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均值回归策略，通过移动平均线和布林带识别价格偏离并生成回归交易信号。适用于量化金融教学与均值回归概念演示；基于简化技术指标模型，不适用于实盘交易。
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| name | mean-reversion |
| description | 均值回归策略，通过移动平均线和布林带识别价格偏离并生成回归交易信号。适用于量化金融教学与均值回归概念演示；基于简化技术指标模型，不适用于实盘交易。 |
| version | 1.0.0 |
| author | wangyi |
均值回归策略：通过移动平均线和布林带识别价格偏离均值的程度，在价格极端偏离时生成回归交易信号。
📌 说明：本工具基于简化模型实现，仅使用 Python 标准库。结果仅供学习参考，不构成投资建议或交易依据。
calc_sma(prices, window)计算简单移动平均。 必填: prices, window
calc_rolling_std(prices, window, sma)计算滚动标准差。 必填: prices, window, sma
calc_bollinger_bands(prices, window=20, num_std=2.0)计算布林带。 必填: prices
calc_zscore(prices, sma, stds)计算 Z-score。 必填: prices, sma, stds
generate_mean_reversion_signals(z_scores, pct_b, entry_z=2.0, exit_z=0.5)生成均值回归交易信号。 必填: z_scores, pct_b
calc_strategy_stats(prices, signals)计算均值回归策略的统计信息。 必填: prices, signals
run_mean_reversion(prices, window=20, num_std=2.0, entry_z=2.0, exit_z=0.5)运行完整的均值回归策略。 必填: prices
python scripts/main.py \
--prices <prices> \
--window <window> \
--num-std <num_std> \
--entry-z <entry_z> \
--exit-z <exit_z>