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crypto-backtesting

Simulate trading strategies on historical OHLCV data with Python and pandas.

4|2|Updated Nov 13, 2025
Search Tags:#pandas#backtesting#trading#risk-metrics#walk-forward#strategy-validation
One-click install
npx skills add https://github.com/khanh-vu/claude-force --skill crypto-backtesting
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: crypto-backtesting
Source: https://github.com/khanh-vu/claude-force/tree/main/.claude/skills/crypto-backtesting
Command: npx skills add https://github.com/khanh-vu/claude-force --skill crypto-backtesting

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables quantitative teams and solo traders to rigorously test trading ideas by simulating real-world execution on historical data, reducing guesswork and providing reproducible results.

Core Features & Use Cases

  • Backtesting Engine: Evaluate strategies against OHLCV data with realistic entry/exit execution assumptions.
  • Walk-Forward Analysis: Validate performance by rotating training and testing windows to detect overfitting.
  • Risk & Metrics: Compute metrics such as total return, Sharpe ratio, maximum drawdown, win rate, and number of trades.
  • Use Case: A trader wants to compare two momentum strategies across multiple assets and timeframes to select the most robust approach.

Quick Start

Prepare an OHLCV dataset, implement a strategy object with a generate_signal method, instantiate a Backtester with an initial capital, and call run() to obtain performance metrics.

Frequently Asked Questions about crypto-backtesting

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

FAQPage Schema
How do I backtest trading strategies with pandas using historical data?▼

To backtest trading strategies with pandas, you need an OHLCV dataset and a strategy object with a generate_signal method. The Skill simulates trades on historical data with realistic execution to produce deterministic performance results.

How does walk-forward analysis validate trading strategy performance?▼

Walk-forward analysis validates trading strategy performance by rotating training and testing windows on historical data. This mechanism detects overfitting by evaluating how a strategy generalizes across different time periods.

What risk metrics can I compute when backtesting trading strategies?▼

When backtesting trading strategies, you can compute risk metrics including total return, Sharpe ratio, maximum drawdown, win rate, and number of trades. These metrics help assess strategy robustness and facilitate performance comparisons across assets.

Can I use Python to compare multiple trading strategies across different timeframes?▼

Yes, you can use Python with pandas to compare multiple trading strategies across different timeframes. The Skill evaluates strategies against OHLCV data to help select the most robust approach for momentum or other trading methods.

Do I need OHLCV data to run a backtesting simulation in Python?▼

Yes, you need an OHLCV data source to run a backtesting simulation in Python. The Skill relies on this historical price data to simulate realistic entry and exit execution for quantitative testing.

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