Update pycatchs/stls.py

pycatchs/stls.py

@@ -0,0 +1,94 @@
+import yfinance as yf
+import pandas as pd
+from datetime import datetime, timedelta
+from pymongo import MongoClient
+import pytz
+import os 
+
+mongo_url = os.environ['MongoURL']
+df_logo = pd.read_csv('https://raw.githubusercontent.com/jarvisx17/nifty500/main/Nifty500.csv')
+df_logo = df_logo[['Symbol', "logo"]]
+tz = pytz.timezone('Asia/Kolkata')
+def UpdatedCollectionName():
+  current_time = datetime.now(tz)
+  collection_name = current_time.strftime('%Y-%m-%d')
+  if current_time.time() >= datetime.strptime('15:30', '%H:%M').time():
+      collection_name = (current_time + timedelta(days=1)).strftime('%Y-%m-%d')
+      return collection_name
+  else:
+    return collection_name
+
+def get_rsi(close, lookback=14):
+    ret = close.diff()
+    up = []
+    down = []
+    for i in range(len(ret)):
+        if ret.iloc[i] < 0:
+            up.append(0)
+            down.append(ret.iloc[i])
+        else:
+            up.append(ret.iloc[i])
+            down.append(0)
+    up_series = pd.Series(up, index=close.index)
+    down_series = pd.Series(down, index=close.index).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, columns=['RSI'])
+    return rsi_df
+
+def Stocks():
+    # end_date = datetime.today()
+    utc_now = datetime.utcnow()
+    indian_timezone = pytz.timezone('Asia/Kolkata')
+    indian_now = utc_now.replace(tzinfo=pytz.utc).astimezone(indian_timezone)
+    end_date = utc_now
+    end_date = end_date.replace(tzinfo=pytz.utc).astimezone(indian_timezone)
+    end_date = end_date.replace(tzinfo=pytz.utc).astimezone(indian_timezone)
+
+    start_date = end_date - timedelta(days=365)
+    nifty500_symbols = []
+    nifty500 = pd.read_csv('https://archives.nseindia.com/content/indices/ind_nifty500list.csv')
+    for symbol in nifty500.Symbol:
+        symbol = f'{symbol}.NS'
+        nifty500_symbols.append(symbol)
+        
+    nifty500_data = pd.DataFrame()
+    print("Downloading data...")
+    for symbol in nifty500_symbols:
+        try:
+            stock_data = yf.download(symbol, start=start_date, end=end_date, progress=False)
+            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['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)
+    nifty500_data_last_2_rows.reset_index(drop=True, inplace=True)
+    nifty500_data_last_2_rows['Prev_RSI'] = nifty500_data_last_2_rows.groupby('Symbol')['RSI'].shift(1)
+
+    filtered_data_by_stock = []
+    for symbol, group in nifty500_data_last_2_rows.groupby('Symbol'):
+        filtered_stock_data = group[(group['RSI'] >= 60) & (group['Prev_RSI'] < 60)]
+        if not filtered_stock_data.empty:
+            filtered_data_by_stock.append(filtered_stock_data)
+            
+    filtered_data = pd.concat(filtered_data_by_stock)
+    filtered_data.reset_index(drop=True, inplace=True)
+    filtered_data[['Open', 'High','Low', 'Close', 'RSI', 'Prev_RSI','SMA20', 'PercentageChange']] = filtered_data[['Open', 'High','Low', 'Close', 'RSI', 'Prev_RSI', 'SMA20', 'PercentageChange']].round(2)
+    filtered_data = filtered_data.sort_values(by='PercentageChange', ascending=False)
+    filtered_data.reset_index(drop=True, inplace=True)
+    filtered_data = pd.merge(filtered_data, df_logo, on='Symbol', how='inner') 
+    filtered_data = filtered_data[['Symbol', 'Date', 'Open', 'High', 'Low', 'Close', 'RSI', 'Prev_RSI','PercentageChange', "logo"]]
+    client = MongoClient(mongo_url)
+    db = client['mydatabase']
+    collection_name = UpdatedCollectionName()
+    collection = db[collection_name]
+    data_dict = filtered_data.to_dict(orient='records')
+    collection.insert_many(data_dict) 
+    return filtered_data.to_dict(orient="records")