A multi-agent LLM framework extended with a custom analyst that simulates how 8 legendary investors debate a stock — then synthesises their conflicting views into a single recommendation.
The original framework runs standard technical + fundamental analysis. This fork adds 8 legendary investor personas — each with a radically different framework for evaluating a stock.
Buffett's moat thinking directly challenges Musk's first-principles skepticism. Jim Simons ignores both and looks at pure patterns. That tension surfaces blind spots a single analyst misses.
Because the personas disagree, the final output includes price targets, re-evaluation triggers, and position sizing guidance — not just a binary call.
Jensen Huang's lens is particularly sharp on AI infrastructure stocks. Simons' quant frame works for technical divergences. The right persona is strong where others are weak.
RSI, MACD, Bollinger bands, price action
Headlines, macro, insider transactions
Balance sheet, cash flow, P/E, margins
8 investor personas, debate each other
Durable moat, 20-year hold test
Day 1 vs Day 2, compounding
First principles, physics of biz
Margins, supply chain, services
AI integration, platform moat
Market size, rake, competition
Real AI vs hype, compute demand
Pure quant — patterns only
Builds the upside case, challenges bear
Identifies risks, challenges bull thesis
Synthesises all analyst and researcher reports into an initial investment proposal with position sizing guidance
Argues for higher risk tolerance
Balances risk and return
Capital preservation focus
git clone https://github.com/sanaships/TradingAgents cd TradingAgents
python3.11 -m venv venv source venv/bin/activate pip install -r requirements.txt
cp .env.example .env # Add ANTHROPIC_API_KEY to .env # Get one free at console.anthropic.com
python3 run.py
Edit run.py to change the ticker or model.