test: add strategy and integration tests

Add tests for trend rider (integration, robustness, v4), US combo sweep, and US fundamentals modules.
2026-05-14 12:53:26 +08:00
parent 541f7bcf5b
commit 24663ebd35
5 changed files with 302 additions and 0 deletions
--- a/tests/test_trend_rider_robustness.py
+++ b/tests/test_trend_rider_robustness.py
@@ -0,0 +1,91 @@
+import unittest
+
+import numpy as np
+import pandas as pd
+
+from research import trend_rider_robustness as robustness
+
+
+class TrendRiderRobustnessTests(unittest.TestCase):
+    def test_evaluate_weights_reports_core_risk_metrics(self):
+        dates = pd.bdate_range("2024-01-01", periods=6)
+        prices = pd.DataFrame(
+            {
+                "AAA": [100, 110, 105, 120, 118, 130],
+                "BBB": [50, 49, 51, 50, 52, 53],
+            },
+            index=dates,
+        )
+        weights = pd.DataFrame(
+            {
+                "AAA": [0, 1, 1, 0, 0, 1],
+                "BBB": [0, 0, 0, 1, 1, 0],
+            },
+            index=dates,
+        )
+
+        result = robustness.evaluate_weights("synthetic", weights, prices, transaction_cost=0.001)
+
+        self.assertEqual(result.name, "synthetic")
+        self.assertGreater(result.final_multiple, 1.0)
+        self.assertLessEqual(result.max_drawdown, 0.0)
+        self.assertGreater(result.switches, 0)
+        self.assertGreater(result.avg_daily_turnover, 0.0)
+
+    def test_parameter_sweep_returns_rankable_rows(self):
+        dates = pd.bdate_range("2023-01-02", periods=320)
+        trend = np.linspace(100, 180, len(dates))
+        prices = pd.DataFrame(
+            {
+                "SPY": trend,
+                "TQQQ": trend * 1.5,
+                "UPRO": trend * 1.4,
+                "GLD": np.linspace(100, 105, len(dates)),
+                "DBC": np.linspace(90, 95, len(dates)),
+            },
+            index=dates,
+        )
+
+        sweep = robustness.parameter_sweep(
+            prices,
+            variants=[
+                {"vol_enter": 0.14, "dd_stop": 0.05, "peak_enter": 0.02, "mom_lookback": 63},
+                {"vol_enter": 0.16, "dd_stop": 0.07, "peak_enter": 0.03, "mom_lookback": 84},
+            ],
+            start="2023-01-02",
+        )
+
+        self.assertEqual(len(sweep), 2)
+        self.assertIn("cagr", sweep.columns)
+        self.assertIn("max_drawdown", sweep.columns)
+        self.assertTrue(sweep["cagr"].notna().all())
+
+    def test_candidate_weights_include_v4_and_market_benchmarks(self):
+        dates = pd.bdate_range("2023-01-02", periods=320)
+        trend = np.linspace(100, 180, len(dates))
+        prices = pd.DataFrame(
+            {
+                "SPY": trend,
+                "QQQ": trend * 1.1,
+                "SSO": trend * 1.5,
+                "QLD": trend * 1.6,
+                "UPRO": trend * 2.0,
+                "TQQQ": trend * 2.2,
+                "SHY": np.linspace(100, 103, len(dates)),
+                "IEF": np.linspace(100, 104, len(dates)),
+                "TLT": np.linspace(100, 105, len(dates)),
+                "GLD": np.linspace(100, 115, len(dates)),
+                "DBC": np.linspace(90, 105, len(dates)),
+            },
+            index=dates,
+        )
+
+        candidates = robustness.candidate_weights(prices)
+
+        self.assertIn("TrendRiderV4", candidates)
+        self.assertIn("SPY Buy&Hold", candidates)
+        self.assertIn("QQQ Buy&Hold", candidates)
+
+
+if __name__ == "__main__":
+    unittest.main()