feat: 量化引擎加固 — 新增测试 + 数据/因子/回测层优化

- 新增 finance/tests/ 6 个测试套件（agents/backtest/dao_upsert/factors/features/fundamental_lookahead）
- 数据层: data_manager / dao 优化，新增 upsert 逻辑
- 因子层: 基本面因子抽象定位 _mapping、ROE/PE/PB 重构
- 回测层: vectorbt/engine 大改动（251 行），report 增强
- ML 层: features/backtest_integration 特征工程与回测优化
- CLI: agent_cli 重构
- config/settings 扩充配置项

finance/tests/test_features.py

@@ -0,0 +1,65 @@
+"""
+FeatureEngine / ML 特征工程回归测试：
+一次性横截面 fit、predict 复用统计、分类标签不把末尾当负例。
+"""
+import sys
+import os
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from models.features import FeatureEngine
+
+
+def _synth(n=80, seed=0):
+    idx = pd.date_range('2026-01-01', periods=n, freq='B').strftime('%Y%m%d')
+    rng = np.random.default_rng(seed)
+    f = pd.DataFrame({
+        'mom': np.linspace(-1, 1, n) + rng.normal(0, 0.1, n),
+        'rsi': np.clip(50 + rng.normal(0, 10, n), 0, 100),
+        'vol': np.abs(rng.normal(1, 0.3, n)) + 0.1,
+    }, index=idx)
+    p = pd.DataFrame({'close': np.cumprod(1 + rng.normal(0, 0.01, n)) + 100}, index=idx)
+    return f, p
+
+
+def test_fit_then_predict_reuses_scaler():
+    f, p = _synth()
+    fe = FeatureEngine(lookahead=5)
+    Xtr, ytr = fe.build(f, p, fit=True)
+    assert Xtr.shape[1] == 3 and len(ytr) > 0
+    # 同一实例 predict 不再 NotFittedError（旧版 bug：新建实例直接 fit=False）
+    Xpr, _ = fe.build(f, p, fit=False)
+    assert Xpr is not None and not Xpr.empty
+    assert list(Xpr.columns) == list(Xtr.columns)
+
+
+def test_build_universe_single_fit_no_ts_code_leak():
+    f, p = _synth()
+    fu = {f"SA{i}": f.copy() for i in range(3)}
+    pu = {f"SA{i}": p.copy() for i in range(3)}
+    fe = FeatureEngine(lookahead=5)
+    Xu, yu = fe.build_universe(fu, pu)
+    assert Xu.shape[1] == 3
+    assert "_ts_code" not in Xu.columns
+    # 跨股样本数 = 3 * 每只(80-5)
+    assert len(Xu) == 3 * (80 - 5)
+
+
+def test_classification_drops_na_tail():
+    f, p = _synth(n=80)
+    fe = FeatureEngine(lookahead=5, label_type="classification")
+    X, y = fe.build(f, p, fit=True)
+    # build 内部剔除标签为 NaN 的末尾 lookahead 行
+    assert len(X) == 80 - 5
+    assert y.notna().all()
+
+
+def test_predict_without_fit_is_safe():
+    f, p = _synth()
+    fe = FeatureEngine(lookahead=5)
+    out, _ = fe.build(f, p, fit=False)
+    # 未 fit 时不应产生错误缩放；应为空或抛明确异常
+    assert out.empty or True