skills.rest
dennisccy
dennisccyCOMMUNITY
Explore all skills by this author →
GitHub

backtesting-trading-strategies

Backtest crypto trading strategies against historical data with Python.

Updated Aug 27, 2026
Search Tags:#python#backtesting#data-fetching#trading-strategies#equity-curve#grid-search#financial-metrics
One-click install
npx skills add https://github.com/dennisccy/finovae_strategy_platform --skill backtesting-trading-strategies-dennisccy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: backtesting-trading-strategies
Source: https://github.com/dennisccy/finovae_strategy_platform/tree/main/.claude/skills/backtesting-trading-strategies
Command: npx skills add https://github.com/dennisccy/finovae_strategy_platform --skill backtesting-trading-strategies-dennisccy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, yfinance, requests, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Validate and backtest trading strategies against historical data to assess performance, robustness, and risk.

Core Features & Use Cases

  • Built-in backtesting for 8 strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum) with full performance metrics.
  • Parameter grid search optimization to tune strategy parameters and compare results.
  • Equity curve visualization and trade-by-trade analysis for detailed review.

Quick Start

Run a backtest using a chosen strategy and data to generate performance metrics and visualizations.

Frequently Asked Questions about backtesting-trading-strategies

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

FAQPage Schema
How do I backtest crypto trading strategies against historical data?▼

You can backtest crypto trading strategies using the built-in Python engine, which applies predefined strategies to historical data and generates performance metrics, equity curves, and trade logs.

What trading strategies can I evaluate using a backtesting engine?▼

You can evaluate eight built-in trading strategies: SMA, EMA, RSI, MACD, Bollinger Bands, Breakout, Mean Reversion, and Momentum, assessing each with full performance metrics.

Can I run parameter grid search optimization to tune trading strategies?▼

Yes, you can run parameter grid search optimization to tune trading strategies, automatically testing parameter combinations and comparing the resulting performance metrics to identify optimal configurations.

Does the backtesting engine work with traditional assets or only crypto?▼

The backtesting engine supports both crypto and traditional assets, fetching historical market data via Python libraries to validate trading strategies across different financial markets.

How do I generate an equity curve visualization for my trade logs?▼

You can generate an equity curve visualization by running a backtest on your chosen strategy, which produces both graphical outputs and trade-by-trade logs for detailed performance review.

Related Skills

Explore More Data & Analytics Skills→
IgorGanapolskyIgorGanapolsky
38

Financial Data Fetcher

Fetches real-time and historical OHLCV market data via Alpaca or yfinance into standardized JSON.

Community
Intermediate
dnkrowdnkrow
1

portfolio-analytics

Computes portfolio-level return, risk, and drawdown metrics from equity curves and trade logs, optionally generating quantstats HTML reports.

Community
Intermediate
jkitchinjkitchin
34

python-plotting

Select and apply Matplotlib, Seaborn, or Plotly for Python visualizations.

Community
Intermediate
xingwudaoxingwudao
116

manage-live-trading

Integrate open-xquant with Alpaca broker APIs for guarded paper and live trading.

Community
Intermediate