AI Trading on a Budget: $0 Setup Guide

You don't need thousands of dollars to start AI trading. Here's how I built a working system for $0 upfront.

The Free Stack

Data ($0)

• Yahoo Finance (yfinance) — Historical stock data
• CoinGecko API — Crypto prices and market data
• Alpha Vantage — 25 API calls/day (enough for EOD strategies)

AI/LLM ($0)

• ChatGPT Free Tier — GPT-3.5 for signal generation
• Claude (free tier) — Alternative to ChatGPT
• Local LLMs — Run Llama 3 on your laptop (if you have 16GB RAM)

Backtesting ($0)

• Backtrader (Python) — Open-source backtesting framework
• VectorBT — Vectorized backtesting (fast!)
• QuantConnect — Cloud-based (free tier, limited compute)

Brokerage ($0 commissions)

• Alpaca — Paper trading account (no real money risk)
• Robinhood — $0 commissions for live trading
• TD Ameritrade — Free API access + paper trading

Hosting ($0)

• PythonAnywhere — Free Python hosting
• Replit — Free cloud dev environment
• GitHub Actions — Free cron jobs for scheduled trading

My $0 Strategy

I run a simple GPT-3.5 swing trading bot:

1. Data collection: yfinance pulls daily closes for 20 stocks
2. Signal generation: ChatGPT analyzes price patterns and suggests trades
3. Backtesting: Backtrader validates signals on historical data
4. Paper trading: Alpaca executes trades with fake money

Result: Learning without losing real capital.

When to Upgrade (Paid Tier)

I upgraded to paid tools after 3 months of paper trading:

• Polygon.io ($29/mo) — Real-time data
• ChatGPT Plus ($20/mo) — GPT-4 for better signal quality
• AWS Lambda ($5/mo) — Reliable hosting

Total: $54/month. Still cheaper than a gym membership.

Key Takeaway

Start free. Prove it works. Then pay for speed/scale.

You don't need a hedge fund budget to learn AI trading. The tools are free—the bottleneck is your willingness to learn and iterate.

Questions? Drop them in the comments!