Update main.py

main.py

@@ -1,310 +1,305 @@
-from fastapi import FastAPI, Request
 from fastapi.responses import HTMLResponse
 from fastapi.templating import Jinja2Templates
+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()
-
-templates = Jinja2Templates(directory="templates")
 
-@app.get("/", response_class=HTMLResponse)
-async def read_root(request: Request):
-    return templates.TemplateResponse("hello.html", {"request": request})
+app = FastAPI()
 
+origins = ["*"]
 
-# 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.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})
+
+# @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)