Prediction Market Automation: Kalshi, Polymarket & Beyond

Build systems that find and exploit pricing edges across Kalshi, Polymarket, Fanatics, and every other prediction market platform — then execute trades automatically while you sleep.

This isn't a guide to picking winners. Markets already price in who's likely to win. This framework teaches you to build systems that find spots where your model's probability estimate diverges meaningfully from the market price — and automate the execution so you're placing bets daily without manual work.

Written by engineers who built and ran these systems live: crypto lag arbitrage bots reacting in milliseconds, NCAAB win probability models running daily during March Madness, political market scrapers aggregating polling data, and automated execution pipelines placing orders with no human in the loop.

What's covered:

Platform Architecture — API integration for Kalshi (RSA-PSS auth, fee structure, market data), Polymarket (wallet signing, USDC settlement, gas management), and a universal client pattern that works across any platform. Cross-platform position management and smart order routing included.

Strategy Taxonomy — From millisecond lag arbitrage (crypto price feed → prediction market gap detection) to multi-week fundamental analysis. Covers market making, information edge trading, sentiment analysis, and long-horizon political/economic forecasting. Each strategy type gets its own architecture, data sources, and model approach.

Data Pipeline Engineering — Real-time WebSocket feeds for crypto, polling aggregation for political markets, advanced sports analytics (Barttorvik, Football Reference), weather model ensembles (GFS, ECMWF). Cache-first architecture so you're not burning API credits unnecessarily. Event detection system that scans all sources and surfaces actionable opportunities.

ML Model Development — Model type selection by strategy (gradient boosting for sports/tabular, ARIMA for sequential, deep learning for long-horizon). Walk-forward validation that respects temporal ordering. Feature engineering patterns across every market type: adjusted efficiency metrics for sports, weighted polling for politics, on-chain analysis + sentiment for crypto, meteorological ensemble features for weather.

Risk Management & Sizing — Fee-adjusted Kelly Criterion that accounts for Kalshi's 7% taker fee and $0.02/contract cap. Daily exposure caps, correlation analysis across positions, dynamic sizing based on market conditions and time to expiry. Stop-loss and trailing stop logic. Platform-specific risk considerations (smart contract risk on Polymarket, settlement timing on Kalshi).

Automated Execution — Universal order management system with full retry logic and circuit breakers. TWAP execution for large orders. Iceberg orders to hide position size. WebSocket-based submission for low-latency strategies. Smart order routing across platforms with slippage controls.

OpenClaw Integration — Cron-based daily trading cycles, real-time Discord alerts for high-confidence opportunities, sub-agent spawning for compute-intensive analysis, and a complete production bot scaffold you can deploy immediately.

Backtesting & Validation — Platform-specific fee and liquidity modeling in backtests (Kalshi fee tiers, Polymarket gas costs). Walk-forward validation. Model calibration analysis, Brier scores, log loss, AUC-ROC. Decision framework for "is my edge real or am I fooling myself?"

Production Patterns — Deployment checklist, health monitoring, circuit breakers, emergency stops, compliance audit trails. Multi-model ensembles with dynamic softmax weighting. Adaptive retraining triggered by performance degradation or concept drift. Cross-platform portfolio optimization with correlation-aware position sizing.

The honest truth about variance: Even a well-calibrated model with a real edge will have losing days, losing weeks. That's not failure — that's how probability works. The framework includes the risk management infrastructure to survive variance and the validation methodology to distinguish "bad luck this week" from "my edge evaporated." Trust your numbers if they're validated. Volume is how edges compound.

All code patterns are Python, production-ready, and built on battle-tested approaches from systems that ran live.