Automated trading on prediction markets using mathematical strategies, machine learning, and cross-platform arbitrage.
Prediction markets like Kalshi and Polymarket are full of pricing inefficiencies — emotional premiums, mispriced spreads, and arbitrage windows where YES + NO contracts cost less than $1.00. Capturing these opportunities manually is impossible at speed and scale.
A multi-bot system with different strategies: a Smart Bettor that uses ML scoring to find high-confidence directional bets, a market maker for two-sided spread capture, and a cross-platform arbitrage engine that detects price discrepancies between Kalshi and Polymarket. The ML model (LightGBM) was trained on 744K+ historical markets and retrains nightly.
Market making at small scale isn't profitable — the spread capture doesn't cover losses. The pivot to directional ML-scored bets was critical. I also learned the importance of observation mode: running the bot in "watch only" mode first, logging what it would have traded, then analyzing those decisions before enabling real execution. The nightly retrain pipeline with automatic validation-before-deploy prevents model regression.