Update main.py

main.py

@@ -1,297 +1,310 @@
-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
-
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse
+from fastapi.templating import Jinja2Templates
 
 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"}
+templates = Jinja2Templates(directory="templates")
+
+@app.get("/", response_class=HTMLResponse)
+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
+# # @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)
+# # if __name__ == "__main__":
+# #     import uvicorn
+# #     uvicorn.run(app, host="0.0.0.0", port=8000)