I Built a Prediction Market Bot That Trades Itself —

I built an autonomous prediction market trading bot that continuously monitors Polymarket, identifies pricing inefficiencies, and executes trades without human intervention.

Architecture

The bot runs on a VPS with a persistent Python process managed by systemd. It uses the Polymarket API for market data and order execution, with Redis for state management and SQLite for trade history.

# systemd service
[Unit]
Description=PolyX Trading Bot
After=network.target

[Service]
Type=simple
User=ubuntu
WorkingDirectory=~/polyx-bot
ExecStart=~/polyx-bot/venv/bin/python bot.py
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

Strategy

The bot uses a mean-reversion approach on binary markets with high liquidity:

• Monitor price deviations from historical baselines
• Check for stale prices on low-volume markets
• Calculate position sizing based on Kelly criterion
• Execute limit orders with tight spreads

Results

Over 30 days of live trading, the bot achieved a 4.2% ROI with 187 trades executed. Drawdown was kept under 2% through strict position limits. The biggest lesson: latency matters less than edge detection in prediction markets.

Key Takeaways

• Start with paper trading or very small amounts
• Prediction markets are less efficient than commonly believed
• Infrastructure reliability is as important as strategy
• Regulatory uncertainty remains the biggest risk