Update stls.py

stls.py

@@ -1,14 +1,54 @@
-import yfinance as yf
 import pandas as pd
 from datetime import datetime, timedelta
 from pymongo import MongoClient
 import pytz
 import os 
+import requests
 
 mongo_url = os.environ['MongoURL']
 df_logo = pd.read_csv('https://raw.githubusercontent.com/jarvisx17/nifty500/main/Stocks.csv')
 df_logo = df_logo[['Symbol','Industry', "logo", "FNO"]]
 tz = pytz.timezone('Asia/Kolkata')
+
+base_url = "https://groww.in/v1/api/charting_service/v3/chart/exchange/NSE/segment/CASH/"
+indian_timezone = pytz.timezone('Asia/Kolkata')
+utc_timezone = pytz.timezone('UTC')
+headers = {
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0'
+}
+
+def get_time_range(days=7):
+    current_time = datetime.now(indian_timezone)
+    start_time = current_time - timedelta(days=days)
+    start_time_utc = start_time.astimezone(pytz.utc)
+    current_time_utc = current_time.astimezone(pytz.utc)
+    start_time_millis = int(start_time_utc.timestamp() * 1000)
+    end_time_millis = int(current_time_utc.timestamp() * 1000)
+    return start_time_millis, end_time_millis
+
+def fetch_stock_data(symbol, interval=15, days=7):
+    start_time, end_time = get_time_range(days)
+    params = {
+        'endTimeInMillis': end_time,
+        'intervalInMinutes': interval,
+        'startTimeInMillis': start_time,
+    }
+    try:
+        print("Downloading data of", symbol.upper())
+        response = requests.get(base_url + symbol.upper(), params=params, headers=headers)
+        response.raise_for_status()
+        data = response.json()
+        columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
+        for row in data['candles']:
+            row[0] = datetime.utcfromtimestamp(row[0])
+        df = pd.DataFrame(data['candles'], columns=columns)
+        df['Date'] = pd.to_datetime(df['Date'])
+        df['Date'] = df['Date'].dt.tz_localize(utc_timezone).dt.tz_convert(indian_timezone)
+        return df
+    except requests.exceptions.RequestException as e:
+        print(f"Error during API request: {e}")
+        return None
+
 def UpdatedCollectionName():
   current_time = datetime.now(tz)
   collection_name = current_time.strftime('%Y-%m-%d')
@@ -35,8 +75,7 @@ def get_rsi(close, lookback=14):
     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, columns=['RSI'])
-    return rsi_df
+    return rsi
 
 def Stocks():
     # end_date = datetime.today()
@@ -58,14 +97,16 @@ def Stocks():
     print("Downloading data...")
     for symbol in nifty500_symbols:
         try:
-            stock_data = yf.download(symbol, start=start_date, end=end_date, progress=False)
+            stock_data = fetch_stock_data(symbol.replace('.NS',''), interval=1440, days=365)
             stock_data['Symbol'] = symbol 
             nifty500_data = pd.concat([nifty500_data, stock_data], axis=0)
         except Exception as e:
             print(f"Error fetching data for {symbol}: {e}")
 
-    nifty500_data.reset_index(inplace=True)
-    nifty500_data['RSI'] = nifty500_data.groupby('Symbol')['Close'].apply(lambda x: get_rsi(x, lookback=14))
+    nifty500_data['RSI'] = (
+        nifty500_data.groupby('Symbol')['Close']
+        .transform(lambda x: get_rsi(x, lookback=14))
+    )
     nifty500_data['SMA20'] = nifty500_data.groupby('Symbol')['Close'].transform(lambda x: x.rolling(window=20).mean())
     nifty500_data['PercentageChange'] = nifty500_data.groupby('Symbol')['Close'].pct_change() * 100
     nifty500_data_last_2_rows = nifty500_data.groupby('Symbol').tail(2)