gaaiyun/deepdow-portfolio-skill
/SKILL.md
深度学习组合优化与四个非 DL 基准（equal_weight / momentum / min_variance / risk_parity）在同一份数据上做 Sharpe / 最大回撤 / Calmar 对比的实验工具。
tools
Updated May 31, 2026
$ install --global
skillsauthnpx skillsauth add gaaiyun/deepdow-portfolio-skill deepdow-portfolio-skillInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- deepdow-portfolio-skill
- description:
- 深度学习组合优化与四个非 DL 基准（equal_weight / momentum / min_variance / risk_parity）在同一份数据上做 Sharpe / 最大回撤 / Calmar 对比的实验工具。
deepdow-portfolio-skill
什么时候用
- "帮我跑一下 AAPL / MSFT / GOOG / AMZN 这几个的最小方差组合"
- "等权 1/N 和风险平价在过去两年谁更好"
- "我想训一个 DL 组合优化模型，看看比经典方法好多少"
- "把我这份 CSV 收益率数据跑一遍 portfolio backtest"
入口
python __main__.py fetch --tickers AAPL,MSFT,GOOG --start 2022-01-01 -o data.csv
python __main__.py baseline --csv data.csv --strategy risk_parity
python __main__.py train --csv data.csv --epochs 30 -o outputs/model.pth
python __main__.py compare --csv data.csv -o report.json
库调用入口：
scripts/baselines.py::BASELINES—{"equal_weight": fn, "momentum_topk": fn, ...}scripts/portfolio_eval.py::compare(returns, weights_by_name) -> List[PerformanceMetrics]scripts/data_io.py::load_yfinance / load_csv / synthetic_returnsscripts/simple_model.py::SimpleNet / train_simple / predict_weights
评估指标
| 指标 | 含义 |
|---|---|
| cum_return | 整段累计收益（不年化） |
| annualized_return | 按 252 period/年年化 |
| annualized_volatility | 同上 |
| sharpe | (mean - rf) / std × √252 |
| sortino | 只用下行波动算分母 |
| max_drawdown | 累计净值最大回撤（负数） |
| calmar | 年化收益 / |最大回撤| |
依赖
- 必需：Python 3.10+，numpy / pandas / torch
- 可选 yfinance（抓真实日线）
- 可选 deepdow（用真 deepdow 框架那条路径：
scripts/train.py/predict.py）
注意事项
- 合成数据是 i.i.d. 高斯，没可学时序结构；DL 模型在合成数据上没必然优势。
- 拿对比报告做投资决策前请用至少一段 out-of-sample 数据复核，不构成投资建议。
- 月线 / 周线数据要把
evaluate(..., periods_per_year=12)/=52传对。
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