A Coding Guide to Build a Finance Analytics Tool for Extracting Yahoo Finance Data, Computing Financial Analysis, and Creating Custom PDF Reports

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()