| from fastapi import FastAPI, Request | |
| from fastapi.responses import HTMLResponse | |
| from fastapi.templating import Jinja2Templates | |
| app = FastAPI() | |
| templates = Jinja2Templates(directory="templates") | |
| async def read_root(request: Request): | |
| return templates.TemplateResponse("hello.html", {"request": request}) | |
| # from fastapi import FastAPI, Request, HTTPException | |
| # from fastapi.middleware.cors import CORSMiddleware | |
| # import warnings | |
| # import yfinance as yf | |
| # import pandas as pd | |
| # import requests | |
| # warnings.simplefilter(action='ignore', category=FutureWarning) | |
| # warnings.filterwarnings('ignore') | |
| # df_logo = pd.read_csv("https://raw.githubusercontent.com/jarvisx17/nifty500/main/Nifty500.csv") | |
| # async def calculate_profit(ltp, share, entry): | |
| # tgt1 = entry + (0.02 * entry) | |
| # tgt2 = entry + (0.04 * entry) | |
| # if ltp > tgt2: | |
| # profit = round((share / 3 * (tgt1-entry)) + (share / 3 * (tgt2-entry)) + (share / 3 * (ltp-entry)), 2) | |
| # elif ltp > tgt1 and ltp < tgt2: | |
| # profit = round((share / 3 * (tgt1-entry)) + ((share / 3) * 2 * (ltp-entry)), 2) | |
| # elif ltp > tgt1: | |
| # profit = round(share * (ltp-entry), 2) | |
| # else: | |
| # profit = round(share * (ltp-entry), 2) | |
| # return profit | |
| # async def info(ticker): | |
| # data = df_logo[df_logo['Symbol'] == ticker] | |
| # logo = data.logo.values[0] | |
| # Industry = data.Industry.values[0] | |
| # return logo, Industry | |
| # async def calculate_percentage_loss(buying_price, ltp): | |
| # percentage_loss = ((ltp - buying_price) / buying_price) * 100 | |
| # return f"{percentage_loss:.2f}%" | |
| # async def latestprice(ticker): | |
| # ticker = ticker.split(".")[0] | |
| # url = f"https://stock-market-lo24myw5sq-el.a.run.app/currentprice?ticker={ticker}" | |
| # response = requests.get(url) | |
| # if response.status_code == 200: | |
| # data = response.json() | |
| # return data['ltp'] | |
| # else: | |
| # return "N/A" | |
| # async def process_dataframe(df): | |
| # def get_rsi(close, lookback): | |
| # ret = close.diff() | |
| # up = [] | |
| # down = [] | |
| # for i in range(len(ret)): | |
| # if ret[i] < 0: | |
| # up.append(0) | |
| # down.append(ret[i]) | |
| # else: | |
| # up.append(ret[i]) | |
| # down.append(0) | |
| # up_series = pd.Series(up) | |
| # down_series = pd.Series(down).abs() | |
| # up_ewm = up_series.ewm(com=lookback - 1, adjust=False).mean() | |
| # down_ewm = down_series.ewm(com=lookback - 1, adjust=False).mean() | |
| # rs = up_ewm / down_ewm | |
| # rsi = 100 - (100 / (1 + rs)) | |
| # rsi_df = pd.DataFrame(rsi).rename(columns={0: 'RSI'}).set_index(close.index) | |
| # rsi_df = rsi_df.dropna() | |
| # return rsi_df[3:] | |
| # df['RSI'] = get_rsi(df['Close'], 14) | |
| # df['SMA20'] = df['Close'].rolling(window=20).mean() | |
| # df.drop(['Adj Close'], axis=1, inplace=True) | |
| # df = df.dropna() | |
| # return df | |
| # async def fin_data(ticker, startdate): | |
| # ltp = await latestprice(ticker) | |
| # df=yf.download(ticker, period="36mo", progress=False) | |
| # df = await process_dataframe(df) | |
| # df.reset_index(inplace=True) | |
| # df['Prev_RSI'] = df['RSI'].shift(1).round(2) | |
| # df = df.dropna() | |
| # df.reset_index(drop=True, inplace=True) | |
| # df[['Open', 'High', 'Low', 'Close',"RSI","SMA20"]] = df[['Open', 'High', 'Low', 'Close',"RSI", "SMA20"]].round(2) | |
| # df = df[200:] | |
| # df['Target1'] = df['High'] + (df['High'] * 0.02) | |
| # df['Target1'] = df['Target1'].round(2) | |
| # df['Target2'] = df['High'] + (df['High'] * 0.04) | |
| # df['Target2'] = df['Target2'].round(2) | |
| # df['Target3'] = "will announced" | |
| # df['SL'] = df['Low'] | |
| # df['LTP'] = ltp | |
| # date_index = df.loc[df['Date'] == startdate].index[0] | |
| # df = df.loc[date_index-1:] | |
| # df['Date'] = pd.to_datetime(df['Date']) | |
| # df.reset_index(drop=True,inplace=True) | |
| # return df | |
| # async def eqt(ticker, startdate, share_qty = 90): | |
| # df = await fin_data(ticker, startdate) | |
| # logo, Industry = await info(ticker) | |
| # entry = False | |
| # trading = False | |
| # shares_held = 0 | |
| # buy_price = 0 | |
| # target1 = False | |
| # target2 = False | |
| # target3 = False | |
| # tgt1 = 0 | |
| # tgt2 = 0 | |
| # tgt3 = 0 | |
| # total_profit = 0 | |
| # profits = [] | |
| # stop_loss = 0 | |
| # capital_list = [] | |
| # data = {} | |
| # totalshares = share_qty | |
| # ltp = await latestprice(ticker) | |
| # for i in range(1, len(df)-1): | |
| # try: | |
| # if df.at[i, 'RSI'] > 60 and df.at[i - 1, 'RSI'] < 60 and df.at[i, 'High'] < df.at[i + 1, 'High'] and not entry and not trading: | |
| # buy_price = df.at[i, 'High'] | |
| # stop_loss = df.at[i, 'Low'] | |
| # capital = buy_price * share_qty | |
| # capital_list.append(capital) | |
| # shares_held = share_qty | |
| # entdate = df.at[i+1, 'Date'] | |
| # entry_info = {"Date": pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Note": "Entry Successful", "SL": stop_loss} | |
| # entryStock_info = df.iloc[i: i+1].reset_index(drop=True).to_dict(orient='records')[0] # Entry info | |
| # entryStock_info['Date'] = str(pd.to_datetime(df.at[i, 'Date']).strftime('%d-%m-%Y')) | |
| # data['StockInfo'] = {} | |
| # data['StockInfo']['Stock'] = {} | |
| # data['StockInfo']['Stock']['Name'] = ticker | |
| # data['StockInfo']['Stock']['Industry'] = Industry | |
| # data['StockInfo']['Stock']['Logo'] = logo | |
| # data['StockInfo']['Stock']['Status'] = "Active" | |
| # data['StockInfo']['Stock']['Levels'] = "Entry" | |
| # data['StockInfo']['Stock']['Values'] = entryStock_info | |
| # buying_price = entryStock_info['High'] | |
| # ltp = entryStock_info['LTP'] | |
| # data['StockInfo']['Stock']['Values']['Share QTY'] = share_qty | |
| # data['StockInfo']['Stock']['Values']['Invested Amount'] = (share_qty * buy_price).round(2) | |
| # data['StockInfo']['Stock']['Values']['Percentage'] = await calculate_percentage_loss(buying_price, ltp) | |
| # data['StockInfo']['Stock']['Values']['Total P/L'] = await calculate_profit(ltp, totalshares, buy_price) | |
| # data['StockInfo']['Entry'] = entry_info | |
| # entry = True | |
| # trading = True | |
| # if trading and not target1: | |
| # if (df.at[i + 1, 'High'] - buy_price) >= 0.02 * buy_price: | |
| # stop_loss = buy_price | |
| # target1 = True | |
| # tgt1 = 0.02 * buy_price * (share_qty / 3) | |
| # shares_held -= (share_qty / 3) | |
| # total_profit = round(tgt1,2) | |
| # target1_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : round(tgt1,2), "Remaining Shares": shares_held,"Note" : "TGT1 Achieved Successfully", "SL" : stop_loss} | |
| # data['StockInfo']['TGT1'] = target1_info | |
| # data['StockInfo']['Stock']['Values']['SL'] = stop_loss | |
| # data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] + " TGT1" | |
| # data['StockInfo']['Stock']['Values']['Total P/L'] = await calculate_profit(ltp, totalshares, buy_price) | |
| # data['StockInfo']['Entry']['Trade Status'] = "Trading is ongoing...." | |
| # if trading and target1 and not target2: | |
| # if (df.at[i + 1, 'High'] - buy_price) >= 0.04 * buy_price: | |
| # target2 = True | |
| # tgt2 = 0.04 * buy_price * (share_qty / 3) | |
| # total_profit += round(tgt2,2) | |
| # shares_held -= (share_qty / 3) | |
| # data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] + " TGT2" | |
| # data['StockInfo']['Stock']['Values']['Total P/L'] = await calculate_profit(ltp, totalshares, buy_price) | |
| # target2_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : round(tgt2,2), "Remaining Shares": shares_held,"Note" : "TGT2 Achieved Successfully", "SL" : stop_loss} | |
| # data['StockInfo']['TGT2'] = target2_info | |
| # data['StockInfo']['Entry']['Trade Status'] = "Trading is ongoing...." | |
| # if trading and target2 and not target3: | |
| # if (df.at[i + 1, 'Open'] < df.at[i + 1, 'SMA20'] < df.at[i + 1, 'Close']) or (df.at[i + 1, 'Open'] > df.at[i + 1, 'SMA20'] > df.at[i + 1, 'Close']): | |
| # stop_loss = df.at[i + 1, 'Low'] | |
| # data['StockInfo']['Stock']['Values']['SL'] = stop_loss | |
| # if df.at[i + 2, 'Low'] < stop_loss: | |
| # target3 = True | |
| # tgt3 = stop_loss * (share_qty / 3) | |
| # shares_held -= (share_qty / 3) | |
| # total_profit += round(tgt3,2) | |
| # target3_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : round(tgt3,2), "Remaining Shares": shares_held,"Note" : "TGT3 Achieved Successfully", "SL" : stop_loss} | |
| # data['StockInfo']['Stock']['Values']['Target3'] = tgt3 | |
| # data['StockInfo']['TGT3'] = target3_info | |
| # data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] +" TGT3" | |
| # data['StockInfo']['Stock']['Values']['Total P/L'] = await calculate_profit(ltp, totalshares, buy_price) | |
| # data['StockInfo']['TotalProfit'] = {} | |
| # data['StockInfo']['TotalProfit']['Profit'] = total_profit | |
| # data['StockInfo']['Entry']['Trade Status'] = "Trade closed successfully...." | |
| # data['StockInfo']['TotalProfit']['Trade Status'] = "Trade closed successfully...." | |
| # break | |
| # if (df.at[i + 1, 'Low'] < stop_loss and trading and entdate != df.at[i + 1, 'Date']) or stop_loss > ltp: | |
| # profit_loss = (shares_held * stop_loss) - (shares_held * buy_price) | |
| # total_profit += profit_loss | |
| # profits.append(total_profit) | |
| # shares_held = 0 | |
| # if data['StockInfo']['Stock']['Values']['Target3'] == "will announced" : | |
| # data['StockInfo']['Stock']['Values']['Target3'] = "-" | |
| # data['StockInfo']['Stock']['Status'] = "Closed" | |
| # data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] +" SL" | |
| # stoploss_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : total_profit, "SL" : stop_loss, "Remaining Shares": shares_held,"Note" : "SL Hit Successfully"} | |
| # data['StockInfo']['SL'] = stoploss_info | |
| # data['StockInfo']['TotalProfit'] = {} | |
| # data['StockInfo']['TotalProfit']['Profit'] = round(total_profit, 2) | |
| # data['StockInfo']['Stock']['Values']['Total P/L'] = round(total_profit, 2) | |
| # data['StockInfo']['Entry']['Trade Status'] = "Trade closed successfully...." | |
| # data['StockInfo']['TotalProfit']['Trade Status'] = "Trade closed successfully...." | |
| # buy_price = 0 | |
| # entry = False | |
| # trading = False | |
| # target1 = target2 = target3 = False | |
| # tgt1 = tgt2 = tgt3 = 0 | |
| # total_profit = 0 | |
| # break | |
| # except IndexError: | |
| # continue | |
| # if capital_list and profits: | |
| # return data | |
| # else: | |
| # if data: | |
| # return data | |
| # else: | |
| # data['StockInfo'] = {} | |
| # data['StockInfo']['Stock'] = {} | |
| # data['StockInfo']['Stock']['Name'] = ticker | |
| # data['StockInfo']['Stock']['Industry'] = Industry | |
| # data['StockInfo']['Stock']['Logo'] = logo | |
| # data['StockInfo']['Stock']['Status'] = "Waiting for entry" | |
| # entryStock_info = df.iloc[1: 2].reset_index(drop=True).to_dict(orient='records')[0] # Entry info | |
| # entryStock_info['Date'] = str(pd.to_datetime(df.at[1, 'Date']).strftime('%d-%m-%Y')) | |
| # data['StockInfo']['Stock']['Values'] = entryStock_info | |
| # data['StockInfo']['Stock']['Values']['Target3'] = "-" | |
| # data['StockInfo']['Info'] = "Don't buy stock right now...." | |
| # return data | |
| # app = FastAPI() | |
| # origins = ["*"] | |
| # app.add_middleware( | |
| # CORSMiddleware, | |
| # allow_origins=origins, | |
| # allow_credentials=True, | |
| # allow_methods=["*"], | |
| # allow_headers=["*"], | |
| # ) | |
| # @app.get('/') | |
| # def index(): | |
| # return {"message": "welcome to Investify"} | |
| # # @app.post('/process_stock_details') | |
| # # async def process_stock_details(request: Request): | |
| # # data = await request.json() | |
| # # processed_data = { | |
| # # 'symbol': data['symbol'], | |
| # # 'date': data['date'], | |
| # # 'share': data['share'] | |
| # # } | |
| # # return processed_data | |
| # @app.get('/data') | |
| # async def get_data(ticker: str, date: str, qty: int): | |
| # try: | |
| # response = await eqt(ticker, date, qty) | |
| # return response | |
| # except: | |
| # return {"Timeout" : "Error"} | |
| # # @app.post('/data') | |
| # # async def post_data(request: Request): | |
| # # data = await request.json() | |
| # # ticker = data.get('ticker') | |
| # # date = data.get('date') | |
| # # share_qty = data.get('qty') | |
| # # response = data_manager.get_equity_data(ticker, date, share_qty) | |
| # # return response | |
| # # if __name__ == "__main__": | |
| # # import uvicorn | |
| # # uvicorn.run(app, host="0.0.0.0", port=8000) |