Align HK combo dynamic backtest semantics

scripts/research_hk_equity_combo_backtest.py

@@ -58,8 +58,8 @@
 
 DYNAMIC_WEIGHT_MAP: dict[str, float] = {
     "risk_on": 0.40,
-    "soft_defense": 0.20,
-    "hard_defense": 0.00,
+    "soft_defense": 0.49,
+    "hard_defense": 0.70,
 }
 
 # Dividend-style simulation parameters applied to the ETF-eligible universe.
@@ -69,11 +69,6 @@
 DIVIDEND_SYMBOL = "03110"  # High-dividend proxy from the ETF universe
 DIVIDEND_ANNUAL_VOL_SCALE = 0.85  # Dividend stocks are ~15 % less volatile
 
-# Period definitions for the regime signal (uses the same date range as the
-# ETF backtest config by default).
-BREADTH_REGIME_WINDOW_DAYS = 63
-
-
 # ---------------------------------------------------------------------------
 # Dividend-leg simulation helpers
 # ---------------------------------------------------------------------------
@@ -122,9 +117,8 @@ def _breadth_regime(close: pd.DataFrame, as_of: pd.Timestamp) -> str:
       - soft_defense  : 0.30 <= breadth < 0.45
       - hard_defense  : breadth <  0.30
     """
-    window_start = close.index[close.index.searchsorted(as_of - pd.Timedelta(days=BREADTH_REGIME_WINDOW_DAYS))]
-    window = close.loc[window_start:as_of]
-    if window.empty:
+    window = close.loc[:as_of]
+    if len(window) < 100:
         return "risk_on"
 
     sma200 = window.rolling(200, min_periods=100).mean()
@@ -139,6 +133,17 @@ def _breadth_regime(close: pd.DataFrame, as_of: pd.Timestamp) -> str:
     return "risk_on"
 
 
+def _dynamic_leg_weights(combo: "ComboConfig", regime: str) -> tuple[float, float]:
+    """Mirror production combo regime semantics for ETF/dividend weights."""
+    if regime == "soft_defense":
+        etf_target_weight = min(combo.etf_weight * 0.85, 1.0)
+    elif regime == "hard_defense":
+        etf_target_weight = min(combo.etf_weight * 0.50, 1.0)
+    else:
+        etf_target_weight = combo.etf_weight
+    return etf_target_weight, 1.0 - etf_target_weight
+
+
 # ---------------------------------------------------------------------------
 # Combo backtest runner
 # ---------------------------------------------------------------------------
@@ -200,8 +205,7 @@ def _combo_strategy_returns(
             regime = "static"
         else:
             regime = _breadth_regime(close, as_of)
-            div_target_weight = DYNAMIC_WEIGHT_MAP.get(regime, 0.0)
-            etf_target_weight = 1.0 - div_target_weight
+            etf_target_weight, div_target_weight = _dynamic_leg_weights(combo, regime)
 
         regime_history.append({"date": as_of, "regime": regime, "div_weight": div_target_weight})
 

tests/test_hk_equity_combo_backtest.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+SCRIPTS_DIR = Path(__file__).resolve().parents[1] / "scripts"
+if str(SCRIPTS_DIR) not in sys.path:
+    sys.path.insert(0, str(SCRIPTS_DIR))
+
+from research_hk_equity_combo_backtest import (  # noqa: E402
+    ComboConfig,
+    _breadth_regime,
+    _dynamic_leg_weights,
+)
+
+
+def _close_frame(rate: float, *, periods: int = 260) -> pd.DataFrame:
+    dates = pd.bdate_range("2024-01-02", periods=periods)
+    data = {}
+    for idx, symbol in enumerate(("02800", "03110", "03188", "03033")):
+        start = 20.0 + idx
+        data[symbol] = [start * (rate**step) for step in range(periods)]
+    return pd.DataFrame(data, index=dates)
+
+
+def test_breadth_regime_uses_enough_history_for_risk_on():
+    close = _close_frame(1.002)
+
+    assert _breadth_regime(close, close.index[-1]) == "risk_on"
+
+
+def test_breadth_regime_detects_hard_defense():
+    close = _close_frame(0.998)
+
+    assert _breadth_regime(close, close.index[-1]) == "hard_defense"
+
+
+def test_dynamic_leg_weights_match_production_combo_semantics():
+    combo = ComboConfig(etf_weight=0.60, dividend_weight=0.40)
+
+    assert _dynamic_leg_weights(combo, "risk_on") == pytest.approx((0.60, 0.40))
+    assert _dynamic_leg_weights(combo, "soft_defense") == pytest.approx((0.51, 0.49))
+    assert _dynamic_leg_weights(combo, "hard_defense") == pytest.approx((0.30, 0.70))