Backtest trading strategies against historical data with performance metrics and parameter optimization.
Works with
Includes 8 pre-built strategies (SMA, EMA, RSI, MACD, Bollinger Bands, Breakout, Mean Reversion, Momentum) for crypto and traditional assets
Calculates comprehensive metrics: Sharpe, Sortino, Calmar ratios, max drawdown, VaR, volatility, win rate, and profit factor
Supports parameter grid search optimization to find best strategy combinations
Generates equity curves, trade logs
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionbacktesting-trading-strategiesExecute the skills CLI command in your project's root directory to begin installation:
Fetches backtesting-trading-strategies from jeremylongshore/claude-code-plugins-plus-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate backtesting-trading-strategies. Access via /backtesting-trading-strategies in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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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:
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
${CLAUDE_SKILL_DIR}/data/ for reuse):
python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d
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}'
${CLAUDE_SKILL_DIR}/reports/ -- includes *_summary.txt (performance metrics), *_trades.csv (trade log), *_equity.csv (equity curve data), and *_chart.png (visual equity curve).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
| 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 |
================================================================================
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
================================================================================
| 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 |
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
See ${CLAUDE_SKILL_DIR}/references/errors.md for common issues and solutions.
See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed usage examples including:
| 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 |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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I recommend backtesting-trading-strategies for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
backtesting-trading-strategies has been reliable in day-to-day use. Documentation quality is above average for community skills.
backtesting-trading-strategies fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: backtesting-trading-strategies is focused, and the summary matches what you get after install.
I recommend backtesting-trading-strategies for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
backtesting-trading-strategies has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: backtesting-trading-strategies is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for backtesting-trading-strategies matched our evaluation — installs cleanly and behaves as described in the markdown.
We added backtesting-trading-strategies from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added backtesting-trading-strategies from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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