Prediction Market Trading

Prediction market trading applies trading strategies, arbitrage detection, market making, and risk management techniques specifically to binary and categorical outcome markets. Unlike traditional financial markets, prediction markets have bounded payoffs (typically $0 or $1), guaranteed resolution dates, and outcomes tied to real-world events. These structural differences create unique opportunities for systematic trading, AI agent deployment (like the Olas/Polystrat approach), and portfolio construction across binary outcomes.

## Key Points

1. Base rates for similar events
2. Current evidence and trends
3. Key factors that could change the outcome
4. Potential biases in the current market price
1. Expected value in prediction markets is simply your_probability minus market_price; trade only when this edge exceeds your transaction costs and minimum threshold
2. Kelly Criterion gives optimal sizing but is very aggressive; use half-Kelly or quarter-Kelly in practice to survive variance
3. Cross-platform arbitrage requires prices to sum to less than 1 after fees; these opportunities are rare but risk-free when found
4. Market making in prediction markets earns the spread while managing inventory; skew quotes to flatten position when inventory grows
5. AI trading agents combine LLM research with systematic position sizing; the Olas/Polystrat approach automates the full pipeline from research to execution
6. Prediction market portfolios need Monte Carlo simulation because all positions are binary (no partial outcomes)
7. Correlation management is critical: political markets are often highly correlated, and a single wrong assessment can wipe out a concentrated portfolio
8. Pre-trade risk checks (position limits, drawdown limits, concentration limits, minimum edge) prevent the most common causes of ruin