Building a prediction market copy trading strategy, $200 → $52 lessons learned

I developed a bot that tracks whale trades on Polymarket. Paper Trading simulated over 5000 trades, with a 58% win rate and a profit of +$770. Confidently, I put in real money of $200.

Result: 25 real trades, with a 28% win rate, remaining $52.

Pitfalls encountered:

1. Phantom Decisions - The code recorded "I want to buy," but API errors prevented the trade from executing. The database stored a bunch of decision records, but the account balance remained unchanged. Self-deceptive data = false confidence.

2. Repeated Orders - Whales bought the same market 5 times in a row, and the bot followed with 5 purchases. Single market exposure reached 40%, a single loss would cause a blow-up.

3. Position Limits Are Virtually Useless - Configured with a 5% cap, but each order actually took up 40%. Risk control code was bypassed but went unnoticed (because it wasn’t tested).

4. The Illusion of Paper Trading - Simulated without slippage or latency; signal price = execution price. In real environments, it takes seconds for signals to execute, and prices have already moved.

5. Whale Traders in Sports Markets Are Gamblers - NHL trades lost $80 per trade, NBA trades all lost in 5 attempts. Not all whales are smart money; sports betting whales might just be wealthy gamblers.

Lessons to learn: average entry price controlled within 0.5-0.6 range (not chasing high odds), 5000 trades to diversify risk. But this edge is too thin; friction costs in real environments eat up all profits.

Lesson: simulated profits ≠ real profits. $80 tuition fees.