Simon
6acf938caf
- 删除 11 个残留文件: continuation.md, init_plan.md, reasonix.toml, djapi/continuation.md, djapi/.serena/, djapi/.claude/, djapi/.mcp.json, .claude/skills/, docs/usage.html, docs/db_schema.md, docs/report_db_design.md - 7 个 CLAUDE-*.md 移入 docs/ 并重命名去 CLAUDE- 前缀 - 新增 4 个文档: architecture.md, development.md, api.md, deployment.md - 重写 usage.md, README.md - 修复所有过时引用和交叉链接
46 lines
1.7 KiB
Markdown
46 lines
1.7 KiB
Markdown
# ML 模型层
|
||
|
||
## FeatureEngine (`finance/models/features.py`)
|
||
|
||
因子 → 特征矩阵 + 标签。防前视偏差。
|
||
|
||
```python
|
||
from models.features import FeatureEngine
|
||
fe = FeatureEngine(lookahead=5, label_type="regression")
|
||
X, y = fe.build(factor_df, price_df, fit=True)
|
||
# fit=True: Winsorize(1%/99%) → ffill → median fill → RobustScaler.fit → 标签计算
|
||
# fit=False: 复用训练时的 scaler + 有效特征
|
||
```
|
||
|
||
## LightGBM (`finance/models/lightgbm/model.py`)
|
||
|
||
```python
|
||
from models.lightgbm.model import LightGBMModel
|
||
model = LightGBMModel(params={"n_estimators": 200, "learning_rate": 0.03}, eval_ratio=0.2)
|
||
model.fit(X_train, y_train) # val>=50 行才启用早停
|
||
pred = model.predict(X_test)
|
||
imp = model.get_feature_importance() # → DataFrame
|
||
cv = model.cv_evaluate(X, y, n_folds=5) # TimeSeriesSplit
|
||
```
|
||
|
||
## CatBoost (`finance/models/catboost/model.py`)
|
||
|
||
同接口。`get_feature_importance()` / `cv_evaluate()`。
|
||
|
||
## ML 策略 (`finance/models/backtest_integration.py`)
|
||
|
||
```python
|
||
from models.backtest_integration import MLStrategy, MLBenchmark
|
||
strategy = MLStrategy(model, fe, buy_quantile=0.7, sell_quantile=0.3, rebalance_freq=5)
|
||
# 预测值分位 → 动态阈值 → 交易信号
|
||
benchmark = MLBenchmark([lgb, cb], fe, price_df, factor_df)
|
||
result = benchmark.run() # → DataFrame: model × (IC, return, sharpe, win_rate, trades)
|
||
```
|
||
|
||
## 重要约束
|
||
|
||
- lookahead 固定，不输入模型（防目标泄露）
|
||
- 单股票 IC≈0 是正常现象（噪声主导），多股票截面才是 ML 发挥价值的地方
|
||
- 特征工程严禁使用未来数据（RobustScaler fit 在训练集，transform 在测试集）
|
||
- 交叉验证用 TimeSeriesSplit（不 shuffle）
|