AI Skill: Walk-Forward Backtesting for Betting Strategies

AI Skill: Walk-Forward Backtesting for Betting Strategies

The Walk-Forward Backtesting skill provides a method to evaluate the historical performance of sports betting models. It simulates the exact sequence of events, using historical odds and bet sizing in accordance with the Kelly criterion to assess the validity of a model's predictive edge. This skill refrains from in-sample testing, preferring a rigorous walk-forward approach to ensure results are not artificially inflated.

What this skill does

• Conducts walk-forward historical backtesting on user-specified betting strategies.
• Evaluates return on investment (ROI) over a defined season to verify model profitability.
• Calculates performance metrics such as win rate over time and drawdown analysis.
• Uses historical odds data for precision in simulation and outcome measurement.
• Provides bootstrap confidence intervals to assess statistical reliability.
• Identifies edge compression, helping distinguish genuine predictive power from random patterns.

Who it is for

• Developers and AI practitioners using coding agents such as Claude Code, Cursor, and Codex.
• Data analysts in sports betting sectors looking to validate predictive models historically.
• Statisticians interested in quantitative evaluation of betting strategies.

Use cases

• Evaluating a betting model's historical profitability before live deployment.
• Determining the consistency of ROI metrics across different seasons or sports.
• Applying robust statistical methods to quantify the strength of a betting strategy's edge.

Technical details

• Integrates with PuckAPI (`puckapi-tool`) for acquiring historical odds and game results.
• Credits Usage: Retrieves historical odds using `get_odds` (10 credits per game) and game results with `get_games` (5 credits per query).
• Sample test over a limited timeframe is recommended before committing to a full season backtest to manage credit costs effectively.
• Supports user-provided historical data, circumventing the need for credit expenditure.

Source & Licence

This package is built on open-source work published by PuckAPI (PuckAPI/claude-sports-analytics) and distributed under MIT. The original licence text and copyright notice are included in your download.

Personal and commercial use, modification and redistribution are permitted, provided the original copyright and licence notice are retained.

Your purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.

Delivery & Support

• Delivery: instant — a secure download link is emailed to you as soon as payment is confirmed.
• Format: ZIP archive containing the skill files, documentation and the original licence.
• Support: support@mcpcart.com — we aim to reply within 2 business days.
• Updates: updates are included only where stated on this page.

Refunds

This is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.

Claude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.