याहू वित्त डेटा निकालने, वित्तीय विश्लेषण की गणना करने और कस्टम पीडीएफ रिपोर्ट बनाने के लिए एक वित्त विश्लेषण उपकरण बनाने के लिए एक कोडिंग गाइड – Gadgets Solutions

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages

def compute_indicators(df):
    """
    Computes technical indicators for the DataFrame:
    - 20-day and 50-day Simple Moving Averages (SMA)
    - Bollinger Bands (using 20-day SMA ±2 standard deviations)
    - MACD (12-day EMA minus 26-day EMA) and its 9-day Signal Line
    - RSI (Relative Strength Index) over a 14-day lookback period
    """
    df('SMA20') = df('Close').rolling(window=20).mean()
    df('SMA50') = df('Close').rolling(window=50).mean()
   
    df('STD20') = df('Close').rolling(window=20).std()
    df('UpperBand') = df('SMA20') + 2 * df('STD20')
    df('LowerBand') = df('SMA20') - 2 * df('STD20')


    df('EMA12') = df('Close').ewm(span=12, adjust=False).mean()
    df('EMA26') = df('Close').ewm(span=26, adjust=False).mean()
    df('MACD') = df('EMA12') - df('EMA26')
    df('Signal') = df('MACD').ewm(span=9, adjust=False).mean()
   
    delta = df('Close').diff()
    gain = delta.copy()
    loss = delta.copy()
    gain(gain < 0) = 0
    loss(loss > 0) = 0
    loss = loss.abs()
    avg_gain = gain.rolling(window=14).mean()
    avg_loss = loss.rolling(window=14).mean()
    rs = avg_gain / avg_loss
    df('RSI') = 100 - (100 / (1 + rs))
   
    return df

def create_cover_page(pdf):
    """
    Creates and saves a cover page into the PDF report.
    """
    fig = plt.figure(figsize=(11.69, 8.27))  
    plt.axis('off')
    plt.text(0.5, 0.7, "Financial Analysis Report", fontsize=24, ha="center")
    plt.text(0.5, 0.62, "Analysis of 5 Stocks from Yahoo Finance", fontsize=16, ha="center")
    plt.text(0.5, 0.5, "Includes Technical Indicators: SMA, Bollinger Bands, MACD, RSI", fontsize=12, ha="center")
    plt.text(0.5, 0.4, "Generated with Python and matplotlib", fontsize=10, ha="center")
    pdf.savefig(fig)
    plt.close(fig)

def plot_price_chart(ticker, df):
    """
    Generates a price chart with SMA and Bollinger Bands for a given ticker.
    """
    fig, ax = plt.subplots(figsize=(14, 7))
    ax.plot(df.index, df('Close'), label="Close Price", linewidth=1.5)
    ax.plot(df.index, df('SMA20'), label="SMA (20)", linewidth=1.2)
    ax.plot(df.index, df('SMA50'), label="SMA (50)", linewidth=1.2)
    ax.plot(df.index, df('UpperBand'), label="Upper Bollinger Band", linestyle="--")
    ax.plot(df.index, df('LowerBand'), label="Lower Bollinger Band", linestyle="--")
    ax.fill_between(df.index, df('LowerBand'), df('UpperBand'), color="lightgray", alpha=0.3)
    ax.set_title(f'{ticker}: Price & Moving Averages with Bollinger Bands')
    ax.set_xlabel('Date')
    ax.set_ylabel('Price')
    ax.legend()
    ax.grid(True)
    return fig

def plot_macd_chart(ticker, df):
    """
    Generates a MACD plot for the given ticker.
    """
    fig, ax = plt.subplots(figsize=(14, 5))
    ax.plot(df.index, df('MACD'), label="MACD", linewidth=1.5)
    ax.plot(df.index, df('Signal'), label="Signal Line", linewidth=1.5)
    ax.set_title(f'{ticker}: MACD')
    ax.set_xlabel('Date')
    ax.set_ylabel('MACD')
    ax.legend()
    ax.grid(True)
    return fig

def plot_rsi_chart(ticker, df):
    """
    Generates an RSI plot for the given ticker.
    """
    fig, ax = plt.subplots(figsize=(14, 5))
    ax.plot(df.index, df('RSI'), label="RSI", linewidth=1.5)
    ax.axhline(70, color="red", linestyle="--", linewidth=1, label="Overbought (70)")
    ax.axhline(30, color="green", linestyle="--", linewidth=1, label="Oversold (30)")
    ax.set_title(f'{ticker}: RSI')
    ax.set_xlabel('Date')
    ax.set_ylabel('RSI')
    ax.legend()
    ax.grid(True)
    return fig

def main():
    tickers = ()
    for i in range(5):
        ticker = input(f"Enter ticker #{i+1}: ").upper().strip()
        tickers.append(ticker)
   
    pdf_filename = "financial_report.pdf"
   
    with PdfPages(pdf_filename) as pdf:
        create_cover_page(pdf)


        for ticker in tickers:
            print(f"Downloading data for {ticker} from Yahoo Finance...")
            df = yf.download(ticker, period='1y')
           
            if df.empty:
                print(f"No data found for {ticker}. Skipping to the next ticker.")
                continue
           
            df = compute_indicators(df)
           
            fig_price = plot_price_chart(ticker, df)
            pdf.savefig(fig_price)
            plt.close(fig_price)
           
            fig_macd = plot_macd_chart(ticker, df)
            pdf.savefig(fig_macd)
            plt.close(fig_macd)
           
            fig_rsi = plot_rsi_chart(ticker, df)
            pdf.savefig(fig_rsi)
            plt.close(fig_rsi)
   
    print(f"PDF report generated and saved as '{pdf_filename}'.")

if __name__ == "__main__":
    main()