stls.py

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")