交易策略回测框架：8种内置策略、参数优化与完整性能指标分析

backtesting-trading-strategies by jeremylongshore/claude-code-plugins-plus-skills

2,600 周安装量

1,700 GitHub Stars

安装命令

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

AI/机器学习开发数据分析区块链

🇨🇳中文介绍

回测交易策略

概述

在投入真实资金前，使用历史数据验证交易策略。此技能提供了一个完整的回测框架，包含 8 种内置策略、全面的性能指标和参数优化功能。

主要特性：

8 种预置交易策略（SMA、EMA、RSI、MACD、布林带、突破、均值回归、动量）
完整的性能指标（夏普比率、索提诺比率、卡尔玛比率、VaR、最大回撤）
参数网格搜索优化
权益曲线可视化
逐笔交易分析

先决条件

安装所需依赖：

set -euo pipefail
pip install pandas numpy yfinance matplotlib

高级功能可选安装：

set -euo pipefail
pip install ta-lib scipy scikit-learn

使用说明

获取历史数据（将缓存到 ${CLAUDE_SKILL_DIR}/data/ 以供重用）：

python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d

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使用默认或自定义参数运行回测：

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}'

分析保存到 ${CLAUDE_SKILL_DIR}/reports/ 的结果——包括 *_summary.txt（性能指标）、*_trades.csv（交易日志）、*_equity.csv（权益曲线数据）和 *_chart.png（可视化权益曲线）。

通过网格搜索优化参数以找到最佳组合：

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

指标	描述
总回报率	总体百分比收益/损失
年复合增长率	复合年增长率
夏普比率	风险调整后收益（目标：>1.5）
索提诺比率	下行风险调整后收益
卡尔玛比率	收益除以最大回撤

指标	描述
最大回撤	最大的峰谷跌幅
风险价值 (95%)	95% 置信度下的风险价值
条件风险价值 (95%)	超出 VaR 的预期损失
波动率	年化标准差

指标	描述
总交易次数	往返交易次数
胜率	盈利交易百分比
盈亏比	总利润除以总亏损
期望值	每笔交易的预期价值

================================================================================
                        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
================================================================================

策略	描述	关键参数
`sma_crossover`	简单移动平均线交叉	`fast_period`, `slow_period`
`ema_crossover`	指数移动平均线交叉	`fast_period`, `slow_period`
`rsi_reversal`	RSI 超买/超卖	`period`, `overbought`, `oversold`
`macd`	MACD 信号线交叉	`fast`, `slow`, `signal`
`bollinger_bands`	布林带均值回归	`period`, `std_dev`
`breakout`	价格区间突破	`lookback`, `threshold`
`mean_reversion`	回归移动平均线	`period`, `z_threshold`
`momentum`	变化率动量	`period`, `threshold`

创建 ${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

常见问题及解决方案请参阅 ${CLAUDE_SKILL_DIR}/references/errors.md。

详细使用示例请参阅 ${CLAUDE_SKILL_DIR}/references/examples.md，包括：

多资产比较
前向滚动分析
参数优化工作流

文件	用途
`scripts/backtest.py`	主回测引擎
`scripts/fetch_data.py`	历史数据获取器
`scripts/strategies.py`	策略定义
`scripts/metrics.py`	性能计算
`scripts/optimize.py`	参数优化

yfinance - Yahoo Finance 数据
TA-Lib - 技术分析库
QuantStats - 投资组合分析

🇺🇸English

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

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

Weekly Installs

2.6K

Repository

jeremylongshore…s-skills

GitHub Stars

1.7K

First Seen

Jan 26, 2026

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