FinData Explorer: A Step-by-Step Tutorial Using BeautifulSoup, yfinance, matplotlib, ipywidgets, and fpdf for Financial Data Extraction, Interactive Visualization, and Dynamic PDF Report Generation

Building an Advanced Financial Data Reporting Tool

In this tutorial, we will guide you through creating a financial data reporting tool using Google Colab and various Python libraries. You will learn to:

• Scrape live financial data from web pages
• Retrieve historical stock data using yfinance
• Visualize trends with matplotlib
• Integrate an interactive user interface with ipywidgets
• Generate dynamic PDF reports using FPDF

Step 1: Install Required Libraries

Begin by installing the necessary libraries:

!pip install fpdf beautifulsoup4 yfinance ipywidgets

Step 2: Import Libraries

Import the libraries needed for building the financial data tool:

import requests
from bs4 import BeautifulSoup
from fpdf import FPDF
import yfinance as yf
import matplotlib.pyplot as plt
import ipywidgets as widgets
from IPython.display import display, FileLink

Step 3: Generate Financial Report

The following function generates a financial report based on user inputs:

def generate_report(b):
    symbol = symbol_text.value.upper().strip()
    start_date = start_date_picker.value
    end_date = end_date_picker.value
   
    output_area.clear_output()
   
    if not (symbol and start_date and end_date):
        with output_area:
            print("Please provide valid inputs for stock symbol and both dates.")
        return
   
    with output_area:
        print(f"Generating report for {symbol} from {start_date} to {end_date}...")
   
    try:
        stock = yf.Ticker(symbol)
        current_price = stock.info.get('regularMarketPrice', 'N/A')
    except Exception as e:
        current_price = "Error retrieving price"
        with output_area:
            print("Error retrieving current price:", e)
   
    try:
        hist = stock.history(start=start_date, end=end_date)
    except Exception as e:
        hist = None
        with output_area:
            print("Error fetching historical data:", e)
   
    if hist is not None and not hist.empty:
        plt.figure(figsize=(10, 5))
        plt.plot(hist.index, hist['Close'], marker='o', linestyle='-', label="Close Price")
        plt.title(f"{symbol} Historical Closing Prices")
        plt.xlabel("Date")
        plt.ylabel("Close Price (USD)")
        plt.grid(True)
        plt.xticks(rotation=45)
        plt.tight_layout()
        graph_filename = "graph.png"
        plt.savefig(graph_filename)
        plt.show()
    else:
        graph_filename = None
        with output_area:
            print("No historical data available for the selected date range.")
   
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", "B", 16)
    pdf.cell(0, 10, f"Financial Report for {symbol}", ln=True, align="C")
    pdf.ln(10)
   
    pdf.set_font("Arial", size=12)
    pdf.cell(0, 10, f"Current Price: {current_price}", ln=True)
    pdf.cell(0, 10, f"Date Range: {start_date} to {end_date}", ln=True)
    pdf.ln(10)
   
    if graph_filename:
        pdf.cell(0, 10, "Historical Closing Prices:", ln=True)
        pdf.image(graph_filename, w=180)
   
    pdf_filename = "financial_report.pdf"
    pdf.output(pdf_filename)
   
    with output_area:
        print(f"PDF Report generated: {pdf_filename}")
        display(FileLink(pdf_filename))

Step 4: Create User Interface

Set up an interactive user interface using ipywidgets:

symbol_text = widgets.Text(
    value="AAPL",
    description="Stock Symbol:",
    placeholder="e.g., AAPL"
)
start_date_picker = widgets.DatePicker(
    description='Start Date'
)
end_date_picker = widgets.DatePicker(
    description='End Date'
)
generate_button = widgets.Button(
    description="Generate Report",
    button_style='success'
)
output_area = widgets.Output()

generate_button.on_click(generate_report)

display(widgets.VBox([symbol_text, start_date_picker, end_date_picker, generate_button, output_area]))

Conclusion

By following this tutorial, you have successfully integrated web scraping, data analysis, interactive UI design, and PDF report generation into a single Google Colab notebook. This process demonstrates how to utilize Python’s libraries to create a user-friendly financial data tool.

For further assistance or inquiries about managing AI in business, please contact us at hello@itinai.ru.

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