NSE Stock Fundamental Analysis in Python

 NSE Stock Fundamental Analysis in Python with yfinance library. Here is the full code:

import random

import time

from tqdm import tqdm

import yfinance as yf

import pandas as pd

from datetime import datetime, timedelta

def get_nifty50_symbols():

    nifty50_symbols = ["ACC", "PRSMJOHNSN", "AETHER"]

    return [symbol + ".NS" for symbol in nifty50_symbols]

def get_all_indian_stocks():

    # Fetch list of Indian Stocks

    url = f".\\Nifty_Stocks_Anaysis.csv"

    df = pd.read_csv(url)

    symbols = df['SYMBOL'].tolist()

    return [symbol + ".NS" for symbol in symbols]

def get_stock_data(symbol):

    stock = yf.Ticker(symbol)

    # Get price data

    end_date = datetime.now()

    start_date = end_date - timedelta(days=365)

    price_data = stock.history(start=start_date, end=end_date)

    if price_data.empty:

        return None

    latest_price = price_data.iloc[-1]

    # Calculate percentage changes

    daily_change = (latest_price['Close'] - price_data.iloc[-2]['Close']) / price_data.iloc[-2]['Close'] * 100

    weekly_change = (latest_price['Close'] - price_data.iloc[-2]['Close']) / price_data.iloc[-6]['Close'] * 100

    monthly_change = (latest_price['Close'] - price_data.iloc[-2]['Close']) / price_data.iloc[-22]['Close'] * 100

    # Get Fundamental Data

    info = stock.info

    stock_data = {

        'Symbol': symbol,

        'Company Name': info.get('longName', 'N/A'),

        'Sector': info.get('sector', 'N/A'),

        'Industry': info.get('industry', 'N/A'),

        'Market Cap': info.get('marketcap', 'N/A'),

        'P/E Ratio': info.get('trailingPE', 'N/A'),

        'Forward P/E': info.get('forwardPE', 'N/A'),

        'PEG Ratio': info.get('pegRatio', 'N/A'),

        'Price to Book': info.get('priceToBook', 'N/A'),

        'EV/EBITDA': info.get('enterpriseToEbitda', 'N/A'),

        'Profit Margin': info.get('profitMargins', 'N/A'),

        'Operating Margin': info.get('operatingMargins', 'N/A'),

        'ROE': info.get('returnOnEquity', 'N/A'),

        'ROA': info.get('returnOnAssets', 'N/A'),

        'Revenue': info.get('totalRevenue', 'N/A'),

        'Revenue Per Share': info.get('revenuePerShare', 'N/A'),

        'Quarterly Revenue Growth': info.get('quarterlyRevenueGrowth', 'N/A'),

        'Gross Profit': info.get('grossProfits', 'N/A'),

        'EBITDA': info.get('ebitda', 'N/A'),

        'Net Income': info.get('netIncomeToCommon', 'N/A'),

        'EPS': info.get('trailingEps', 'N/A'),

        'Quarterly Earnings Growth': info.get('quarterlyEarningsGrowth', 'N/A'),

        'Total Cash': info.get('totalCash', 'N/A'),

        'Total Debt': info.get('totalDebt', 'N/A'),

        'Debt To Equity': info.get('debtToEquity', 'N/A'),

        'Current Ratio': info.get('currentRatio', 'N/A'),

        'Book Value': info.get('bookValue', 'N/A'),

        'Free Cash Flow': info.get('freeCashFlow', 'N/A'),

        'Dividend Rate': info.get('dividendRate', 'N/A'),

        'Dividend Yield': info.get('dividendYield', 'N/A    '),

        'Payout Ratio': info.get('payoutRatio', 'N/A'),

        'Beta': info.get('beta', 'N/A'),

        '52 Week High': info.get('fiftyTwoWeekHigh', 'N/A'),

        '52 Week Low': info.get('fiftyTwoWeekLow', 'N/A'),

        '50 Day Average': info.get('fiftyDayAverage', 'N/A'),

        '200 Day Average': info.get('twoHundredDayAverage', 'N/A'),

        'Daily Change %': f"{daily_change:.2f}%",

        'Weekly Change %': f"{weekly_change:.2f}%",

        'Monthly Change %': f"{monthly_change:.2f}%",

        'Volume': latest_price['Volume']

    }

    return stock_data

def main():

    """

    nifty50_symbols = get_nifty50_symbols()

    all_stock_data = []

    for symbol in nifty50_symbols:

        print(f"Fetching data for {symbol}...")

        try:

            stock_data = get_stock_data(symbol)

            if stock_data:

                all_stock_data.append(stock_data)

                print(f"Data Fetched successfully for {symbol}")

            else:

                print(f"No data available for {symbol}")

        except Exception as e:

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

            all_stock_data.append({'Symbol': symbol, 'Error': str(e)})

    # Convert to Dataframe and Save to CSV

    df = pd.DataFrame(all_stock_data)

    csv_file = 'nifty50_stock_data.csv'

    df.to_csv(csv_file, index=False)

    print(f"Data Saved to CSV file {csv_file}")

"""

    all_symbols = get_all_indian_stocks()

    print(f"Total Number of Stocks: {len(all_symbols)}")

    all_stock_data = []

    # Create a tqdm Progress Bar

    pbar = tqdm(total=len(all_symbols), desc="Processing Stocks", unit="stock")

    for symbol in all_symbols:

        try:

            stock_data = get_stock_data(symbol)

            if stock_data:

                all_stock_data.append(stock_data)

                pbar.set_postfix_str(f"Fetched Data for Symbol {symbol}")

            else:

                pbar.set_postfix_str(f"No Data Available for Symbol {symbol}")

        except Exception as e:

            pbar.set_postfix_str(f"Error fetching data for {symbol} : {str(e)}")

            all_stock_data.append({'Symbol': symbol, 'Error': str(e)})

        # Update the progress bar

        pbar.update(1)

        # add a small random delay between request

        time.sleep(random.uniform(1, 3))

    # Close the progress bar

    pbar.close()

    # Save all data to a single CSV file

    df = pd.DataFrame(all_stock_data)

    csv_file = 'all_indian_stocks_data.csv'

    df.to_csv(csv_file, index=False)

    print(f"Data Saved to CSV file {csv_file}")

    print(f"All stocks are processed and saved to a single csv file ")

if __name__ == '__main__':

    main()

"""

name_of_file = f"./All_Stocks_Anaysis_9_Sep_2024_5_Days.xlsx"

data = pd.read_excel(name_of_file)

required_colum_name = "Stock_Symbol"

print(data[required_colum_name])

ticker = yf.Ticker('ADANIPOWER.nse')

print(dir(ticker))

for k, v in ticker.info.items():

    print(k, '\t', v, '\n')

"""