backtesting

Name: backtesting
Availability: InStock
Author: sports-data-hq

Simulate historical betting performance with walk-forward tests and report ROI and drawdown.

1|Updated May 5, 2026

Search Tags:#calibration #betting #backtesting #roi #walk-forward #bankroll #sportsbook

Ready to run

npx skills add https://github.com/sports-data-hq/hockey-skills --skill backtesting-sports-data-hq

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: backtesting
Source: https://github.com/sports-data-hq/hockey-skills/tree/main/skills/backtesting
Command: npx skills add https://github.com/sports-data-hq/hockey-skills --skill backtesting-sports-data-hq

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill determines whether a betting strategy has a real edge by simulating historical wagers in time order, so you can distinguish genuine profitability from in-sample overfitting.

Core Features & Use Cases

Walk-Forward Simulation: Tests strategies only on unseen historical periods and rejects in-sample validation.
Performance Auditing: Measures ROI, win rate, drawdown, Sharpe ratio, calibration drift, and edge compression over time.
Bookmaker Comparison: Separates results by sportsbook to reveal whether the edge exists at sharp books or only at softer markets.
Use Case: A bettor can backtest a hockey model across a prior season, compare flat and Kelly staking, and decide whether the strategy is ready for live deployment.

Quick Start

Use the backtesting skill to evaluate my historical betting strategy with a walk-forward test and summarize ROI, drawdown, calibration, and confidence intervals.

Frequently Asked Questions about backtesting

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I backtest a sports betting strategy to verify if my edge is real?▼

Walk-forward backtesting tests your wagering models only on unseen historical periods while rejecting in-sample validation. This leakage-free time-ordered evaluation reveals if your betting edge is genuine or a result of overfitting.

What metrics are used to audit historical betting performance during a simulation?▼

Historical betting performance auditing measures ROI, win rate, drawdown, Sharpe ratio, and calibration drift. It also tracks edge compression over time to provide a comprehensive view of your strategy's viability.

Can I compare my betting model results across different sportsbooks?▼

Yes, you can separate backtest results by sportsbook to reveal whether your edge exists at sharp books or only at softer markets. This bookmaker comparison helps validate the reliability of your betting model.

How do I evaluate flat staking versus Kelly staking in a wagering backtest?▼

You can simulate historical betting performance using both flat and Kelly staking rules to compare their risk-adjusted returns. Applying bankroll rules during the walk-forward test shows which staking method optimizes your ROI.

What historical data do I need to run a walk-forward betting simulation?▼

Running a walk-forward simulation requires historical game results, odds, bankroll rules, and bootstrap confidence intervals. This data enables leakage-free time-ordered evaluation to measure calibration drift and drawdown.

Why does my sports betting model show high ROI in backtesting but fail in live deployment?▼

High ROI in backtesting often indicates in-sample overfitting rather than a real edge. Using walk-forward tests on unseen historical periods and measuring calibration drift helps prevent deploying overfit wagering models.