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quant-backtest

Backtests trading strategies on historical market data with Python.

1|2|Updated Mar 21, 2026
Search Tags:#python#optimization#strategy#trading#backtest#quantitative
One-click install
npx skills add https://github.com/bluegitter/QClaw --skill quant-backtest-bluegitter
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-backtest
Source: https://github.com/bluegitter/QClaw/tree/main/resources/openclaw/config/skills/quant-backtest
Command: npx skills add https://github.com/bluegitter/QClaw --skill quant-backtest-bluegitter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Quantitative traders often need to evaluate how a trading strategy would have performed on historical market data, but setting up backtesting environments, handling data, and analyzing results can be time‑consuming and error‑prone.

Core Features & Use Cases

  • Backtesting Engine: Supports common strategies such as moving‑average crossover, MACD, RSI, etc.
  • Performance Analysis: Generates metrics like annual return, max drawdown, Sharpe ratio, and win rate.
  • Parameter Optimization: Runs grid searches to find the best parameter combinations.
  • Report Generation: Produces markdown‑formatted reports with visual summaries.

Example use case: a quant analyst wants to compare two MA crossover configurations on the CSI 300 index over the last three years to decide which parameters to deploy in live trading.

Quick Start

Ask the quant-backtest skill to run a backtest on the MA crossover strategy for the CSI 300 index over the past three years.

Frequently Asked Questions about quant-backtest

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

FAQPage Schema
How do I backtest a moving average crossover strategy on the CSI 300 index?▼

You can backtest trading strategies like moving average crossover on historical CSI 300 data to calculate metrics such as annual return, max drawdown, and Sharpe ratio, producing a markdown report with visual summaries.

Can I run parameter optimization for RSI or MACD trading strategies?▼

Yes, you can run parameter optimization for RSI or MACD strategies by executing grid searches across historical datasets to identify the best parameter combinations for maximizing performance metrics before live deployment.

Do I need Python with pandas and backtrader to run quantitative backtests?▼

Yes, you need Python with pandas, numpy, backtrader, and matplotlib installed to run quantitative backtests, handle historical market data, execute simulations, and generate visual performance summaries.

What metrics are included in a backtest performance analysis report?▼

A backtest performance analysis report includes metrics like annual return, max drawdown, Sharpe ratio, and win rate, formatted as a markdown document with visual summaries to help evaluate quantitative strategy effectiveness.

What is the best way to compare different parameter configurations for a trading strategy?▼

The best way to compare parameter configurations is running grid search optimization on historical market data, which evaluates combinations to find the best setup and generates markdown reports with metrics like Sharpe ratio and max drawdown.

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