Random Forest Calibration Model with Systematic Backtesting Pipeline

How It Works This is a full machine learning pipeline for systematic Polymarket trading. A Random Forest model is trained on features derived from market data (likely price history, volume, time-to-resolution, category tags). The raw classifier outputs are then calibrated — a critical step that adjusts the model's confidence scores to match empirical resolution frequencies, ensuring that events the model assigns 70% probability actually resolve YES ~70% of the time historically.

Entry Conditions A trade is entered when the calibrated model probability diverges from the current market price by more than a minimum edge threshold. The direction (YES or NO) is determined by whether the model's probability is above or below the market price. The backtesting module is used to validate that the edge is real and not an artifact of overfitting before deploying the model in paper trading.

Exit Conditions Not explicitly documented, but systematic pipelines of this type typically hold to resolution unless the model re-scores the market and the edge disappears or reverses. The backtesting framework would inform optimal hold duration.

Verified Results & Implementation Notes `BallesJr/polymarket-edge-model` (774KB) is the same developer as the weather strategy, suggesting a sophisticated, multi-strategy practitioner. The combination of Random Forest + calibration + backtesting + automated execution is a complete, academically sound pipeline. Calibration is often overlooked in amateur ML trading systems, and its explicit inclusion is a strong quality signal. Paper trading mode indicates the strategy is in validation.

Risks Random Forest models are susceptible to concept drift — the features that predicted outcomes in historical data may not remain predictive as market participant sophistication increases. Feature engineering quality is the key determinant of edge. Overfitting risk is real even with out-of-sample testing if the validation window is short. The model requires continuous retraining as new resolution data accumulates.