~/blog/stocksScreener
Published on

Building a Stock Screener Using Python and Streamlit

530 words3 min read
Authors

Introduction

In this blog, we’ll explore how I built a Stock Screener — a tool that allows you to screen and rank S&P 500 stocks based on financial metrics like P/E, ROE, EPS Growth, and more. The project merges Python, data visualization, and financial analysis into a no-code, interactive web app powered by Streamlit.

Whether you’re a retail investor, finance student, or data science enthusiast, this app helps you make data-driven stock decisions with clarity and confidence.

stock-screener-ui

Project Overview

This project focuses on factor-based investing — an approach used by quantitative hedge funds and asset managers to group and evaluate stocks based on characteristics like value, quality, and growth.

What You Can Do With It:

  1. Filter stocks based on:
    • Price-to-Earnings (P/E)
    • Return on Equity (ROE)
    • Debt-to-Equity (D/E)
    • EPS Growth
    • Market Cap
  2. Assign custom weights to each factor and generate a Factor Score.
  3. View the top 10 ranked stocks and full filtered list.
  4. Visualize relationships between financial features using:
    • Bar charts
    • Scatter plots
    • Bubble charts
    • Market Cap histograms
  5. Plan your investment with an interactive savings allocator:
    • Input your savings
    • Get recommendations based on Value, Growth, and Quality strategies

Tools and Libraries

  • Streamlit – for building the web app UI
  • Pandas – data manipulation
  • yfinance – stock data ingestion
  • Matplotlib / Seaborn – visualizations
  • Python – the glue holding everything together

Install everything with:

pip install -r requirements.txt

GitHub Repository

Check out the full code here: GitHub Repository

Key Features

1. Financial Filters

Users can interactively filter stocks by financial health indicators:

  • P/E for valuation
  • ROE for profitability
  • D/E for risk
  • EPS Growth for momentum

2. Custom Factor Scoring

You can assign a weight to each metric to match your investing style — whether you're value-driven, growth-focused, or somewhere in between.

3. Visual Analytics

The app comes with rich visualizations:

  • Top 10 stock bar charts
  • ROE vs. P/E scatter plot
  • Market cap distribution
  • Bubble charts for visual stock comparison

4. Investment Planner

Enter your total investable savings and get a personalized investment breakdown across:

  • Value (40%)
  • Quality (35%)
  • Growth (25%)

5. CSV Export

Download your filtered results with a single click to use in Excel, Tableau, or your own analysis workflows.

Challenges and Improvements

  • Live data delays: yfinance can occasionally throttle or slow down large requests. Caching helped speed up reruns.
  • Outlier handling: extremely high or negative values needed to be clipped or filtered for better scoring logic.
  • Sector awareness: adding sector or industry filters could help build more specific screeners.
  • Backtesting (future): evaluating past performance of screened portfolios would take this to the next level.

Conclusion

This Stock Screener is more than just a coding project — it’s a data product designed to empower smarter investing. By combining financial theory with interactive technology, I created a platform that gives anyone the tools to think like a quant and act with insight.

What started as a learning project turned into a full-featured app that touches multiple aspects of finance, data science, and product thinking.

Have feedback or ideas? Open a GitHub issue or connect with me on LinkedIn!