feat: add Bollinger Bands factor

Author: Satvik-Singh192

.coverage

@@ -1,15 +1,15 @@
 """Factors module public API."""
 
 from .base import Factor
+from .bollinger import BollingerBandsFactor
 from .momentum import CrossSectionalMomentum, MomentumFactor
 from .size import SizeFactor
 from .value import ValueFactor
 from .volatility import IdiosyncraticVolatility, VolatilityFactor
-from .bollinger import BollingerBandsFactor
-
 
 __all__ = [
     "Factor",
+    "BollingerBandsFactor",
     "MomentumFactor",
     "CrossSectionalMomentum",
     "ValueFactor",

src/quant_research_starter/factors/__init__.py

@@ -1,4 +1,5 @@
 import pandas as pd
+
 from .base import Factor
 
 
@@ -8,7 +9,9 @@ class BollingerBandsFactor(Factor):
     z = (price - rolling_mean) / rolling_std
     """
 
-    def __init__(self, name: str = "bollinger_bands", lookback: int = 20, num_std: float = 2.0):
+    def __init__(
+        self, name: str = "bollinger_bands", lookback: int = 20, num_std: float = 2.0
+    ):
         super().__init__(name=name, lookback=lookback)
         self.num_std = num_std
 
@@ -26,4 +29,4 @@ def compute(self, prices: pd.DataFrame) -> pd.DataFrame:
         # Save results
         self._values = zscore
 
-        return zscore
+        return zscore

src/quant_research_starter/factors/bollinger.py

@@ -5,11 +5,11 @@
 import pytest
 
 from quant_research_starter.factors import (
+    BollingerBandsFactor,
     MomentumFactor,
     SizeFactor,
     ValueFactor,
     VolatilityFactor,
-    BollingerBandsFactor,
 )
 
 
@@ -197,6 +197,7 @@ def test_volatility_calculation(self):
             spearman_corr < -0.5
         ), f"volatility factor should be negatively correlated with realized volatility (spearman={spearman_corr})"
 
+
 class TestBollingerBandsFactor:
     """Test Bollinger Bands factor calculations."""
 
@@ -211,7 +212,7 @@ def test_bollinger_basic(self, sample_prices):
         assert set(result.columns) == set(sample_prices.columns)
 
         # Values should be finite where enough data exists
-        valid_values = result.iloc[factor.lookback:].values.flatten()
+        valid_values = result.iloc[factor.lookback :].values.flatten()
         assert np.all(np.isfinite(valid_values)), "NaNs found after lookback period"
 
         # Mean of z-scores should be roughly centered near 0