Backtest crypto and traditional trading strategies against historical market data.
Works with
Includes 8 pre-built strategies (SMA, EMA, RSI, MACD, Bollinger Bands, Breakout, Mean Reversion, Momentum) with customizable parameters
Calculates comprehensive performance metrics: Sharpe, Sortino, Calmar ratios, max drawdown, VaR, volatility, win rate, and profit factor
Supports parameter grid search optimization to find best-performing configurations across strategy parameters
Generates trade-by-
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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 gracefullight/stock-checker 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:
pip install pandas numpy yfinance matplotlib
Optional for advanced features:
pip install ta-lib scipy scikit-learn
python {baseDir}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d
Data is cached to {baseDir}/data/{symbol}_{interval}.csv for reuse.
Basic backtest with default parameters:
python {baseDir}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y
Advanced backtest with custom parameters:
# Example: backtest with specific date range
python {baseDir}/scripts/backtest.py \
--strategy rsi_reversal \
--symbol ETH-USD \
--period 1y \
--capital 10000 \
--params '{"period": 14, "overbought": 70, "oversold": 30}'
Results are saved to {baseDir}/reports/ including:
*_summary.txt - Performance metrics*_trades.csv - Trade log*_equity.csv - Equity curve data*_chart.png - Visual equity curveFind optimal parameters via grid search:
python {baseDir}/scripts/optimize.py \
--strategy sma_crossover \
--symbol BTC-USD \
--period 1y \
--param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}'
| 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 {baseDir}/config/settings.yaml:
data:
provider: yfinance
cache_dir: ./data
backtest:
default_capital: 10000
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 {baseDir}/references/errors.md for common issues and solutions.
See {baseDir}/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
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✗ Don't
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✓ 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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Solid pick for teams standardizing on skills: backtesting-trading-strategies is focused, and the summary matches what you get after install.
Useful defaults in backtesting-trading-strategies — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for backtesting-trading-strategies matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend backtesting-trading-strategies for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: backtesting-trading-strategies is the kind of skill you can hand to a new teammate without a long onboarding doc.
backtesting-trading-strategies reduced setup friction for our internal harness; good balance of opinion and flexibility.
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.
backtesting-trading-strategies reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: backtesting-trading-strategies is the kind of skill you can hand to a new teammate without a long onboarding doc.
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