About Gruntech

What is Gruntech?

Gruntech is an automated stock analysis platform that scores over 3,700 quality US stocks using AWS Nova. Instead of relying on gut feelings or tips from friends, this system uses AI to analyze financial fundamentals, generate an objective score (0-100), and explain the results in plain English with specific numbers.

The goal: Help everyday investors (like my classmates or parents) understand which stocks might be worth researching further, without needing a finance degree.

The Challenge

Most stock analysis tools are either too complicated (Bloomberg terminals cost $20,000/year) or too simplistic (just showing price charts). I wanted to build something in between: powerful enough to be useful, but simple enough that anyone could understand it.

The Solution

A Pure AI Scoring system powered by AWS Nova:

• Every stock gets AI analysis: All 3,700+ stocks in our filtered universe are analyzed by AWS Nova using Warren Buffett-style fundamental analysis.
• AI Score (0-100) with decimals: Each stock receives a precise score (like 73.4, 67.8) based on capital efficiency (ROIC), financial health, valuation, growth trajectory, and risk factors.
• Detailed Analysis with Numbers: The AI provides 5-7 sentence assessments citing specific metrics (e.g., "$1.2B free cash flow", "28% ROIC") and explaining what they mean for investors.
• Beginner-Friendly Explanations: Each analysis covers both strengths and risks in plain English, with a company description and one memorable key insight.
• Reference Indicators: Classic metrics like Piotroski F-Score, Altman Z-Score, and ROIC are displayed for additional context.

The AI acts like a professional analyst, reviewing each company's financial statements and providing a clear recommendation with reasoning you can understand.

Important note: The system does not use analyst expectations in scoring. It relies on reported fundamentals and real financial data.

Backend (Python)

All the data processing, scoring logic, and AI integration is written in Python. Why Python?

• Great libraries for finance (NumPy, Pandas)
• Easy to read and debug
• Native AWS Lambda support
• Cursor AI excels at Python

Frontend (Vanilla JavaScript)

The website is built with pure HTML, CSS, and JavaScript, no complex frameworks. This keeps it fast, easy to understand, and perfect for learning.

• Responsive design works on mobile and desktop
• PWA (Progressive Web App) support for installing on phones
• Chart.js for price history graphs
• Hosted on S3 + CloudFront for global CDN

Infrastructure as Code (Terraform)

Instead of manually clicking through AWS console, all infrastructure is defined in Terraform code. Benefits:

• Reproducible - can recreate entire system from code
• Version controlled - track changes over time
• Testable - deploy to test environment first
• Documented - code describes the architecture

Data Sources

Financial data comes from Alpha Vantage, a professional financial data provider that delivers:

• Company fundamentals (revenue, earnings, debt, cash flow, etc.)
• Financial ratios (P/E, P/B, ROE, ROIC, etc.)
• Technical indicators and price history
• Comprehensive coverage of 5,600+ US stocks
• Real-time and historical market data

Key Takeaways

• You don't need to know how to code - AI tools like Cursor and Kiro can generate all the code from your descriptions
• But you do need to understand concepts - how databases work, what APIs are, how cloud infrastructure connects together
• Break big problems into small pieces - I didn't build this all at once; I started with one stock, then 10, then 5,600+
• Ask lots of questions - the more specific your questions to the AI, the better the results

What I Learned About Investing

• Financial Statements: Balance sheets, income statements, and cash flow tell the story of a company's health
• Valuation Metrics: P/E ratios, P/B ratios, and ROE help determine if a stock is cheap or expensive
• Risk Indicators: Piotroski F-Score, Altman Z-Score, and Beneish M-Score help identify risky or potentially fraudulent companies

Challenge 1: API Rate Limits

Problem: Market-data APIs have strict rate limits. I needed to fetch reliable data for 5,600+ stocks without getting throttled.

Solution: Implemented batching, retry/backoff, and a self-invoking orchestrator that can run safely over multiple Lambda invocations.

Challenge 2: Data Inconsistencies

Problem: Some stocks had percentage values as decimals (0.15), others as whole numbers (15), causing wildly incorrect scores.

Solution: Built normalization functions to detect and standardize data formats before scoring. Added validation checks and confidence indicators.

Challenge 3: Cold Start Times

Problem: Lambda functions took 3-5 seconds to start when not recently used, making the API feel slow.

Solution: Added CloudFront caching for static assets, optimized Lambda package size, and implemented database query optimization with Global Secondary Indexes.

Challenge 4: Cost Control

Problem: AWS can get expensive if not managed carefully.

Solution: Used serverless architecture (pay-per-use), set billing alarms, optimized database queries, and implemented caching. Total cost: ~$10/month.

Short-Term (Next 3 Months)

• Add international stocks (UK, Europe, Asia)
• Implement user accounts and watchlists
• Add email alerts for score changes
• Mobile app (React Native)

Long-Term (Next Year)

• Backtesting engine to validate scoring methodology
• Machine learning to improve AI scoring accuracy
• Community features (share stock ideas, discuss)
• Premium tier with real-time updates

AI Changes Everything

A few years ago, building something like this would have required years of coding experience and a team of developers. Today, with AI-assisted development tools, a high school student can build a production-quality system in weeks.

This isn't just about stock analysis. It's about a future where anyone with an idea can build software, without needing to learn programming languages first.

The barrier to creating technology is disappearing. What will you build?