RS indicator scan

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import yfinance as yf

import pandas as pd

import numpy as np

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"]

# Function to download and process stock data for NIFTY 50

def get_nifty50_data(start_date, lookback_period=123):

    # Get NIFTY 50 stock symbols

    # nifty50_symbols = pd.read_html('https://en.wikipedia.org/wiki/NIFTY_50')[2]['Symbol']

    # nifty50_symbols = nifty50_symbols + '.NS'

    nifty50_symbols = get_nifty50_symbols()

    # Download historical data for NIFTY 50 stocks

    print("Downloading data for NIFTY 50 stocks...")

    stocks_data = yf.download(nifty50_symbols, start=start_date)['Adj Close']

    # Download historical data for NIFTY 50 index

    print("Downloading data for NIFTY 50 index...")

    index_data = yf.download('^NSEI', start=start_date)['Adj Close']

    # Calculate returns for the lookback period

    stock_return = stocks_data.iloc[-1] / stocks_data.iloc[-lookback_period]

    index_return = index_data.iloc[-1] / index_data.iloc[-lookback_period]

    # Calculate RS values as the ratio of returns minus 1

    rs_values = stock_return / index_return - 1

    # Rank the stocks based on RS

    rs_ranking = rs_values.sort_values(ascending=False)

    return rs_ranking

# Function to save the ranking to a CSV file

def save_ranking_to_csv(ranking, file_name):

    ranking.to_csv(file_name, header=["Relative Strength"], index_label="Stock")

    print(f"Saved ranking to {file_name}")

# Start date for downloading historical data

start_date = '2022-01-01'

# Calculate RS ranking for NIFTY 50 stocks

nifty50_ranking = get_nifty50_data(start_date)

print("\nTop NIFTY 50 Stocks by Relative Strength (RS):")

print(nifty50_ranking)

# Save the ranking to a CSV file

# save_ranking_to_csv(nifty50_ranking, "nifty50_rs_ranking.csv")

filepath = r"/content/drive/MyDrive/Quantzaar-tradingS, InvestmentS, Trade&investMyTheory/Backtesting/nifty50_rs_ranking.csv"

nifty50_ranking.to_csv(filepath, header=["Relative Strength"], index_label="Stock")

OUTPUT sample:

[*********************100%%**********************]  50 of 50 completed

[*********************100%%**********************]  1 of 1 completed

Top NIFTY 50 Stocks by Relative Strength (RS):

Ticker

M&M.NS           4.678069

POWERGRID.NS     2.744263

SBIN.NS          2.464280

DIVISLAB.NS      2.425309

ONGC.NS          2.414882

BPCL.NS          2.373599

COALINDIA.NS     2.207892

NTPC.NS          2.192894

MARUTI.NS        2.191889

ADANIPORTS.NS    2.128205

EICHERMOT.NS     2.104814

TATAMOTORS.NS    2.076403

GRASIM.NS        1.859392

BHARTIARTL.NS    1.846706

BAJAJ-AUTO.NS    1.828874

LICI.NS          1.700953

IOC.NS           1.651606

SBILIFE.NS       1.637801