update_predictions.py

import gc
import os
import re
import subprocess
import time
from datetime import datetime, timezone, timedelta
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from binance.client import Client

from model import KronosTokenizer, Kronos, KronosPredictor

# --- Configuration ---
Config = {
    "REPO_PATH": Path(__file__).parent.resolve(),
    "MODEL_PATH": "../Kronos_model",
    "SYMBOL": 'BTCUSDT',
    "INTERVAL": '1h',
    "HIST_POINTS": 360,
    "PRED_HORIZON": 24,
    "N_PREDICTIONS": 30,
    "VOL_WINDOW": 24,
}


def load_model():
    """Loads the Kronos model and tokenizer."""
    print("Loading Kronos model...")
    tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base", cache_dir=Config["MODEL_PATH"])
    model = Kronos.from_pretrained("NeoQuasar/Kronos-small", cache_dir=Config["MODEL_PATH"])
    tokenizer.eval()
    model.eval()
    predictor = KronosPredictor(model, tokenizer, device="cpu", max_context=512)
    print("Model loaded successfully.")
    return predictor


def make_prediction(df, predictor):
    """Generates probabilistic forecasts using the Kronos model."""
    last_timestamp = df['timestamps'].max()
    start_new_range = last_timestamp + pd.Timedelta(hours=1)
    new_timestamps_index = pd.date_range(
        start=start_new_range,
        periods=Config["PRED_HORIZON"],
        freq='H'
    )
    y_timestamp = pd.Series(new_timestamps_index, name='y_timestamp')
    x_timestamp = df['timestamps']
    x_df = df[['open', 'high', 'low', 'close', 'volume', 'amount']]

    with torch.no_grad():
        print("Making main prediction (T=1.0)...")
        begin_time = time.time()
        close_preds_main, volume_preds_main = predictor.predict(
            df=x_df, x_timestamp=x_timestamp, y_timestamp=y_timestamp,
            pred_len=Config["PRED_HORIZON"], T=1.0, top_p=0.95,
            sample_count=Config["N_PREDICTIONS"], verbose=True
        )
        print(f"Main prediction completed in {time.time() - begin_time:.2f} seconds.")

        # print("Making volatility prediction (T=0.9)...")
        # begin_time = time.time()
        # close_preds_volatility, _ = predictor.predict(
        #     df=x_df, x_timestamp=x_timestamp, y_timestamp=y_timestamp,
        #     pred_len=Config["PRED_HORIZON"], T=0.9, top_p=0.9,
        #     sample_count=Config["N_PREDICTIONS"], verbose=True
        # )
        # print(f"Volatility prediction completed in {time.time() - begin_time:.2f} seconds.")
        close_preds_volatility = close_preds_main

    return close_preds_main, volume_preds_main, close_preds_volatility


def fetch_binance_data():
    """Fetches K-line data from the Binance public API."""
    symbol, interval = Config["SYMBOL"], Config["INTERVAL"]
    limit = Config["HIST_POINTS"] + Config["VOL_WINDOW"]

    print(f"Fetching {limit} bars of {symbol} {interval} data from Binance...")
    client = Client()
    klines = client.get_klines(symbol=symbol, interval=interval, limit=limit)

    cols = ['open_time', 'open', 'high', 'low', 'close', 'volume', 'close_time',
            'quote_asset_volume', 'number_of_trades', 'taker_buy_base_asset_volume',
            'taker_buy_quote_asset_volume', 'ignore']
    df = pd.DataFrame(klines, columns=cols)

    df = df[['open_time', 'open', 'high', 'low', 'close', 'volume', 'quote_asset_volume']]
    df.rename(columns={'quote_asset_volume': 'amount', 'open_time': 'timestamps'}, inplace=True)

    df['timestamps'] = pd.to_datetime(df['timestamps'], unit='ms')
    for col in ['open', 'high', 'low', 'close', 'volume', 'amount']:
        df[col] = pd.to_numeric(df[col])

    print("Data fetched successfully.")
    return df


def calculate_metrics(hist_df, close_preds_df, v_close_preds_df):
    """

    Calculates upside and volatility amplification probabilities for the 24h horizon.

    """
    last_close = hist_df['close'].iloc[-1]

    # 1. Upside Probability (for the 24-hour horizon)
    # This is the probability that the price at the end of the horizon is higher than now.
    final_hour_preds = close_preds_df.iloc[-1]
    upside_prob = (final_hour_preds > last_close).mean()

    # 2. Volatility Amplification Probability (over the 24-hour horizon)
    hist_log_returns = np.log(hist_df['close'] / hist_df['close'].shift(1))
    historical_vol = hist_log_returns.iloc[-Config["VOL_WINDOW"]:].std()

    amplification_count = 0
    for col in v_close_preds_df.columns:
        full_sequence = pd.concat([pd.Series([last_close]), v_close_preds_df[col]]).reset_index(drop=True)
        pred_log_returns = np.log(full_sequence / full_sequence.shift(1))
        predicted_vol = pred_log_returns.std()
        if predicted_vol > historical_vol:
            amplification_count += 1

    vol_amp_prob = amplification_count / len(v_close_preds_df.columns)

    print(f"Upside Probability (24h): {upside_prob:.2%}, Volatility Amplification Probability: {vol_amp_prob:.2%}")
    return upside_prob, vol_amp_prob


def create_plot(hist_df, close_preds_df, volume_preds_df):
    """Generates and saves a comprehensive forecast chart."""
    print("Generating comprehensive forecast chart...")
    # plt.style.use('seaborn-v0_8-whitegrid')
    fig, (ax1, ax2) = plt.subplots(
        2, 1, figsize=(15, 10), sharex=True,
        gridspec_kw={'height_ratios': [3, 1]}
    )

    hist_time = hist_df['timestamps']
    last_hist_time = hist_time.iloc[-1]
    pred_time = pd.to_datetime([last_hist_time + timedelta(hours=i + 1) for i in range(len(close_preds_df))])

    ax1.plot(hist_time, hist_df['close'], color='royalblue', label='Historical Price', linewidth=1.5)
    mean_preds = close_preds_df.mean(axis=1)
    ax1.plot(pred_time, mean_preds, color='darkorange', linestyle='-', label='Mean Forecast')
    ax1.fill_between(pred_time, close_preds_df.min(axis=1), close_preds_df.max(axis=1), color='darkorange', alpha=0.2, label='Forecast Range (Min-Max)')
    ax1.set_title(f'{Config["SYMBOL"]} Probabilistic Price & Volume Forecast (Next {Config["PRED_HORIZON"]} Hours)', fontsize=16, weight='bold')
    ax1.set_ylabel('Price (USDT)')
    ax1.legend()
    ax1.grid(True, which='both', linestyle='--', linewidth=0.5)

    ax2.bar(hist_time, hist_df['volume'], color='skyblue', label='Historical Volume', width=0.03)
    ax2.bar(pred_time, volume_preds_df.mean(axis=1), color='sandybrown', label='Mean Forecasted Volume', width=0.03)
    ax2.set_ylabel('Volume')
    ax2.set_xlabel('Time (UTC)')
    ax2.legend()
    ax2.grid(True, which='both', linestyle='--', linewidth=0.5)

    separator_time = hist_time.iloc[-1] + timedelta(minutes=30)
    for ax in [ax1, ax2]:
        ax.axvline(x=separator_time, color='red', linestyle='--', linewidth=1.5, label='_nolegend_')
        ax.tick_params(axis='x', rotation=30)

    fig.tight_layout()
    chart_path = Config["REPO_PATH"] / 'prediction_chart.png'
    fig.savefig(chart_path, dpi=120)
    plt.close(fig)
    print(f"Chart saved to: {chart_path}")


def update_html(upside_prob, vol_amp_prob):
    """

    Updates the index.html file with the latest metrics and timestamp.

    This version uses a more robust lambda function for replacement to avoid formatting errors.

    """
    print("Updating index.html...")
    html_path = Config["REPO_PATH"] / 'index.html'
    now_utc_str = datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')
    upside_prob_str = f'{upside_prob:.1%}'
    vol_amp_prob_str = f'{vol_amp_prob:.1%}'

    with open(html_path, 'r', encoding='utf-8') as f:
        content = f.read()

    # Robustly replace content using lambda functions
    content = re.sub(
        r'(<strong id="update-time">).*?(</strong>)',
        lambda m: f'{m.group(1)}{now_utc_str}{m.group(2)}',
        content
    )
    content = re.sub(
        r'(<p class="metric-value" id="upside-prob">).*?(</p>)',
        lambda m: f'{m.group(1)}{upside_prob_str}{m.group(2)}',
        content
    )
    content = re.sub(
        r'(<p class="metric-value" id="vol-amp-prob">).*?(</p>)',
        lambda m: f'{m.group(1)}{vol_amp_prob_str}{m.group(2)}',
        content
    )

    with open(html_path, 'w', encoding='utf-8') as f:
        f.write(content)
    print("HTML file updated successfully.")


def git_commit_and_push(commit_message):
    """Adds, commits, and pushes specified files to the Git repository."""
    print("Performing Git operations...")
    try:
        os.chdir(Config["REPO_PATH"])
        subprocess.run(['git', 'add', 'prediction_chart.png', 'index.html'], check=True, capture_output=True, text=True)
        commit_result = subprocess.run(['git', 'commit', '-m', commit_message], check=True, capture_output=True, text=True)
        print(commit_result.stdout)
        push_result = subprocess.run(['git', 'push'], check=True, capture_output=True, text=True)
        print(push_result.stdout)
        print("Git push successful.")
    except subprocess.CalledProcessError as e:
        output = e.stdout if e.stdout else e.stderr
        if "nothing to commit" in output or "Your branch is up to date" in output:
            print("No new changes to commit or push.")
        else:
            print(f"A Git error occurred:\n--- STDOUT ---\n{e.stdout}\n--- STDERR ---\n{e.stderr}")


def main_task(model):
    """Executes one full update cycle."""
    print("\n" + "=" * 60 + f"\nStarting update task at {datetime.now(timezone.utc)}\n" + "=" * 60)
    df_full = fetch_binance_data()
    df_for_model = df_full.iloc[:-1]

    close_preds, volume_preds, v_close_preds = make_prediction(df_for_model, model)

    hist_df_for_plot = df_for_model.tail(Config["HIST_POINTS"])
    hist_df_for_metrics = df_for_model.tail(Config["VOL_WINDOW"])

    upside_prob, vol_amp_prob = calculate_metrics(hist_df_for_metrics, close_preds, v_close_preds)
    create_plot(hist_df_for_plot, close_preds, volume_preds)
    update_html(upside_prob, vol_amp_prob)

    commit_message = f"Auto-update forecast for {datetime.now(timezone.utc):%Y-%m-%d %H:%M} UTC"
    git_commit_and_push(commit_message)

    # --- 新增的内存清理步骤 ---
    # 显式删除大的DataFrame对象，帮助垃圾回收器
    del df_full, df_for_model, close_preds, volume_preds, v_close_preds
    del hist_df_for_plot, hist_df_for_metrics

    # 强制执行垃圾回收
    gc.collect()
    # --- 内存清理结束 ---

    print("-" * 60 + "\n--- Task completed successfully ---\n" + "-" * 60 + "\n")


def run_scheduler(model):
    """A continuous scheduler that runs the main task hourly."""
    while True:
        now = datetime.now(timezone.utc)
        next_run_time = (now + timedelta(hours=1)).replace(minute=0, second=5, microsecond=0)
        sleep_seconds = (next_run_time - now).total_seconds()

        if sleep_seconds > 0:
            print(f"Current time: {now:%Y-%m-%d %H:%M:%S UTC}.")
            print(f"Next run at: {next_run_time:%Y-%m-%d %H:%M:%S UTC}. Waiting for {sleep_seconds:.0f} seconds...")
            time.sleep(sleep_seconds)

        try:
            main_task(model)
        except Exception as e:
            print(f"\n!!!!!! A critical error occurred in the main task !!!!!!!")
            print(f"Error: {e}")
            import traceback
            traceback.print_exc()
            print("Retrying in 5 minutes...")
            print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n")
            time.sleep(300)


if __name__ == '__main__':
    model_path = Path(Config["MODEL_PATH"])
    model_path.mkdir(parents=True, exist_ok=True)

    loaded_model = load_model()
    main_task(loaded_model)  # Run once on startup
    run_scheduler(loaded_model)  # Start the schedule