backtesting-trading-strategies

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Backtesting Trading Strategies

交易策略回测

Overview

概述

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

在投入真实资金前，基于历史数据验证交易策略。本Skill提供完整的回测框架，包含8种内置策略、全面的绩效指标以及参数优化功能。

核心特性：

8种预构建交易策略（SMA、EMA、RSI、MACD、布林带、突破、均值回归、动量）
完整绩效指标（Sharpe比率、Sortino比率、Calmar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

Prerequisites

前置条件

Install required dependencies:

bash

pip install pandas numpy yfinance matplotlib

Optional for advanced features:

bash

pip install ta-lib scipy scikit-learn

安装所需依赖：

bash

pip install pandas numpy yfinance matplotlib

高级功能可选依赖：

bash

pip install ta-lib scipy scikit-learn

Instructions

操作步骤

Step 1: Fetch Historical Data

步骤1：获取历史数据

bash

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

Data is cached to

{baseDir}/data/{symbol}_{interval}.csv

for reuse.

bash

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

数据会缓存到

{baseDir}/data/{symbol}_{interval}.csv

以便重复使用。

Step 2: Run Backtest

步骤2：运行回测

Basic backtest with default parameters:

bash

python {baseDir}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y

Advanced backtest with custom parameters:

bash

undefined

使用默认参数进行基础回测：

bash

python {baseDir}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y

使用自定义参数进行高级回测：

bash

undefined

Example: backtest with specific date range

示例：针对特定日期范围进行回测

python {baseDir}/scripts/backtest.py
--strategy rsi_reversal
--symbol ETH-USD
--period 1y
--capital 10000
--params '{"period": 14, "overbought": 70, "oversold": 30}'

undefined

python {baseDir}/scripts/backtest.py
--strategy rsi_reversal
--symbol ETH-USD
--period 1y
--capital 10000
--params '{"period": 14, "overbought": 70, "oversold": 30}'

undefined

Step 3: Analyze Results

步骤3：分析结果

Results are saved to

{baseDir}/reports/

including:

```
*_summary.txt
```
- Performance metrics
```
*_trades.csv
```
- Trade log
```
*_equity.csv
```
- Equity curve data
```
*_chart.png
```
- Visual equity curve

结果会保存到

{baseDir}/reports/

目录，包含：

```
*_summary.txt
```
- 绩效指标汇总
```
*_trades.csv
```
- 交易日志
```
*_equity.csv
```
- 权益曲线数据
```
*_chart.png
```
- 可视化权益曲线

Step 4: Optimize Parameters

步骤4：优化参数

Find optimal parameters via grid search:

bash

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]}'

通过网格搜索寻找最优参数：

bash

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]}'

Output

输出内容

Performance Metrics

绩效指标

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

指标	描述
总回报率	整体盈利/亏损百分比
CAGR	复合年增长率
Sharpe比率	风险调整后回报率（目标：>1.5）
Sortino比率	下行风险调整后回报率
Calmar比率	回报率除以最大回撤

Risk Metrics

风险指标

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

指标	描述
最大回撤	最大的峰谷跌幅
VaR (95%)	95%置信水平下的风险价值
CVaR (95%)	VaR之外的预期损失
波动率	年化标准差

Trade Statistics

交易统计

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

指标	描述
总交易次数	往返交易的数量
胜率	盈利交易的百分比
利润因子	总盈利除以总亏损
预期收益	每笔交易的预期价值

Example Output

示例输出

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

================================================================================
                    回测结果：SMA交叉策略
                    BTC-USD | [开始日期] 至 [结束日期]
================================================================================
 绩效                          | 风险
 总回报率:        +47.32%         | 最大回撤:      -18.45%
 CAGR:                +47.32%         | VaR (95%):         -2.34%
 Sharpe比率:        1.87            | 波动率:        42.1%
 Sortino比率:       2.41            | 溃疡指数:       8.2
--------------------------------------------------------------------------------
 交易统计
 总交易次数:        24              | 利润因子:     2.34
 胜率:            58.3%           | 预期收益:        $197.17
 平均盈利:             $892.45         | 连续最大亏损次数: 3
================================================================================

Supported Strategies

支持的策略

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`

策略	描述	核心参数
`sma_crossover`	简单移动平均交叉	`fast_period` , `slow_period`
`ema_crossover`	指数移动平均交叉	`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`	基于布林带的均值回归	`period` , `std_dev`
`breakout`	价格突破区间	`lookback` , `threshold`
`mean_reversion`	回归至移动平均	`period` , `z_threshold`
`momentum`	变动率动量	`period` , `threshold`

Configuration

配置

Create

{baseDir}/config/settings.yaml

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

创建

{baseDir}/config/settings.yaml

文件：

yaml

data:
  provider: yfinance
  cache_dir: ./data

backtest:
  default_capital: 10000
  commission: 0.001     # 每笔交易0.1%
  slippage: 0.0005      # 0.05%滑点

risk:
  max_position_size: 0.95
  stop_loss: null       # 可选固定止损
  take_profit: null     # 可选固定止盈

Error Handling

错误处理

See

{baseDir}/references/errors.md

for common issues and solutions.

请查看

{baseDir}/references/errors.md

获取常见问题及解决方案。

Examples

示例

See

{baseDir}/references/examples.md

for detailed usage examples including:

Multi-asset comparison
Walk-forward analysis
Parameter optimization workflows

请查看

{baseDir}/references/examples.md

获取详细使用示例，包括：

多资产比较
滚动窗口分析
参数优化工作流

Files

文件说明

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

文件	用途
`scripts/backtest.py`	主回测引擎
`scripts/fetch_data.py`	历史数据获取工具
`scripts/strategies.py`	策略定义文件
`scripts/metrics.py`	绩效计算模块
`scripts/optimize.py`	参数优化工具

Resources

参考资源

yfinance - Yahoo Finance data
TA-Lib - Technical analysis library
QuantStats - Portfolio analytics

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