backtest-runner

Name: backtest-runner
Availability: InStock
Author: moyuchi

Run backtests on specified stocks and time periods to output returns, drawdown, and Sharpe ratio.

Updated Apr 13, 2026

Search Tags:#algorithm #strategy #finance #trading #backtest #quantitative

Ready to run

npx skills add https://github.com/Moyuchiiii/claude-investment-sim --skill backtest-runner-moyuchiiii

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: backtest-runner
Source: https://github.com/Moyuchiiii/claude-investment-sim/tree/main/.claude/skills/backtest-runner
Command: npx skills add https://github.com/Moyuchiiii/claude-investment-sim --skill backtest-runner-moyuchiiii

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This tool runs backtests to validate trading strategies on specified stocks and time periods, turning hypothetical ideas into data-backed insights.

Core Features & Use Cases

Run backtests for chosen tickers over a defined period to measure performance and risk.
Output metrics like returns, win rate, Sharpe ratio, and maximum drawdown to inform strategy tuning.
Use Case: A quant analyst wants to test a moving average crossover across a year of data to decide whether to deploy the strategy live.

Quick Start

Ask Claude to run a backtest for a chosen ticker and period using the backtest runner.

Frequently Asked Questions about backtest-runner

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

FAQPage Schema

How do I run a backtest to validate my trading strategy on historical stock data?▼

Yes, you can run backtests across multiple tickers simultaneously to evaluate strategy performance. This allows you to measure risk and returns across different stocks and date ranges, enabling parameter sweeps for broader market analysis.

What financial metrics do I get from backtesting a quantitative strategy?▼

Backtesting a quantitative strategy returns key performance metrics including total returns, win rate, maximum drawdown, and Sharpe ratio. These data points help you tune algorithm parameters and decide whether to deploy the trading strategy live.

Can I run parameter sweeps to optimize a moving average crossover strategy?▼

You can execute parameter sweeps to optimize trading strategies like a moving average crossover. By testing different parameters across specified time periods, you receive comparative metrics that inform which configuration performs best before live deployment.

What do I need to provide to test an algorithmic trading strategy over a defined period?▼

You need to provide the target stock ticker and the defined historical time period to test an algorithmic trading strategy. The backtest workflow uses these inputs to process the data and output metrics like returns and drawdown.