test: Owner Earnings + fraud detection + ST risk

Adds 13 tests covering:
- Owner Earnings: basic calc, negative earnings
- Maintenance capex: below/above depreciation
- Moat score: wide moat, no moat
- FraudDetector: empty/healthy/audit fail/CFO negative
- ST risk: empty/consecutive losses/healthy

1205 passed (1192 old + 13 new)

Author: sijie-Z

tests/test_risk/test_financial_health.py

@@ -0,0 +1,135 @@
+"""Tests for financial health checks (fraud, ST risk, owner earnings)."""
+
+import pandas as pd
+import pytest
+
+from quant_platform.risk.financial_health import (
+    FraudDetector,
+    FraudReport,
+    assess_st_risk,
+    owner_earnings,
+    estimate_maintenance_capex,
+    moat_score,
+)
+
+
+class TestOwnerEarnings:
+    def test_basic_calculation(self):
+        result = owner_earnings(
+            net_income=1000,
+            depreciation=200,
+            maintenance_capex=150,
+        )
+        assert result["owner_earnings"] == 1050  # 1000 + 200 - 150
+        assert result["earnings_quality"] == "high"
+
+    def test_negative_owner_earnings(self):
+        result = owner_earnings(
+            net_income=100,
+            depreciation=50,
+            maintenance_capex=300,
+        )
+        assert result["owner_earnings"] < 0
+        assert result["earnings_quality"] == "low"
+
+
+class TestEstimateMaintenanceCapex:
+    def test_capex_less_than_depreciation(self):
+        result = estimate_maintenance_capex(total_capex=100, depreciation=150)
+        assert result == 100  # All capex is maintenance
+
+    def test_capex_exceeds_depreciation(self):
+        result = estimate_maintenance_capex(total_capex=200, depreciation=100)
+        # 100 + (200 - 100) * 0.3 = 130
+        assert result == pytest.approx(130.0)
+
+
+class TestMoatScore:
+    def test_wide_moat(self):
+        result = moat_score(
+            gross_margin_stability=0.01,
+            roe_avg=0.25,
+            debt_equity=0.1,
+            pricing_power=True,
+        )
+        assert result["score"] >= 8
+        assert "宽" in result["label"]
+
+    def test_no_moat(self):
+        result = moat_score(
+            gross_margin_stability=0.15,
+            roe_avg=0.08,
+            debt_equity=1.5,
+            pricing_power=False,
+        )
+        assert result["score"] < 4
+
+
+class TestFraudDetector:
+    def test_empty_df(self):
+        detector = FraudDetector()
+        report = detector.analyze(pd.DataFrame())
+        assert isinstance(report, FraudReport)
+        assert report.total_score == 0
+
+    def test_healthy_company(self):
+        df = pd.DataFrame({
+            "gross_margin": [0.40, 0.41, 0.42],
+            "cfo": [100, 110, 120],
+            "net_income": [100, 110, 120],
+            "revenue": [1000, 1100, 1200],
+            "receivables": [200, 205, 210],
+            "goodwill": [10, 10, 10],
+            "net_assets": [500, 550, 600],
+        })
+        detector = FraudDetector()
+        report = detector.analyze(df)
+        assert report.risk_level == "低风险"
+
+    def test_audit_fail_direct_exclude(self):
+        df = pd.DataFrame({
+            "audit_opinion": ["无法表示意见"],
+            "gross_margin": [0.35],
+        })
+        detector = FraudDetector()
+        report = detector.analyze(df)
+        assert report.risk_level == "直接排除"
+
+    def test_cfo_negative_flag(self):
+        df = pd.DataFrame({
+            "cfo": [-100, -110, -120],
+            "net_income": [50, 55, 60],
+            "gross_margin": [0.35, 0.36, 0.37],
+            "revenue": [1000, 1100, 1200],
+            "receivables": [200, 210, 220],
+            "goodwill": [10, 10, 10],
+            "net_assets": [500, 550, 600],
+        })
+        detector = FraudDetector()
+        report = detector.analyze(df)
+        assert report.total_score > 0
+        assert "中风险" in report.risk_level or "高风险" in report.risk_level
+
+
+class TestSTRisk:
+    def test_empty_df(self):
+        report = assess_st_risk(pd.DataFrame())
+        assert report.total_score == 0
+
+    def test_consecutive_losses(self):
+        df = pd.DataFrame({
+            "net_income": [-100, -200, -300],
+            "revenue": [1e9, 1e9, 1e9],
+            "net_assets": [500, 300, 100],
+        })
+        report = assess_st_risk(df)
+        assert report.total_score >= 4
+
+    def test_healthy(self):
+        df = pd.DataFrame({
+            "net_income": [100, 200, 300],
+            "revenue": [1e9, 2e9, 3e9],
+            "net_assets": [1000, 1100, 1200],
+        })
+        report = assess_st_risk(df)
+        assert report.risk_level == "低风险"