Skill

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Audit score 70

backtesting-trading-strategies

jeremylongshore/claude-code-plugins-plus-skills

Backtest crypto and traditional trading strategies with performance metrics, equity curves, and parameter optimization.

What is backtesting-trading-strategies?

Validate trading strategies against historical data before risking capital. This skill provides 8 pre-built strategies, comprehensive performance metrics (Sharpe, Sortino, max drawdown), equity curve visualization, and parameter grid search optimization. Use when testing strategy viability, comparing approaches, or optimizing signal parameters.

Run backtests on 8 built-in strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)
Calculate performance metrics including Sharpe ratio, Sortino ratio, Calmar ratio, max drawdown, and VaR
Generate equity curves and trade-by-trade analysis logs
Optimize strategy parameters via grid search to find best combinations
Fetch and cache historical data from yfinance for reuse
Visualize results with equity curve charts and summary reports

How to install backtesting-trading-strategies

npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill backtesting-trading-strategies

Prerequisites

Python 3.7+
pip install pandas numpy yfinance matplotlib
Optional: pip install ta-lib scipy scikit-learn for advanced features

Claude Code

Cursor

Windsurf

Cline

How to use backtesting-trading-strategies

1.Fetch historical data using fetch_data.py with desired symbol and period
2.Run backtest.py with chosen strategy, symbol, and timeframe
3.Review generated reports in ${CLAUDE_SKILL_DIR}/reports/ including summary metrics, trade log, and equity curve chart
4.Optionally run optimize.py with parameter grid to find best strategy parameters
5.Analyze performance metrics (Sharpe, max drawdown, win rate) to validate strategy viability

Use cases

Good for

Test a moving average crossover strategy on Bitcoin historical data to validate signal quality
Compare RSI reversal strategy performance across multiple assets and timeframes
Optimize SMA period parameters to find the best fast/slow combination for a given symbol
Analyze max drawdown and Sharpe ratio to assess strategy risk-adjusted returns
Run walk-forward analysis to validate strategy robustness across different market periods

Who it's for

Quantitative traders developing algorithmic strategies
Retail investors backtesting trading ideas before live deployment
Strategy researchers comparing multiple approaches on historical data
Risk managers evaluating strategy drawdown and volatility characteristics

backtesting-trading-strategies FAQ

What data sources are supported?

yfinance is the default provider, supporting stocks, crypto (BTC-USD, ETH-USD), and other Yahoo Finance symbols. Historical data is cached locally for reuse.

Can I test my own custom strategy?

Yes. The skill includes 8 pre-built strategies, but you can modify strategies.py to add custom logic or edit existing strategy definitions.

What does the Sharpe ratio tell me?

Sharpe ratio measures risk-adjusted returns (target >1.5). Higher values indicate better returns per unit of risk taken.

How do I optimize strategy parameters?

Use optimize.py with --param-grid to specify ranges for each parameter. It runs grid search to find the best combination based on Sharpe ratio or other metrics.

What output files are generated?

Reports include *_summary.txt (metrics), *_trades.csv (trade log), *_equity.csv (equity curve data), and *_chart.png (visualization).

Full instructions (SKILL.md)

Source of truth, from jeremylongshore/claude-code-plugins-plus-skills.

name: backtesting-trading-strategies description: '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".

' allowed-tools: Read, Write, Edit, Grep, Glob, Bash(python:*) version: 2.0.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT tags:

crypto
testing
performance compatibility: Designed for Claude Code, also compatible with Codex and OpenClaw

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

Run a backtest with default or custom parameters:

python ${CLAUDE_SKILL_DIR}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y
python ${CLAUDE_SKILL_DIR}/scripts/backtest.py \
  --strategy rsi_reversal \
  --symbol ETH-USD \
  --period 1y \
  --capital 10000 \  # 10000: 10 seconds in ms
  --params '{"period": 14, "overbought": 70, "oversold": 30}'

Analyze results saved to ${CLAUDE_SKILL_DIR}/reports/ -- includes *_summary.txt (performance metrics), *_trades.csv (trade log), *_equity.csv (equity curve data), and *_chart.png (visual equity curve).

Optimize parameters via grid search to find the best combination:

python ${CLAUDE_SKILL_DIR}/scripts/optimize.py \
  --strategy sma_crossover \
  --symbol BTC-USD \
  --period 1y \
  --param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}'  # HTTP 200 OK

Output

Performance Metrics

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

Risk Metrics

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

Trade Statistics

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

Example Output

================================================================================
                    BACKTEST RESULTS: SMA CROSSOVER
                    BTC-USD | [start_date] to [end_date]
================================================================================
 PERFORMANCE                          | RISK
 Total Return:        +47.32%         | Max Drawdown:      -18.45%
 CAGR:                +47.32%         | VaR (95%):         -2.34%
 Sharpe Ratio:        1.87            | Volatility:        42.1%
 Sortino Ratio:       2.41            | Ulcer Index:       8.2
--------------------------------------------------------------------------------
 TRADE STATISTICS
 Total Trades:        24              | Profit Factor:     2.34
 Win Rate:            58.3%           | Expectancy:        $197.17
 Avg Win:             $892.45         | Max Consec. Losses: 3
================================================================================

Supported Strategies

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`

Configuration

Create ${CLAUDE_SKILL_DIR}/config/settings.yaml:

data:
  provider: yfinance
  cache_dir: ./data

backtest:
  default_capital: 10000  # 10000: 10 seconds in ms
  commission: 0.001     # 0.1% per trade
  slippage: 0.0005      # 0.05% slippage

risk:
  max_position_size: 0.95
  stop_loss: null       # Optional fixed stop loss
  take_profit: null     # Optional fixed take profit

Error Handling

See ${CLAUDE_SKILL_DIR}/references/errors.md for common issues and solutions.

Examples

See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed usage examples including:

Multi-asset comparison
Walk-forward analysis
Parameter optimization workflows

Files

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

Resources

yfinance - Yahoo Finance data
TA-Lib - Technical analysis library
QuantStats - Portfolio analytics

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