backtesting-trading-strategies

•

1.9k2593 months ago

Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".

Quick Install

npxskills add jeremylongshore/claude-code-plugins-plus-skills--skill backtesting-trading-strategies

Instructions

Backtesting Trading Strategies

Overview

Validate trading strategies against historical data before risking real capital. This skill provides a complete backtesting framework with 8 built-in strategies, comprehensive performance metrics, and parameter optimization.

Key Features:

8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)
Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)
Parameter grid search optimization
Equity curve visualization
Trade-by-trade analysis

Prerequisites

Install required dependencies:

set -euo pipefail
pip install pandas numpy yfinance matplotlib

Optional for advanced features:

set -euo pipefail
pip install ta-lib scipy scikit-learn

Instructions

Fetch historical data (cached to ${CLAUDE_SKILL_DIR}/data/ for reuse):

python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d

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Tags & Topics

aiautomationclaude-codedevopsmcpai-agents

Backtesting Trading Strategies

Overview

Key Features:

8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)
Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)
Parameter grid search optimization
Equity curve visualization
Trade-by-trade analysis

Prerequisites

Install required dependencies:

set -euo pipefail
pip install pandas numpy yfinance matplotlib

Optional for advanced features:

set -euo pipefail
pip install ta-lib scipy scikit-learn

Instructions

Fetch historical data (cached to ${CLAUDE_SKILL_DIR}/data/ for reuse):

python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d

Metric	Description
Total Return	Overall percentage gain/loss
CAGR	Compound annual growth rate
Sharpe Ratio	Risk-adjusted return (target: >1.5)
Sortino Ratio	Downside risk-adjusted return
Calmar Ratio	Return divided by max drawdown

Metric	Description
Max Drawdown	Largest peak-to-trough decline
VaR (95%)	Value at Risk at 95% confidence
CVaR (95%)	Expected loss beyond VaR
Volatility	Annualized standard deviation

Metric	Description
Total Trades	Number of round-trip trades
Win Rate	Percentage of profitable trades
Profit Factor	Gross profit divided by gross loss
Expectancy	Expected value per trade

Strategy	Description	Key Parameters
`sma_crossover`	Simple moving average crossover	`fast_period`, `slow_period`
`ema_crossover`	Exponential MA crossover	`fast_period`, `slow_period`
`rsi_reversal`	RSI overbought/oversold	`period`, `overbought`, `oversold`
`macd`	MACD signal line crossover	`fast`, `slow`, `signal`
`bollinger_bands`	Mean reversion on bands	`period`, `std_dev`
`breakout`	Price breakout from range	`lookback`, `threshold`
`mean_reversion`	Return to moving average	`period`, `z_threshold`
`momentum`	Rate of change momentum	`period`, `threshold`

File	Purpose
`scripts/backtest.py`	Main backtesting engine
`scripts/fetch_data.py`	Historical data fetcher
`scripts/strategies.py`	Strategy definitions
`scripts/metrics.py`	Performance calculations
`scripts/optimize.py`	Parameter optimization

backtesting-trading-strategies

Backtesting Trading Strategies

Overview

Prerequisites

Instructions

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Backtesting Trading Strategies

Overview

Prerequisites

Instructions

Output

Performance Metrics

Risk Metrics

Trade Statistics

Example Output

Supported Strategies

Configuration

Error Handling

Examples

Files

Resources

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