Why Build Your Own Stock Screener?
Commercial stock screeners like Finviz, TradingView, and Stock Rover are powerful, but they come with steep subscription fees ($25-60 per month), cluttered interfaces, and limitations on how you can filter and analyze data. As a personal investor doing your own research, you might want a screener that focuses on the specific metrics you care about, presents data the way you think about it, and uses AI to generate plain-English analysis summaries instead of raw numbers.
With vibe coding, you can build a personal stock research tool that pulls data from free financial APIs, filters stocks by your preferred fundamental criteria (P/E ratio, revenue growth, profit margins, debt levels), and generates AI-powered summaries that explain what the numbers mean in context. Think of it as your personal research assistant.
Important disclaimer: This is a personal research and educational tool only. It does not provide investment advice, buy/sell recommendations, or trading signals. Stock market investing involves significant risk, including the potential loss of principal. Past performance does not guarantee future results. Always do your own research and consult a licensed financial advisor before making investment decisions. The AI-generated summaries are for informational purposes only and may contain errors.
The value here is in learning to build data-driven applications with AI -- the stock screener is the project, and the skills you learn apply to any domain.
How to Build It: Step-by-Step with Vibe Coding
Open Cursor or Replit Agent and start with: "Build a stock screener dashboard that pulls fundamental data from the Financial Modeling Prep free API. Let me filter stocks by P/E ratio, market cap, revenue growth, and profit margin. Display results in a sortable table with AI-generated analysis summaries for each stock."
The AI generates the application. Then refine with these prompts:
- 1API integration: "Connect to the Financial Modeling Prep API (free tier). Fetch company profiles, financial statements, and key ratios. Cache the data for 24 hours to stay within rate limits. Handle API errors gracefully."
- 2Filter interface: "Create a filter panel with sliders and dropdowns for: P/E ratio (range), market cap (micro/small/mid/large/mega), revenue growth % (min), gross margin % (min), debt-to-equity ratio (max), dividend yield (min). Show the number of matching stocks as filters are applied."
- 3Results table: "Show matching stocks in a sortable, paginated table with columns: ticker, company name, sector, market cap, P/E, revenue growth, profit margin, debt/equity, and a 'Score' column that ranks stocks by how well they match all criteria. Clicking a row opens a detail view."
- 4AI analysis: "For each stock in the detail view, generate an AI summary that explains the company's financial health in plain English. Include strengths, weaknesses, and how it compares to sector averages. Make it clear this is AI-generated analysis, not investment advice."
- 5Watchlist: "Add a watchlist feature where I can save stocks I want to monitor. Show watchlist stocks on the dashboard with their current key metrics and any significant changes since I added them."
Use Claude Code for the API integration and AI summary generation. Use v0 for the filter panel and data table components.
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Key Features and Technical Architecture
Your stock screener combines financial data APIs with AI analysis in a way that commercial tools charge hundreds per year for:
Financial data source: The Financial Modeling Prep API provides free access to fundamental data for thousands of publicly traded companies. The free tier allows 250 requests per day, which is plenty for a personal screener. Alternative free APIs include Alpha Vantage and Yahoo Finance (via unofficial libraries). Your app caches data locally to minimize API calls.
Multi-criteria filtering: The filter engine applies all criteria simultaneously and updates results in real time. The AI generates the filtering logic from your prompt, handling edge cases like missing data (some companies do not report certain metrics) and ensuring the filters work with the data format returned by the API.
Composite scoring: The app calculates a composite score for each stock based on how well it matches your filter criteria. A stock that meets all criteria perfectly scores 100. This scoring system helps you rank results even when many stocks pass your filters. The scoring weights are customizable through the settings page.
AI-generated summaries: When you click on a stock, the app sends its fundamental data to an AI model with a prompt like: "Analyze this company's fundamentals and provide a 150-word summary covering financial health, growth trajectory, and notable strengths or risks. Write for an informed individual investor. This is for educational purposes only." The AI produces readable summaries that explain what the raw numbers mean.
Data visualization: Charts show revenue and earnings trends over the last 5 years, sector comparison charts, and a radar chart comparing the stock's key metrics to sector averages. All generated through vibe coding prompts.
All analysis is clearly labeled as AI-generated and for informational purposes only.
Business Potential
A personal stock screener project opens several doors:
Educational product: Package the screener with educational content about fundamental analysis. "Build Your Own Stock Research Tool" as a $49-79 course teaches both vibe coding and basic financial analysis. The educational angle avoids regulatory issues since you are teaching skills, not giving advice.
Data analysis portfolio piece: Even if you never monetize the screener itself, it is an outstanding portfolio project. It demonstrates API integration, data processing, AI features, and responsive UI design. Hiring managers in fintech love seeing projects like this.
Custom research tools: Financial analysts, wealth managers, and family offices often need custom research dashboards. Your screener demonstrates your ability to build them. Custom financial dashboards command $5,000-25,000 depending on complexity.
Newsletter integration: Build a weekly newsletter that uses your screener to identify stocks matching certain criteria. The newsletter is the product; the screener is the engine. Financial newsletters with AI analysis are in high demand.
Always ensure any commercial use complies with financial regulations in your jurisdiction. Providing personalized investment advice typically requires licensing.
Learn to Build at CodeLeap
A stock screener is one of the most impressive projects you can build with vibe coding -- it combines API integration, data processing, AI features, and sophisticated UI design. At the CodeLeap AI Bootcamp, you learn to build exactly these kinds of data-driven applications.
Our 8-week program teaches you modern web development with AI tools. You will master Cursor, Claude Code, v0, and deployment platforms. Every module is project-based: you build real applications that demonstrate real skills. No prior coding experience is required.
Whether you want to build financial tools, SaaS products, or portfolio projects that land you a developer job, CodeLeap gives you the foundation. Early bird price: $997 (regular $1,297). Join the next cohort and start building applications that work with real data and real AI.