[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H4W6MM6XWJS7TAPJSNWJ5V":3,"skill-same:01M6H4W6MM6XWJS7TAPJSNWJ5V":129},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M6H4W6MM6XWJS7TAPJSNWJ5V","backtesting-trading-strategies",null,"Backtest crypto and traditional trading strategies against historical data.\nCalculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves,\nand optimizes strategy parameters. Use when user wants to test a trading strategy,\nvalidate signals, or compare approaches.\nTrigger with phrases like \"backtest strategy\", \"test trading strategy\", \"historical performance\",\n\"simulate trades\", \"optimize parameters\", or \"validate signals\".","micsapp\u002Fmicstec-skills","micsapp","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F82411001?v=4","01M6GWCFQ6D3PV8EHNY62NN5VW","01M6GWCFVE1PA5HG0DKC7AMFJ3",4,"MIT","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fcrypto\u002Ftrading-strategy-backtester\u002Fskills\u002Fbacktesting-trading-strategies\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"strategies","trading","backtesting trading strategies","backtesting","optimize","with","equity","compare","trigger","io","signals","crypto","python","use","strategy","Bash","code","test","performance","codex","2026-11-10 10:12:30",false,"---\nname: backtesting-trading-strategies\ndescription: |\n  Backtest crypto and traditional trading strategies against historical data.\n  Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves,\n  and optimizes strategy parameters. Use when user wants to test a trading strategy,\n  validate signals, or compare approaches.\n  Trigger with phrases like \"backtest strategy\", \"test trading strategy\", \"historical performance\",\n  \"simulate trades\", \"optimize parameters\", or \"validate signals\".\nallowed-tools: Read, Write, Edit, Grep, Glob, Bash(python:*)\nversion: 2.0.0\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\nlicense: MIT\ncompatible-with: claude-code, codex, openclaw\n---\n# Backtesting Trading Strategies\n\n## Overview\n\nValidate 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.\n\n**Key Features:**\n- 8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)\n- Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)\n- Parameter grid search optimization\n- Equity curve visualization\n- Trade-by-trade analysis\n\n## Prerequisites\n\nInstall required dependencies:\n\n```bash\nset -euo pipefail\npip install pandas numpy yfinance matplotlib\n```\n\nOptional for advanced features:\n```bash\nset -euo pipefail\npip install ta-lib scipy scikit-learn\n```\n\n## Instructions\n\n1. Fetch historical data (cached to `${CLAUDE_SKILL_DIR}\u002Fdata\u002F` for reuse):\n   ```bash\n   python ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Ffetch_data.py --symbol BTC-USD --period 2y --interval 1d\n   ```\n2. Run a backtest with default or custom parameters:\n   ```bash\n   python ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Fbacktest.py --strategy sma_crossover --symbol BTC-USD --period 1y\n   python ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Fbacktest.py \\\n     --strategy rsi_reversal \\\n     --symbol ETH-USD \\\n     --period 1y \\\n     --capital 10000 \\  # 10000: 10 seconds in ms\n     --params '{\"period\": 14, \"overbought\": 70, \"oversold\": 30}'\n   ```\n3. Analyze results saved to `${CLAUDE_SKILL_DIR}\u002Freports\u002F` -- includes `*_summary.txt` (performance metrics), `*_trades.csv` (trade log), `*_equity.csv` (equity curve data), and `*_chart.png` (visual equity curve).\n4. Optimize parameters via grid search to find the best combination:\n   ```bash\n   python ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Foptimize.py \\\n     --strategy sma_crossover \\\n     --symbol BTC-USD \\\n     --period 1y \\\n     --param-grid '{\"fast_period\": [10, 20, 30], \"slow_period\": [50, 100, 200]}'  # HTTP 200 OK\n   ```\n\n## Output\n\n### Performance Metrics\n\n| Metric | Description |\n|--------|-------------|\n| Total Return | Overall percentage gain\u002Floss |\n| CAGR | Compound annual growth rate |\n| Sharpe Ratio | Risk-adjusted return (target: >1.5) |\n| Sortino Ratio | Downside risk-adjusted return |\n| Calmar Ratio | Return divided by max drawdown |\n\n### Risk Metrics\n\n| Metric | Description |\n|--------|-------------|\n| Max Drawdown | Largest peak-to-trough decline |\n| VaR (95%) | Value at Risk at 95% confidence |\n| CVaR (95%) | Expected loss beyond VaR |\n| Volatility | Annualized standard deviation |\n\n### Trade Statistics\n\n| Metric | Description |\n|--------|-------------|\n| Total Trades | Number of round-trip trades |\n| Win Rate | Percentage of profitable trades |\n| Profit Factor | Gross profit divided by gross loss |\n| Expectancy | Expected value per trade |\n\n### Example Output\n\n```\n================================================================================\n                    BACKTEST RESULTS: SMA CROSSOVER\n                    BTC-USD | [start_date] to [end_date]\n================================================================================\n PERFORMANCE                          | RISK\n Total Return:        +47.32%         | Max Drawdown:      -18.45%\n CAGR:                +47.32%         | VaR (95%):         -2.34%\n Sharpe Ratio:        1.87            | Volatility:        42.1%\n Sortino Ratio:       2.41            | Ulcer Index:       8.2\n--------------------------------------------------------------------------------\n TRADE STATISTICS\n Total Trades:        24              | Profit Factor:     2.34\n Win Rate:            58.3%           | Expectancy:        $197.17\n Avg Win:             $892.45         | Max Consec. Losses: 3\n================================================================================\n```\n\n## Supported Strategies\n\n| Strategy | Description | Key Parameters |\n|----------|-------------|----------------|\n| `sma_crossover` | Simple moving average crossover | `fast_period`, `slow_period` |\n| `ema_crossover` | Exponential MA crossover | `fast_period`, `slow_period` |\n| `rsi_reversal` | RSI overbought\u002Foversold | `period`, `overbought`, `oversold` |\n| `macd` | MACD signal line crossover | `fast`, `slow`, `signal` |\n| `bollinger_bands` | Mean reversion on bands | `period`, `std_dev` |\n| `breakout` | Price breakout from range | `lookback`, `threshold` |\n| `mean_reversion` | Return to moving average | `period`, `z_threshold` |\n| `momentum` | Rate of change momentum | `period`, `threshold` |\n\n## Configuration\n\nCreate `${CLAUDE_SKILL_DIR}\u002Fconfig\u002Fsettings.yaml`:\n\n```yaml\ndata:\n  provider: yfinance\n  cache_dir: .\u002Fdata\n\nbacktest:\n  default_capital: 10000  # 10000: 10 seconds in ms\n  commission: 0.001     # 0.1% per trade\n  slippage: 0.0005      # 0.05% slippage\n\nrisk:\n  max_position_size: 0.95\n  stop_loss: null       # Optional fixed stop loss\n  take_profit: null     # Optional fixed take profit\n```\n\n## Error Handling\n\nSee `${CLAUDE_SKILL_DIR}\u002Freferences\u002Ferrors.md` for common issues and solutions.\n\n## Examples\n\nSee `${CLAUDE_SKILL_DIR}\u002Freferences\u002Fexamples.md` for detailed usage examples including:\n- Multi-asset comparison\n- Walk-forward analysis\n- Parameter optimization workflows\n\n## Files\n\n| File | Purpose |\n|------|---------|\n| `scripts\u002Fbacktest.py` | Main backtesting engine |\n| `scripts\u002Ffetch_data.py` | Historical data fetcher |\n| `scripts\u002Fstrategies.py` | Strategy definitions |\n| `scripts\u002Fmetrics.py` | Performance calculations |\n| `scripts\u002Foptimize.py` | Parameter optimization |\n\n## Resources\n\n- [yfinance](https:\u002F\u002Fgithub.com\u002Franaroussi\u002Fyfinance) - Yahoo Finance data\n- [TA-Lib](https:\u002F\u002Fta-lib.org\u002F) - Technical analysis library\n- [QuantStats](https:\u002F\u002Fgithub.com\u002Franaroussi\u002Fquantstats) - Portfolio analytics","# Backtesting Trading Strategies\n\n## Overview\n\nValidate 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.\n\n**Key Features:**\n- 8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)\n- Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)\n- Parameter grid search optimization\n- Equity curve visualization\n- Trade-by-trade analysis\n\n## Prerequisites\n\nInstall required dependencies:\n\n```bash\nset -euo pipefail\npip install pandas numpy yfinance matplotlib\n```\n\nOptional for advanced features:\n```bash\nset -euo pipefail\npip install ta-lib scipy scikit-learn\n```\n\n## Instructions\n\n1. Fetch historical data (cached to `${CLAUDE_SKILL_DIR}\u002Fdata\u002F` for reuse):\n   ```bash\n   python ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Ffetch_data.py --symbol BTC-USD --period 2y --interval 1d\n   ```\n2. Run a backtest with default or custom parameters:\n   ```bash\n   python ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Fbacktest.py --strategy sma_crossover --symbol BTC-USD --period 1y\n   python ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Fbacktest.py \\\n     --strategy rsi_reversal \\\n     --symbol ETH-USD \\\n     --period 1y \\\n     --capital 10000 \\  # 10000: 10 seconds in ms\n     --params '{\"period\": 14, \"overbought\": 70, \"oversold\": 30}'\n   ```\n3. Analyze results saved to `${CLAUDE_SKILL_DIR}\u002Freports\u002F` -- includes `*_summary.txt` (performance metrics), `*_trades.csv` (trade log), `*_equity.csv` (equity curve data), and `*_chart.png` (visual equity curve).\n4. Optimize parameters via grid search to find the best combination:\n   ```bash\n   python ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Foptimize.py \\\n     --strategy sma_crossover \\\n     --symbol BTC-USD \\\n     --period 1y \\\n     --param-grid '{\"fast_period\": [10, 20, 30], \"slow_period\": [50, 100, 200]}'  # HTTP 200 OK\n   ```\n\n## Output\n\n### Performance Metrics\n\n| Metric | Description |\n|--------|-------------|\n| Total Return | Overall percentage gain\u002Floss |\n| CAGR | Compound annual growth rate |\n| Sharpe Ratio | Risk-adjusted return (target: >1.5) |\n| Sortino Ratio | Downside risk-adjusted return |\n| Calmar Ratio | Return divided by max drawdown |\n\n### Risk Metrics\n\n| Metric | Description |\n|--------|-------------|\n| Max Drawdown | Largest peak-to-trough decline |\n| VaR (95%) | Value at Risk at 95% confidence |\n| CVaR (95%) | Expected loss beyond VaR |\n| Volatility | Annualized standard deviation |\n\n### Trade Statistics\n\n| Metric | Description |\n|--------|-------------|\n| Total Trades | Number of round-trip trades |\n| Win Rate | Percentage of profitable trades |\n| Profit Factor | Gross profit divided by gross loss |\n| Expectancy | Expected value per trade |\n\n### Example Output\n\n```\n================================================================================\n                    BACKTEST RESULTS: SMA CROSSOVER\n                    BTC-USD | [start_date] to [end_date]\n================================================================================\n PERFORMANCE                          | RISK\n Total Return:        +47.32%         | Max Drawdown:      -18.45%\n CAGR:                +47.32%         | VaR (95%):         -2.34%\n Sharpe Ratio:        1.87            | Volatility:        42.1%\n Sortino Ratio:       2.41            | Ulcer Index:       8.2\n--------------------------------------------------------------------------------\n TRADE STATISTICS\n Total Trades:        24              | Profit Factor:     2.34\n Win Rate:            58.3%           | Expectancy:        $197.17\n Avg Win:             $892.45         | Max Consec. Losses: 3\n================================================================================\n```\n\n## Supported Strategies\n\n| Strategy | Description | Key Parameters |\n|----------|-------------|----------------|\n| `sma_crossover` | Simple moving average crossover | `fast_period`, `slow_period` |\n| `ema_crossover` | Exponential MA crossover | `fast_period`, `slow_period` |\n| `rsi_reversal` | RSI overbought\u002Foversold | `period`, `overbought`, `oversold` |\n| `macd` | MACD signal line crossover | `fast`, `slow`, `signal` |\n| `bollinger_bands` | Mean reversion on bands | `period`, `std_dev` |\n| `breakout` | Price breakout from range | `lookback`, `threshold` |\n| `mean_reversion` | Return to moving average | `period`, `z_threshold` |\n| `momentum` | Rate of change momentum | `period`, `threshold` |\n\n## Configuration\n\nCreate `${CLAUDE_SKILL_DIR}\u002Fconfig\u002Fsettings.yaml`:\n\n```yaml\ndata:\n  provider: yfinance\n  cache_dir: .\u002Fdata\n\nbacktest:\n  default_capital: 10000  # 10000: 10 seconds in ms\n  commission: 0.001     # 0.1% per trade\n  slippage: 0.0005      # 0.05% slippage\n\nrisk:\n  max_position_size: 0.95\n  stop_loss: null       # Optional fixed stop loss\n  take_profit: null     # Optional fixed take profit\n```\n\n## Error Handling\n\nSee `${CLAUDE_SKILL_DIR}\u002Freferences\u002Ferrors.md` for common issues and solutions.\n\n## Examples\n\nSee `${CLAUDE_SKILL_DIR}\u002Freferences\u002Fexamples.md` for detailed usage examples including:\n- Multi-asset comparison\n- Walk-forward analysis\n- Parameter optimization workflows\n\n## Files\n\n| File | Purpose |\n|------|---------|\n| `scripts\u002Fbacktest.py` | Main backtesting engine |\n| `scripts\u002Ffetch_data.py` | Historical data fetcher |\n| `scripts\u002Fstrategies.py` | Strategy definitions |\n| `scripts\u002Fmetrics.py` | Performance calculations |\n| `scripts\u002Foptimize.py` | Parameter optimization |\n\n## Resources\n\n- [yfinance](https:\u002F\u002Fgithub.com\u002Franaroussi\u002Fyfinance) - Yahoo Finance data\n- [TA-Lib](https:\u002F\u002Fta-lib.org\u002F) - Technical analysis library\n- [QuantStats](https:\u002F\u002Fgithub.com\u002Franaroussi\u002Fquantstats) - Portfolio analytics",{"name":5,"author":43,"license":14,"version":44,"description":45,"allowed-tools":46,"compatible-with":47},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","2.0.0","Backtest crypto and traditional trading strategies against historical data.\nCalculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves,\nand optimizes strategy parameters. 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