Upload alpha_factory/deterministic/regime_tagger.py with huggingface_hub

alpha_factory/deterministic/regime_tagger.py

@@ -7,7 +7,6 @@ from dataclasses import dataclass
 
 @dataclass
 class RegimeProfile:
-    """Regime classification for a single year."""
     year: int
     vol_regime: str       # "low" (<15 VIX), "mid" (15-25), "high" (>25)
     trend_regime: str     # "bull" (SPY 12-1 mom > 0), "bear" (< 0)
@@ -15,8 +14,6 @@ class RegimeProfile:
     style_regime: str     # "value" or "growth" leadership
 
 
-# Historical regime data (2019-2024) — hardcoded from public market data
-# In production, compute from VIX/SPY/10Y/Russell indices
 HISTORICAL_REGIMES = {
     2019: RegimeProfile(2019, "low", "bull", "flattening", "growth"),
     2020: RegimeProfile(2020, "high", "bull", "flattening", "growth"),
@@ -28,15 +25,10 @@ HISTORICAL_REGIMES = {
 
 
 def get_regime(year: int) -> RegimeProfile:
-    """Get regime profile for a year."""
     return HISTORICAL_REGIMES.get(year, RegimeProfile(year, "mid", "bull", "flattening", "growth"))
 
 
 def tag_yearly_regimes(yearly_sharpe: list[float], start_year: int = 2019) -> list[dict]:
-    """
-    Tag each year's Sharpe with its regime context.
-    Returns list of {year, sharpe, vol_regime, trend_regime, ...}
-    """
     tagged = []
     for i, sharpe in enumerate(yearly_sharpe):
         year = start_year + i
@@ -53,13 +45,7 @@ def tag_yearly_regimes(yearly_sharpe: list[float], start_year: int = 2019) -> li
 
 
 def detect_regime_dependency(yearly_sharpe: list[float], start_year: int = 2019) -> dict:
-    """
-    Detect if alpha performance is regime-dependent.
-    Returns analysis of which regimes it works/fails in.
-    """
     tagged = tag_yearly_regimes(yearly_sharpe, start_year)
-
-    # Group by regimes
     vol_performance = {"low": [], "mid": [], "high": []}
     trend_performance = {"bull": [], "bear": []}
     style_performance = {"value": [], "growth": []}
@@ -78,12 +64,10 @@ def detect_regime_dependency(yearly_sharpe: list[float], start_year: int = 2019)
         "style_sensitivity": {k: round(avg(v), 3) for k, v in style_performance.items() if v},
     }
 
-    # Detect dependency: if performance differs > 1.0 Sharpe between regimes
     max_vol_diff = max(vol_performance.values(), key=avg, default=[0])
     min_vol_diff = min(vol_performance.values(), key=avg, default=[0])
     analysis["regime_dependent"] = (avg(max_vol_diff) - avg(min_vol_diff)) > 1.0
 
-    # Best/worst regime
     all_regime_sharpes = [(f"{k}_{regime}", avg(v))
                           for category in [vol_performance, trend_performance, style_performance]
                           for regime, sharpes in category.items()