GRQ (Get Rich Quick) - AI Market Opportunities Analyzer

Disclaimer: This is a vibe coding experiment built for fun and learning. It is not financial advice. Do not make investment decisions based on this tool. Always do your own research.

---

GRQ is an AI-powered market analysis tool that aggregates financial news and market data, then uses Claude to identify trading opportunities across stocks, ETFs, crypto, and commodities.

What It Does

1. Collects Data - Pulls news from RSS feeds (Yahoo Finance, Reuters, MarketWatch, CNBC) and fetches market data (prices, technicals, fundamentals, crypto)

2. Applies Market Knowledge - Uses a built-in knowledge base of macroeconomic correlations, supply chain dependencies, and geopolitical impacts to identify non-obvious connections

3. Generates Signals - Produces actionable trading recommendations in four categories:

   • Short-term (days to 2 weeks) - Quick trades with specific price targets
   • Mid-term (2 weeks to 3 months) - Swing trades with catalyst triggers
   • Long-term (3+ months) - Strategic positions with time horizons
   • Sell/Avoid - Exit signals with risk levels

Requirements

• Docker and Docker Compose
• Anthropic API key (required)
• Optional API keys for enhanced data:
  • Finnhub (stock prices)
  • Alpha Vantage (technical indicators)
  • NewsData.io (additional news)
  • Financial Modeling Prep (fundamentals)
  • Commodities API (commodity prices)

Setup

1. Copy the example environment file:

   cp .env.example .env
   

2. Edit .env and add your API keys:

   ANTHROPIC_API_KEY=your-key-here
   FINNHUB_API_KEY=your-key-here
   # ... other optional keys
   

Running

Build and run with Docker Compose:

docker-compose up --build

The analysis will print to stdout when complete.

Project Structure

grq/
├── main.py                 # Entry point
├── src/
│   ├── agents/
│   │   ├── analyzer.py     # Analysis agent (generates signals)
│   │   └── data_collector.py # Data collection agent
│   ├── knowledge/
│   │   ├── __init__.py     # Knowledge base loader
│   │   └── market_correlations.yaml # Market knowledge base
│   ├── tools/
│   │   ├── news_tools.py   # RSS and news API tools
│   │   ├── market_tools.py # Stock, crypto, commodity tools
│   │   ├── tool_definitions.py
│   │   └── tool_executor.py
│   ├── utils/
│   │   ├── api_client.py   # HTTP client with retries
│   │   └── rss_parser.py   # RSS feed parser
│   └── config.py           # Configuration loader
├── Dockerfile
├── docker-compose.yml
└── requirements.txt

Knowledge Base

The market knowledge base (src/knowledge/market_correlations.yaml) contains:

• Sector ETF mappings (all 11 SPDR sectors)
• Interest rate correlations (Fed policy impacts)
• Geopolitical event impacts (Taiwan, Middle East, Russia/Ukraine, China trade)
• Commodity supply chains (oil, lithium, rare earths, semiconductors)
• Country production data (who produces what)
• Correlation rules (if X happens, Y tickers are affected)

This enables the analyzer to "read between the lines" - for example, understanding that Taiwan tensions affect semiconductor stocks even if specific tickers aren't mentioned in the news.

Output

The tool outputs four markdown tables:

1. Short-Term Wins - Asset, price, catalyst, target price, reasoning
2. Mid-Term Plays - Asset, price, thesis, hold-until trigger, reasoning
3. Long-Term Positions - Asset, price, theme, time horizon, reasoning
4. Sell/Avoid - Asset, price, risk level, why sell, what's wrong

Notes

• The tool runs once and exits (not a continuous service)
• Analysis quality depends on available API keys
• The knowledge base can be extended by editing the YAML file
• All data collection and analysis is done via Claude API calls