feat(strategy): add Volume Profile HVN/LVN and Combined adaptive weighting

1 files changed, 57 insertions, 0 deletions

diff --git a/services/strategy-engine/tests/test_combined_strategy.py b/services/strategy-engine/tests/test_combined_strategy.py
index 3408a89..20a572e 100644
--- a/services/strategy-engine/tests/test_combined_strategy.py
+++ b/services/strategy-engine/tests/test_combined_strategy.py
@@ -167,3 +167,60 @@ def test_combined_invalid_weight():
     c.configure({})
     with pytest.raises(ValueError):
         c.add_strategy(AlwaysBuyStrategy(), weight=-1.0)
+
+
+def test_combined_record_result():
+    """Verify trade history tracking works correctly."""
+    c = CombinedStrategy()
+    c.configure({"adaptive_weights": True, "history_window": 5})
+
+    c.record_result("test_strat", True)
+    c.record_result("test_strat", False)
+    c.record_result("test_strat", True)
+
+    assert len(c._trade_history["test_strat"]) == 3
+    assert c._trade_history["test_strat"] == [True, False, True]
+
+    # Fill beyond window size to test trimming
+    for _ in range(5):
+        c.record_result("test_strat", False)
+
+    assert len(c._trade_history["test_strat"]) == 5  # Trimmed to history_window
+
+
+def test_combined_adaptive_weight_increases_for_winners():
+    """Strategy with high win rate gets higher effective weight."""
+    c = CombinedStrategy()
+    c.configure({"threshold": 0.3, "adaptive_weights": True, "history_window": 20})
+    c.add_strategy(AlwaysBuyStrategy(), weight=1.0)
+
+    # Record high win rate for always_buy (80% wins)
+    for _ in range(8):
+        c.record_result("always_buy", True)
+    for _ in range(2):
+        c.record_result("always_buy", False)
+
+    # Adaptive weight should be > base weight (1.0)
+    adaptive_w = c._get_adaptive_weight("always_buy", 1.0)
+    assert adaptive_w > 1.0
+    # 80% win rate -> scale = 0.5 + 0.8 = 1.3 -> weight = 1.3
+    assert abs(adaptive_w - 1.3) < 0.01
+
+
+def test_combined_adaptive_weight_decreases_for_losers():
+    """Strategy with low win rate gets lower effective weight."""
+    c = CombinedStrategy()
+    c.configure({"threshold": 0.3, "adaptive_weights": True, "history_window": 20})
+    c.add_strategy(AlwaysBuyStrategy(), weight=1.0)
+
+    # Record low win rate for always_buy (20% wins)
+    for _ in range(2):
+        c.record_result("always_buy", True)
+    for _ in range(8):
+        c.record_result("always_buy", False)
+
+    # Adaptive weight should be < base weight (1.0)
+    adaptive_w = c._get_adaptive_weight("always_buy", 1.0)
+    assert adaptive_w < 1.0
+    # 20% win rate -> scale = 0.5 + 0.2 = 0.7 -> weight = 0.7
+    assert abs(adaptive_w - 0.7) < 0.01