--- name: backtesting-trading-strategies-cf5508b28d4d 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 license: MIT compatible-with: claude-code, codex, openclaw tags: [crypto, testing, performance] --- # 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: ```bash set -euo pipefail pip install pandas numpy yfinance matplotlib ``` Optional for advanced features: ```bash set -euo pipefail pip install ta-lib scipy scikit-learn ``` ## Instructions 1. Fetch historical data (cached to `${CLAUDE_SKILL_DIR}/data/` for reuse): ```bash python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d ``` 2. Run a backtest with default or custom parameters: ```bash python ${CLAUDE_SKILL_DIR}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y python ${CLAUDE_SKILL_DIR}/scripts/backtest.py \ --strategy rsi_reversal \ --symbol ETH-USD \ --period 1y \ --capital 10000 \ # 10000: 10 seconds in ms --params '{"period": 14, "overbought": 70, "oversold": 30}' ``` 3. Analyze results saved to `${CLAUDE_SKILL_DIR}/reports/` -- includes `*_summary.txt` (performance metrics), `*_trades.csv` (trade log), `*_equity.csv` (equity curve data), and `*_chart.png` (visual equity curve). 4. Optimize parameters via grid search to find the best combination: ```bash 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`: ```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](https://github.com/ranaroussi/yfinance) - Yahoo Finance data - [TA-Lib](https://ta-lib.org/) - Technical analysis library - [QuantStats](https://github.com/ranaroussi/quantstats) - Portfolio analytics --- Source attribution: Jeremy Longshore Original project: https://github.com/aibot88/sec_skill_store/blob/608120788167d56f99e31449e411e603ed250dc6/skills/claudskills/backtesting-trading-strategies/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Jeremy Longshore Original project: https://github.com/aibot88/sec_skill_store/blob/608120788167d56f99e31449e411e603ed250dc6/skills/claudskills/backtesting-trading-strategies/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Finance & Accounting, Marketing Roles: Data Analyst, Financial Analyst, Growth Marketer Specialties: Testing Tools: Pandas, Numpy, Yfinance, Matplotlib, Ta Lib, Scipy, Scikit Learn Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: fe946a111053b2cb070f03a051e77224b86d78c86fa693bfcf2bcc7386432eb9