docs: 文档重构 — 清理 AI agent 残留，整合 docs/ 目录结构

- 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

docs/ml-models.md

@@ -0,0 +1,45 @@
+# 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）