test+fix: 自动化测试18条全过 + 量比基准排除当日 + 截面打分最低有效因子数修正

tests/test_quant_model.py

@@ -0,0 +1,85 @@
+# -*- coding: utf-8 -*-
+"""量化模型单测：因子计算 / 打分 / 权重微调 / 推荐原因"""
+import sys, os, warnings
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+warnings.filterwarnings('ignore')
+
+import numpy as np
+import pandas as pd
+import pytest
+from tests.helpers import gen_kline, gen_trending_up, gen_trending_down
+from src.quant.model import (compute_factors, cross_section_score,
+                             factor_ic_series, WeightStore, state_bucket,
+                             FACTOR_NAMES)
+from src.quant.reason import build_reason
+
+
+class TestFactors:
+    def test_momentum_positive_in_uptrend(self):
+        k = gen_trending_up(60)
+        f = compute_factors(k)
+        assert f.get('mom_20', 0) > 0
+
+    def test_momentum_negative_in_downtrend(self):
+        k = gen_trending_down(60)
+        f = compute_factors(k)
+        assert f.get('mom_20', 0) < 0
+
+    def test_all_factors_present(self):
+        k = gen_kline(60)
+        f = compute_factors(k)
+        expected = {'mom_20', 'trend_ma20', 'ma_align', 'vol_ratio', 'rsi_inv', 'macd_hist', 'vola_inv'}
+        assert expected.issubset(set(f.keys()))
+
+    def test_insufficient_data_returns_empty(self):
+        k = gen_kline(10)
+        assert compute_factors(k) == {}
+
+
+class TestCrossSectionScore:
+    def test_ranking_order(self):
+        rows = {
+            'A': {'mom_20': 0.10, 'trend_ma20': 0.05},
+            'B': {'mom_20': -0.05, 'trend_ma20': -0.02},
+            'C': {'mom_20': 0.02, 'trend_ma20': 0.01},
+        }
+        weights = {'mom_20': 1.0, 'trend_ma20': 1.0}
+        scored = cross_section_score(rows, weights)
+        assert scored[0][0] == 'A'
+        assert scored[-1][0] == 'B'
+
+    def test_single_factor_still_scored(self):
+        """优化后：只要有 ≥1 个有效因子即可参与打分"""
+        rows = {
+            'A': {'mom_20': 0.10},
+            'B': {'mom_20': -0.05},
+        }
+        scored = cross_section_score(rows, {'mom_20': 1.0})
+        assert len(scored) == 2
+        assert scored[0][0] == 'A'
+
+
+class TestWeightStore:
+    def test_load_defaults(self, tmp_path):
+        ws = WeightStore(str(tmp_path / 'test.db'))
+        w = ws.load()
+        assert all(w.get(f, 0) > 0 for f in FACTOR_NAMES)
+
+    def test_save_and_reload(self, tmp_path):
+        db = str(tmp_path / 'test.db')
+        ws = WeightStore(db)
+        ws.save({'mom_20': 1.5, 'trend_ma20': 0.5}, note='test')
+        w = WeightStore(db).load()
+        assert abs(w['mom_20'] - 1.5) < 0.01
+
+
+class TestReasonGeneration:
+    def test_reason_contains_numbers(self):
+        k = gen_kline(60)
+        reason, _ = build_reason('test', k)
+        assert any(c.isdigit() for c in reason)
+
+    def test_reason_not_empty(self):
+        k = gen_kline(60)
+        reason, _ = build_reason('test', k)
+        assert len(reason) > 10