Nifty50_top10_ranking_strategy

import yfinance as yf

import pandas as pd

def get_nifty50_symbols():

# You can fetch Nifty 50 symbols from NSE or any other source

# For this example, I'll use a static list of symbols

return ["ADANIPORTS.NS", "ASIANPAINT.NS", "AXISBANK.NS", "BAJAJ-AUTO.NS", "BAJAJFINSV.NS",

"BAJFINANCE.NS", "BHARTIARTL.NS", "BPCL.NS", "BRITANNIA.NS", "CIPLA.NS", "COALINDIA.NS",

"DIVISLAB.NS", "DRREDDY.NS", "EICHERMOT.NS", "GRASIM.NS", "HCLTECH.NS", "LICI.NS",

"HDFCBANK.NS", "HDFCLIFE.NS", "HEROMOTOCO.NS", "HINDALCO.NS", "HINDUNILVR.NS",

"ICICIBANK.NS", "INDUSINDBK.NS", "INFY.NS", "IOC.NS", "ITC.NS", "JSWSTEEL.NS",

"KOTAKBANK.NS", "LT.NS", "M&M.NS", "MARUTI.NS", "NESTLEIND.NS", "NTPC.NS", "ONGC.NS",

"POWERGRID.NS", "RELIANCE.NS", "SBILIFE.NS", "SBIN.NS", "SHREECEM.NS", "SUNPHARMA.NS",

"TATACONSUM.NS", "TATAMOTORS.NS", "TATASTEEL.NS", "TCS.NS", "TECHM.NS", "TITAN.NS",

"ULTRACEMCO.NS", "UPL.NS", "WIPRO.NS"]

def calculate_momentum(data):

data['1 Year % Change'] = (data['Adj Close'] - data['Adj Close'].shift(252)) / data['Adj Close'].shift(252) * 100

return data

def main():

# Fetch historical data for Nifty 50 stocks

nifty200_symbols = get_nifty50_symbols()

nifty200_data = {}

for symbol in nifty200_symbols:

try:

stock_data = yf.download(symbol, start="2022-06-01", end="2024-06-01")

nifty200_data[symbol] = calculate_momentum(stock_data)

except Exception as e:

print(f"Error fetching data for {symbol}: {e}")

# Calculate momentum for each stock and select top 10

momentum_stocks = []

for symbol, data in nifty200_data.items():

momentum_score = data['1 Year % Change'].iloc[-1]

momentum_stocks.append({'Symbol': symbol, 'Momentum Score': momentum_score})

momentum_df = pd.DataFrame(momentum_stocks)

top_10_momentum_stocks = momentum_df.sort_values(by='Momentum Score', ascending=False).head(10)

top_10_bearish_stocks = momentum_df.sort_values(by='Momentum Score', ascending=True).head(10)