8.1 KiB
❓ Frequently Asked Questions
The questions everyone asks (so you don't have to)
General
Q: Is this free?
A: The software is free and open source. You pay for:
- LLM API usage (~$0.10-0.20 per analysis with Claude/GPT-4)
- Free options exist (Google Gemini, Alpaca paper trading)
Q: Is this actually AI-powered or just buzzwords?
A: Actually AI-powered. Multiple LLM agents (using Claude/GPT-4) debate and analyze stocks. It's like having a team of analysts arguing about your trades.
Q: Will this make me rich?
A: No. This is a tool, not a crystal ball. Use it to inform decisions, not make them for you.
Q: Can I use this for real trading?
A: Yes, but start with paper trading! The Alpaca integration supports both.
Setup
Q: Which LLM provider should I use?
A:
- Claude (Anthropic): Best reasoning, great for complex analysis
- GPT-4 (OpenAI): Faster, well-tested, slightly cheaper
- Gemini (Google): Free tier available, good for experimentation
Q: Do I need to know Python?
A: Not for basic use! The web interface is point-and-click. Python knowledge helps for customization.
Q: Docker or local install?
A: Docker is easier (one command). Local install gives you more control.
Q: What's in the .env file?
A: Your API credentials. These are secrets - never commit to git. Use .env.example as a template and fill in your actual keys.
Features
Q: What's multi-agent analysis?
A: Instead of one AI opinion, you get multiple specialized agents:
- Market Analyst (trends, technicals)
- Fundamentals Expert (financials, ratios)
- News Analyst (sentiment, events)
- Trader (synthesizes everything into a decision)
They literally debate the trade before giving you a signal.
Q: How accurate are the predictions?
A: We don't make predictions - we provide analysis. Accuracy depends on market conditions, which LLM you use, and what data is available. Backtest your strategies first!
Q: Can I customize the analysis?
A: Yes! Edit the agent prompts, add new analysts, change the debate process. It's all Python code.
Q: What stocks can I analyze?
A: Any US stock. Just provide the ticker symbol (NVDA, AAPL, TSLA, etc.). International stocks coming soon!
Paper Trading
Q: What is paper trading?
A: Simulated trading with fake money but REAL market prices. Practice without risk.
Q: Does Alpaca paper trading cost money?
A: No! Completely free. You get $100,000 virtual dollars to play with.
Q: Can I test my strategy without paper trading?
A: Yes - use the backtesting framework. Simulate months of trading in seconds.
Q: How do I switch from paper to live trading?
A: Set ALPACA_PAPER_TRADING=false in your .env file. But seriously - practice more first!
Technical
Q: What's the difference between the brokers?
A:
- Alpaca: Free paper trading, easy API, US stocks only
- Interactive Brokers (coming soon): Professional platform, global markets
Q: How do I add a new LLM provider?
A: Check tradingagents/llm_factory.py - add your provider following the existing pattern. PRs welcome!
Q: Can I run this on a server?
A: Yes! Docker makes it easy. Check DOCKER.md for deployment guides.
Q: How much does it cost to run?
A: Mostly LLM API costs. One analysis:
- Claude: ~$0.15
- GPT-4: ~$0.10
- Gemini: Free (with limits)
Running 24/7 with frequent analyses: Budget $50-200/month.
Q: Does this support real-time data?
A: Currently batch processing. Real-time streaming is on the roadmap!
Q: Can I integrate with other brokers?
A: Currently Alpaca and Interactive Brokers. Want to add another? Submit a PR or open an issue!
Troubleshooting
Q: "API quota exceeded" - what do I do?
A: You hit your LLM provider's limit. Wait for reset or upgrade your plan.
Q: Analysis takes forever
A: Normal! Deep analysis with multiple agents takes 60-90 seconds. Grab coffee. It's worth the wait.
Q: My trades aren't executing
A: Check:
- Market is open (9:30 AM - 4 PM ET, Mon-Fri)
- Broker is connected (
connectcommand) - You have buying power
- Ticker symbol is valid
Q: Docker container keeps restarting
A: Check logs: docker-compose logs. Usually a missing .env or invalid API key.
Q: "Connection refused" on localhost:8000
A: Port 8000 is already in use. Try:
lsof -i :8000 # Find what's using it
docker-compose down && docker-compose up # Restart containers
Q: I see "ModuleNotFoundError"
A: Dependencies missing. Run:
pip install -r requirements.txt
Q: Web UI is slow or freezing
A: Likely waiting for AI analysis. Check browser console for errors. Restart if needed: docker-compose restart
Safety & Security
Q: Is my API key safe?
A: Yes - stored in .env which is gitignored. Never committed to repos. Good practice: rotate keys periodically.
Q: Can someone hack my trading account?
A: Use paper trading first! For live trading, use Alpaca's security features (2FA, IP whitelist).
Q: What data do you collect?
A: We don't collect anything. All analysis happens locally or via your API keys. Read our privacy policy for details.
Q: Is the code audited?
A: It's open source - you can audit it yourself! We encourage security reviews. Found a vulnerability? Report it responsibly.
Contributing
Q: Can I contribute?
A: Please do! We need:
- New broker integrations
- Better UI/UX
- Strategy templates
- Documentation improvements
Q: I found a bug - where do I report it?
A: GitHub Issues: https://github.com/TauricResearch/TradingAgents/issues
Q: Can I fork this for my own use?
A: Absolutely! It's open source. Just follow the license terms.
Q: How do I run tests?
A: Check the contributing guide. Generally:
pytest tests/
Advanced
Q: Can I backtest strategies?
A: Yes! Check examples/backtest_example.py for details.
Q: How do I add custom indicators?
A: Add them to tradingagents/indicators/ and reference in your agents.
Q: Can I trade crypto?
A: Not yet. Stocks only for now. Crypto support is on the roadmap.
Q: Mobile app?
A: On the roadmap! Web app works great on mobile for now.
Q: Can I use this in production?
A: It's production-ready for personal use. For commercial use, consult your legal team.
Q: How do I scale this?
A: Docker deployment handles most scaling. For enterprise needs, check DOCKER.md.
Mistakes & Learning
Q: I made a bad trade with paper money - does it matter?
A: Nope! That's the whole point of paper trading. Make mistakes, learn, improve. Zero consequences.
Q: The AI recommended something stupid - should I blame it?
A: Nah. AI is a tool, not infallible. It's trained on data with limitations. Always do your own research.
Q: Can I see what the AI is thinking?
A: Yes! The analysis output shows each agent's reasoning. You're not flying blind.
Q: How do I get better at this?
A:
- Start with paper trading
- Analyze real trades with the AI
- Compare AI analysis to your own
- Backtest strategies
- Read the code and understand the logic
- Iterate and improve
Performance & Optimization
Q: How fast is the analysis?
A: Typically 60-90 seconds for multi-agent analysis. Depends on LLM provider and market data availability.
Q: Can I speed it up?
A: Yes:
- Use GPT-4 (faster than Claude for some queries)
- Reduce the number of agents
- Cache historical data
- Use paper trading vs live (no latency)
Q: Does it work offline?
A: No - requires API access to LLMs and market data. But you could cache results for offline review.
Getting Help
Didn't find your answer?
- Check the docs: FEATURES.md, DOCKER.md
- Ask on GitHub Discussions
- Read the code (it's well-commented!)
- Check the examples in
examples/
Still stuck?
- Open a GitHub issue with:
- What you tried
- Error message (if any)
- Your setup (Docker/local, Python version, OS)
- Relevant logs
We're here to help! 🤝
Last Updated: November 2025 Have a question not listed? Open an issue and we'll add it here!