading').hide();
+                    $('#scan-results').show();
+                    showError('扫描任务已取消');
+                    $('#scan-error-retry').show();
+                },
+                error: function(xhr, status, error) {
+                    console.error('取消扫描任务失败:', error);
+                }
+            });
+        }
+
+        // 渲染扫描结果
+        function renderResults(results) {
+            if (!results || results.length === 0) {
+                $('#results-table').html('<tr><td colspan="11" class="text-center">未找到符合条件的股票</td></tr>');
+                $('#result-count').text('0');
+                $('#export-btn').hide();
+                return;
+            }
+
+            let html = '';
+            results.forEach(result => {
+                // 获取股票评分的颜色类
+                const scoreClass = getScoreColorClass(result.score);
+
+                // 获取MA趋势的类和图标
+                const maTrendClass = getTrendColorClass(result.ma_trend);
+                const maTrendIcon = getTrendIcon(result.ma_trend);
+
+                // 获取价格变动的类和图标
+                const priceChangeClass = result.price_change >= 0 ? 'trend-up' : 'trend-down';
+                const priceChangeIcon = result.price_change >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+                html += `
+                    <tr>
+                        <td>${result.stock_code}</td>
+                        <td>${result.stock_name || '未知'}</td>
+                        <td>${result.industry || '-'}</td>
+                        <td><span class="badge ${scoreClass}">${result.score}</span></td>
+                        <td>${formatNumber(result.price)}</td>
+                        <td class="${priceChangeClass}">${priceChangeIcon} ${formatPercent(result.price_change)}</td>
+                        <td>${formatNumber(result.rsi)}</td>
+                        <td class="${maTrendClass}">${maTrendIcon} ${result.ma_trend}</td>
+                        <td>${result.volume_status}</td>
+                        <td>${result.recommendation}</td>
+                        <td>
+                            <a href="/stock_detail/${result.stock_code}" class="btn btn-sm btn-primary">
+                                <i class="fas fa-chart-line"></i> 详情
+                            </a>
+                        </td>
+                    </tr>
+                `;
+            });
+
+            $('#results-table').html(html);
+            $('#result-count').text(results.length);
+            $('#export-btn').show();
+        }
+
+        // 导出到CSV
+        function exportToCSV() {
+            // 获取表格数据
+            const table = document.querySelector('#scan-results table');
+            let csv = [];
+            let rows = table.querySelectorAll('tr');
+
+            for (let i = 0; i < rows.length; i++) {
+                let row = [], cols = rows[i].querySelectorAll('td, th');
+
+                for (let j = 0; j < cols.length - 1; j++) { // 跳过最后一列（操作列）
+                    // 获取单元格的文本内容，去除HTML标签
+                    let text = cols[j].innerText.replace(/(\r\n|\n|\r)/gm, '').replace(/,/g, '，');
+                    row.push(text);
+                }
+
+                csv.push(row.join(','));
+            }
+
+            // 下载CSV文件
+            const csvString = csv.join('\n');
+            const filename = '市场扫描结果_' + new Date().toISOString().slice(0, 10) + '.csv';
+
+            const blob = new Blob(['\uFEFF' + csvString], { type: 'text/csv;charset=utf-8;' });
+            const link = document.createElement('a');
+            link.href = URL.createObjectURL(blob);
+            link.download = filename;
+
+            link.style.display = 'none';
+            document.body.appendChild(link);
+
+            link.click();
+
+            document.body.removeChild(link);
+        }
+    });
+
+
+    // 添加到script部分
+    let currentTaskId = null;  // 存储当前任务ID
+
+    // 取消按钮点击事件
+    $('#cancel-scan-btn').click(function() {
+        if (currentTaskId) {
+            cancelScan(currentTaskId);
+        } else {
+            $('#scan-loading').hide();
+            $('#scan-results').show();
+        }
+    });
+
+    // 修改启动成功处理
+    function handleStartSuccess(response) {
+        const taskId = response.task_id;
+        currentTaskId = taskId;  // 保存当前任务ID
+
+        if (!taskId) {
+            showError('启动扫描任务失败：未获取到任务ID');
+            $('#scan-loading').hide();
+            $('#scan-results').show();
+            $('#scan-error-retry').show();
+            return;
+        }
+
+        // 启动轮询任务状态
+        pollScanStatus(taskId, 0);
+    }
+
+</script>
+{% endblock %}

app/web/templates/portfolio.html

@@ -0,0 +1,602 @@
+
+{% extends "layout.html" %}
+
+{% block title %}投资组合 - 智能分析系统{% endblock %}
+
+{% block content %}
+<div class="container-fluid py-4">
+    <div id="alerts-container"></div>
+
+    <div class="row mb-4">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header d-flex justify-content-between">
+                    <h5 class="mb-0">我的投资组合</h5>
+                    <button class="btn btn-sm btn-outline-primary" data-bs-toggle="modal" data-bs-target="#addStockModal">
+                        <i class="fas fa-plus"></i> 添加股票
+                    </button>
+                </div>
+                <div class="card-body">
+                    <div id="portfolio-empty" class="text-center py-4">
+                        <i class="fas fa-folder-open fa-3x text-muted mb-3"></i>
+                        <p>您的投资组合还是空的，请添加股票</p>
+                        <button class="btn btn-primary" data-bs-toggle="modal" data-bs-target="#addStockModal">
+                            <i class="fas fa-plus"></i> 添加股票
+                        </button>
+                    </div>
+
+                    <div id="portfolio-content" style="display: none;">
+                        <div class="table-responsive">
+                            <table class="table table-hover">
+                                <thead>
+                                    <tr>
+                                        <th>代码</th>
+                                        <th>名称</th>
+                                        <th>行业</th>
+                                        <th>持仓比例</th>
+                                        <th>当前价格</th>
+                                        <th>今日涨跌</th>
+                                        <th>综合评分</th>
+                                        <th>建议</th>
+                                        <th>操作</th>
+                                    </tr>
+                                </thead>
+                                <tbody id="portfolio-table">
+                                    <!-- 投资组合数据将在JS中动态填充 -->
+                                </tbody>
+                            </table>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="portfolio-analysis" class="row mb-4" style="display: none;">
+        <div class="col-md-6">
+            <div class="card h-100">
+                <div class="card-header">
+                    <h5 class="mb-0">投资组合评分</h5>
+                </div>
+                <div class="card-body">
+                    <div class="row">
+                        <div class="col-md-4 text-center">
+                            <div id="portfolio-score-chart"></div>
+                            <h4 id="portfolio-score" class="mt-2">--</h4>
+                            <p class="text-muted">综合评分</p>
+                        </div>
+                        <div class="col-md-8">
+                            <h5 class="mb-3">维度评分</h5>
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>技术面</span>
+                                    <span id="technical-score">--/40</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="technical-progress" class="progress-bar bg-info" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>基本面</span>
+                                    <span id="fundamental-score">--/40</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="fundamental-progress" class="progress-bar bg-success" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                            <div class="mb-3">
+                                <div class="d-flex justify-content-between mb-1">
+                                    <span>资金面</span>
+                                    <span id="capital-flow-score">--/20</span>
+                                </div>
+                                <div class="progress">
+                                    <div id="capital-flow-progress" class="progress-bar bg-warning" role="progressbar" style="width: 0%"></div>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+        <div class="col-md-6">
+            <div class="card h-100">
+                <div class="card-header">
+                    <h5 class="mb-0">行业分布</h5>
+                </div>
+                <div class="card-body">
+                    <div id="industry-chart"></div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="portfolio-recommendations" class="row mb-4" style="display: none;">
+        <div class="col-12">
+            <div class="card">
+                <div class="card-header">
+                    <h5 class="mb-0">投资建议</h5>
+                </div>
+                <div class="card-body">
+                    <ul class="list-group" id="recommendations-list">
+                        <!-- 投资建议将在JS中动态填充 -->
+                    </ul>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- 添加股票模态框 -->
+<div class="modal fade" id="addStockModal" tabindex="-1" aria-labelledby="addStockModalLabel" aria-hidden="true">
+    <div class="modal-dialog">
+        <div class="modal-content">
+            <div class="modal-header">
+                <h5 class="modal-title" id="addStockModalLabel">添加股票到投资组合</h5>
+                <button type="button" class="btn-close" data-bs-dismiss="modal" aria-label="Close"></button>
+            </div>
+            <div class="modal-body">
+                <form id="add-stock-form">
+                    <div class="mb-3">
+                        <label for="add-stock-code" class="form-label">股票代码</label>
+                        <input type="text" class="form-control" id="add-stock-code" required>
+                    </div>
+                    <div class="mb-3">
+                        <label for="add-stock-weight" class="form-label">持仓比例 (%)</label>
+                        <input type="number" class="form-control" id="add-stock-weight" min="1" max="100" value="10" required>
+                    </div>
+                </form>
+            </div>
+            <div class="modal-footer">
+                <button type="button" class="btn btn-secondary" data-bs-dismiss="modal">取消</button>
+                <button type="button" class="btn btn-primary" id="add-stock-btn">添加</button>
+            </div>
+        </div>
+    </div>
+</div>
+{% endblock %}
+
+{% block scripts %}
+<script>
+    // 投资组合数据
+    let portfolio = [];
+    let portfolioAnalysis = null;
+
+    $(document).ready(function() {
+        // 从本地存储加载投资组合
+        loadPortfolio();
+
+        // 添加股票按钮点击事件
+        $('#add-stock-btn').click(function() {
+            addStockToPortfolio();
+        });
+    });
+
+    // 从本地存储加载投资组合
+    function loadPortfolio() {
+        const savedPortfolio = localStorage.getItem('portfolio');
+        if (savedPortfolio) {
+            portfolio = JSON.parse(savedPortfolio);
+            renderPortfolio(); // 先用缓存数据渲染一次，避免白屏
+            
+            // 为每个股票获取最新数据
+            portfolio.forEach((stock, index) => {
+                fetchStockData(stock.stock_code);
+            });
+        }
+    }
+
+    // 渲染投资组合
+    function renderPortfolio() {
+        if (portfolio.length === 0) {
+            $('#portfolio-empty').show();
+            $('#portfolio-content').hide();
+            $('#portfolio-analysis').hide();
+            $('#portfolio-recommendations').hide();
+            return;
+        }
+
+        $('#portfolio-empty').hide();
+        $('#portfolio-content').show();
+        $('#portfolio-analysis').show();
+        $('#portfolio-recommendations').show();
+
+        let html = '';
+        portfolio.forEach((stock, index) => {
+            const scoreClass = getScoreColorClass(stock.score || 0);
+            const priceChangeClass = (stock.price_change || 0) >= 0 ? 'trend-up' : 'trend-down';
+            const priceChangeIcon = (stock.price_change || 0) >= 0 ? '<i class="fas fa-caret-up"></i>' : '<i class="fas fa-caret-down"></i>';
+
+            // 显示加载状态或实际数据
+            const stockName = stock.loading ?
+                '<span class="text-muted"><i class="fas fa-spinner fa-pulse"></i> 加载中...</span>' :
+                (stock.stock_name || '未知');
+
+            const industryDisplay = stock.industry || '-';
+
+            html += `
+                <tr>
+                    <td>${stock.stock_code}</td>
+                    <td>${stockName}</td>
+                    <td>${industryDisplay}</td>
+                    <td>${stock.weight}%</td>
+                    <td>${stock.price ? formatNumber(stock.price, 2) : '-'}</td>
+                    <td class="${priceChangeClass}">${stock.price_change ? (priceChangeIcon + ' ' + formatPercent(stock.price_change, 2)) : '-'}</td>
+                    <td><span class="badge ${scoreClass}">${stock.score || '-'}</span></td>
+                    <td>${stock.recommendation || '-'}</td>
+                    <td>
+                        <div class="btn-group btn-group-sm" role="group">
+                            <a href="/stock_detail/${stock.stock_code}" class="btn btn-outline-primary">
+                                <i class="fas fa-chart-line"></i>
+                            </a>
+                            <button type="button" class="btn btn-outline-danger" onclick="removeStock(${index})">
+                                <i class="fas fa-trash"></i>
+                            </button>
+                        </div>
+                    </td>
+                </tr>
+            `;
+        });
+
+        $('#portfolio-table').html(html);
+    }
+
+    // 添加股票到投资组合
+    function addStockToPortfolio() {
+        const stockCode = $('#add-stock-code').val().trim();
+        const weight = parseInt($('#add-stock-weight').val() || 10);
+
+        if (!stockCode) {
+            showError('请输入股票代码');
+            return;
+        }
+
+        // 检查是否已存在
+        const existingIndex = portfolio.findIndex(s => s.stock_code === stockCode);
+        if (existingIndex >= 0) {
+            showError('此股票已在投资组合中');
+            return;
+        }
+
+        // 添加到投资组合
+        portfolio.push({
+            stock_code: stockCode,
+            weight: weight,
+            stock_name: '加载中...',
+            industry: '-',
+            price: null,
+            price_change: null,
+            score: null,
+            recommendation: null,
+            loading: true,
+            isNew: true  // 标记为新添加的股票
+        });
+
+        savePortfolio();
+        $('#addStockModal').modal('hide');
+        $('#add-stock-form')[0].reset();
+        fetchStockData(stockCode);
+    }
+
+    // 添加重试加载功能
+    function retryFetchStockData(stockCode) {
+        showInfo(`正在重新获取 ${stockCode} 的数据...`);
+        fetchStockData(stockCode);
+    }
+
+    // 在渲染函数中添加重试按钮
+    html += `
+        <tr>
+            <td>${stock.stock_code}</td>
+            <td>${stockName} ${stock.stock_name === '获取失败' ?
+                `<button class="btn btn-sm btn-link p-0 ml-2" onclick="retryFetchStockData('${stock.stock_code}')">
+                    <i class="fas fa-sync-alt"></i> 重试
+                </button>` : ''}
+            </td>
+            ...
+    `;
+
+    // 获取股票数据
+    function fetchStockData(stockCode) {
+        const index = portfolio.findIndex(s => s.stock_code === stockCode);
+        if (index < 0) return;
+
+        // 显示加载状态
+        portfolio[index].loading = true;
+        savePortfolio();
+        renderPortfolio();
+
+        $.ajax({
+            url: '/analyze',
+            type: 'POST',
+            contentType: 'application/json',
+            data: JSON.stringify({
+                stock_codes: [stockCode],
+                market_type: 'A'
+            }),
+            success: function(response) {
+                if (response.results && response.results.length > 0) {
+                    const result = response.results[0];
+
+                    portfolio[index].stock_name = result.stock_name || '未知';
+                    portfolio[index].industry = result.industry || '未知';
+                    portfolio[index].price = result.price || 0;
+                    portfolio[index].price_change = result.price_change || 0;
+                    portfolio[index].score = result.score || 0;
+                    portfolio[index].recommendation = result.recommendation || '-';
+                    portfolio[index].loading = false;
+
+                    savePortfolio();
+                    analyzePortfolio();
+                    
+                    // 只在添加新股票时显示成功消息
+                    if (portfolio[index].isNew) {
+                        showSuccess(`已添加 ${result.stock_name || stockCode} 到投资组合`);
+                        portfolio[index].isNew = false;
+                    }
+                } else {
+                    portfolio[index].stock_name = '数据获取失败';
+                    portfolio[index].loading = false;
+                    savePortfolio();
+                    renderPortfolio();
+                    showError(`获取股票 ${stockCode} 数据失败`);
+                }
+            },
+            error: function(error) {
+                portfolio[index].stock_name = '获取失败';
+                portfolio[index].loading = false;
+                savePortfolio();
+                renderPortfolio();
+                showError(`获取股票 ${stockCode} 数据失败`);
+            }
+        });
+    }
+
+    // 从投资组合中移除股票
+    function removeStock(index) {
+        if (confirm('确定要从投资组合中移除此股票吗？')) {
+            portfolio.splice(index, 1);
+            savePortfolio();
+            renderPortfolio();
+            analyzePortfolio();
+        }
+    }
+
+    // 保存投资组合到本地存储
+    function savePortfolio() {
+        localStorage.setItem('portfolio', JSON.stringify(portfolio));
+        renderPortfolio();
+    }
+
+
+    // 分析投资组合
+    function analyzePortfolio() {
+        if (portfolio.length === 0) return;
+
+        // 计算投资组合评分
+        let totalScore = 0;
+        let totalWeight = 0;
+        let industriesMap = {};
+
+        portfolio.forEach(stock => {
+            if (stock.score) {
+                totalScore += stock.score * stock.weight;
+                totalWeight += stock.weight;
+
+                // 统计行业分布
+                const industry = stock.industry || '其他';
+                if (industriesMap[industry]) {
+                    industriesMap[industry] += stock.weight;
+                } else {
+                    industriesMap[industry] = stock.weight;
+                }
+            }
+        });
+
+        // 确保总权重不为零
+        if (totalWeight > 0) {
+            const portfolioScore = Math.round(totalScore / totalWeight);
+
+            // 更新评分显示
+            $('#portfolio-score').text(portfolioScore);
+
+            // 简化的维度评分计算
+            const technicalScore = Math.round(portfolioScore * 0.4);
+            const fundamentalScore = Math.round(portfolioScore * 0.4);
+            const capitalFlowScore = Math.round(portfolioScore * 0.2);
+
+            $('#technical-score').text(technicalScore + '/40');
+            $('#fundamental-score').text(fundamentalScore + '/40');
+            $('#capital-flow-score').text(capitalFlowScore + '/20');
+
+            $('#technical-progress').css('width', (technicalScore / 40 * 100) + '%');
+            $('#fundamental-progress').css('width', (fundamentalScore / 40 * 100) + '%');
+            $('#capital-flow-progress').css('width', (capitalFlowScore / 20 * 100) + '%');
+
+            // 更新投资组合评分图表
+            renderPortfolioScoreChart(portfolioScore);
+
+            // 更新行业分布图表
+            renderIndustryChart(industriesMap);
+
+            // 生成投资建议
+            generateRecommendations(portfolioScore);
+        }
+    }
+
+    // 渲染投资组合评分图表
+    function renderPortfolioScoreChart(score) {
+        const options = {
+            series: [score],
+            chart: {
+                height: 150,
+                type: 'radialBar',
+            },
+            plotOptions: {
+                radialBar: {
+                    hollow: {
+                        size: '70%',
+                    },
+                    dataLabels: {
+                        show: false
+                    }
+                }
+            },
+            colors: [getScoreColor(score)],
+            stroke: {
+                lineCap: 'round'
+            }
+        };
+
+        // 清除旧图表
+        $('#portfolio-score-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#portfolio-score-chart"), options);
+        chart.render();
+    }
+
+    // 渲染行业分布图表
+    function renderIndustryChart(industriesMap) {
+        // 转换数据格式为图表所需
+        const seriesData = [];
+        const labels = [];
+
+        for (const industry in industriesMap) {
+            if (industriesMap.hasOwnProperty(industry)) {
+                seriesData.push(industriesMap[industry]);
+                labels.push(industry);
+            }
+        }
+
+        const options = {
+            series: seriesData,
+            chart: {
+                type: 'pie',
+                height: 300
+            },
+            labels: labels,
+            responsive: [{
+                breakpoint: 480,
+                options: {
+                    chart: {
+                        height: 200
+                    },
+                    legend: {
+                        position: 'bottom'
+                    }
+                }
+            }],
+            tooltip: {
+                y: {
+                    formatter: function(value) {
+                        return value + '%';
+                    }
+                }
+            }
+        };
+
+        // 清除旧图表
+        $('#industry-chart').empty();
+
+        const chart = new ApexCharts(document.querySelector("#industry-chart"), options);
+        chart.render();
+    }
+
+    // 生成投资建议
+    function generateRecommendations(portfolioScore) {
+        let recommendations = [];
+
+        // 根据总分生成基本建议
+        if (portfolioScore >= 80) {
+            recommendations.push({
+                text: '您的投资组合整体评级优秀，当前市场环境下建议保持较高仓位',
+                type: 'success'
+            });
+        } else if (portfolioScore >= 60) {
+            recommendations.push({
+                text: '您的投资组合整体评级良好，可以考虑适度增加仓位',
+                type: 'primary'
+            });
+        } else if (portfolioScore >= 40) {
+            recommendations.push({
+                text: '您的投资组合��体评级一般，建议持币观望，等待更好的入场时机',
+                type: 'warning'
+            });
+        } else {
+            recommendations.push({
+                text: '您的投资组合整体评级较弱，建议减仓规避风险',
+                type: 'danger'
+            });
+        }
+
+        // 检查行业集中度
+        const industries = {};
+        let totalWeight = 0;
+
+        portfolio.forEach(stock => {
+            const industry = stock.industry || '其他';
+            if (industries[industry]) {
+                industries[industry] += stock.weight;
+            } else {
+                industries[industry] = stock.weight;
+            }
+            totalWeight += stock.weight;
+        });
+
+        // 计算行业集中度
+        let maxIndustryWeight = 0;
+        let maxIndustry = '';
+
+        for (const industry in industries) {
+            if (industries[industry] > maxIndustryWeight) {
+                maxIndustryWeight = industries[industry];
+                maxIndustry = industry;
+            }
+        }
+
+        const industryConcentration = maxIndustryWeight / totalWeight;
+
+        if (industryConcentration > 0.5) {
+            recommendations.push({
+                text: `行业集中度较高，${maxIndustry}行业占比${Math.round(industryConcentration * 100)}%，建议适当分散投资降低非系统性风险`,
+                type: 'warning'
+            });
+        }
+
+        // 检查需要调整的个股
+        const weakStocks = portfolio.filter(stock => stock.score && stock.score < 40);
+        if (weakStocks.length > 0) {
+            const stockNames = weakStocks.map(s => `${s.stock_name}(${s.stock_code})`).join('、');
+            recommendations.push({
+                text: `以下个股评分较低，建议考虑调整：${stockNames}`,
+                type: 'danger'
+            });
+        }
+
+        const strongStocks = portfolio.filter(stock => stock.score && stock.score > 70);
+        if (strongStocks.length > 0 && portfolioScore < 60) {
+            const stockNames = strongStocks.map(s => `${s.stock_name}(${s.stock_code})`).join('、');
+            recommendations.push({
+                text: `以下个股表现强势，可考虑增加配置比例：${stockNames}`,
+                type: 'success'
+            });
+        }
+
+        // 渲染建议
+        let html = '';
+        recommendations.forEach(rec => {
+            html += `<li class="list-group-item list-group-item-${rec.type}">${rec.text}</li>`;
+        });
+
+        $('#recommendations-list').html(html);
+    }
+
+    // 获取评分颜色
+    function getScoreColor(score) {
+        if (score >= 80) return '#28a745'; // 绿色
+        if (score >= 60) return '#007bff'; // 蓝色
+        if (score >= 40) return '#ffc107'; // 黄色
+        return '#dc3545'; // 红色
+    }
+</script>
+{% endblock %}