Prediction Market Model
Probability-Driven Event Contract Analysis
An in-development modeling system for comparing estimated event probabilities against prediction-market prices, identifying meaningful pricing gaps, and evaluating potential trades within defined risk limits.
Overview
The system is designed to collect prediction-market contract data, translate market prices into implied probabilities, and compare those values against independent model estimates. The goal is to surface contracts where the estimated probability and market consensus meaningfully diverge.
Technologies
Python Pandas NumPy scikit-learn Kalshi market data REST APIs Feature engineering Probability calibration Backtesting Structured trade logging
Development Status
The project is currently focused on data collection, contract normalization, feature validation, and establishing a repeatable evaluation framework before any performance claims are published.
Design ProcessBuild Sequence
Define the Market Scope
Establish the contract categories, time horizons, liquidity requirements, and risk constraints the model will support.
Build the Data Pipeline
Collect contract prices, order-book information, volume, expiration dates, and resolved outcomes in a consistent format.
Engineer Predictive Features
Transform market behavior and event context into measurable signals while preventing future information from leaking into training data.
Train and Calibrate
Train probability models, compare candidate approaches, and calibrate outputs so predicted confidence aligns with observed outcomes.
Backtest and Validate
Evaluate the system with time-aware testing, simulated execution costs, position limits, and contract-level performance review.
Evaluation FrameworkIn Development
Probability Calibration
BrierTrack probability accuracy and calibration rather than relying only on correct directional calls.
Market Edge
ΔPMeasure the gap between model probability and market-implied probability after applying confidence thresholds.
Backtesting
Walk-ForwardPreserve chronological order and evaluate on unseen contracts to reduce optimistic historical results.
Risk Controls
LimitsModel transaction costs, cap contract exposure, and log every simulated recommendation for review.
Project Results Results // Ongoing
Win / Loss Ratio
Total Profit
$0 Net ProfitModel Confidence