Starter Code

MidasHMM/hmm/midas/feature_engineer.py

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+# midas/feature_engineer.py
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import RobustScaler
+from typing import Dict
+
+class FeatureEngineer:
+    def __init__(self, config: Dict):
+        self.config = config
+        self.scaler = RobustScaler()  # Handles outliers
+        
+    def _calculate_obv(self, df: pd.DataFrame) -> pd.Series:
+        """On-Balance Volume"""
+        obv = (np.sign(df['close'].diff()) * df['volume']).fillna(0).cumsum()
+        return obv.pct_change(periods=14)  # Normalized OBV
+
+    def calculate_features(self, data: Dict[str, pd.DataFrame]) -> pd.DataFrame:
+        all_features = []
+        
+        for ticker, df in data.items():
+            features = pd.DataFrame(index=df.index)
+            
+            # Price-based features
+            if 'returns' in self.config['features']:
+                features['returns'] = np.log(df['close']).diff()
+                
+            if 'volatility' in self.config['features']:
+                features['volatility'] = features['returns'].rolling(20).std() * np.sqrt(252)
+                
+            if 'rsi' in self.config['features']:
+                delta = df['close'].diff()
+                gain = delta.where(delta > 0, 0).rolling(14).mean()
+                loss = -delta.where(delta < 0, 0).rolling(14).mean()
+                features['rsi'] = 100 - (100 / (1 + (gain / loss)))
+                
+            if 'macd' in self.config['features']:
+                ema12 = df['close'].ewm(span=12, adjust=False).mean()
+                ema26 = df['close'].ewm(span=26, adjust=False).mean()
+                features['macd'] = ema12 - ema26
+                
+            if 'atr' in self.config['features']:
+                high_low = df['high'] - df['low']
+                high_close = (df['high'] - df['close'].shift()).abs()
+                low_close = (df['low'] - df['close'].shift()).abs()
+                tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
+                features['atr'] = tr.rolling(14).mean() / df['close']
+                
+            if 'volume_change' in self.config['features']:
+                features['volume_change'] = np.log(df['volume'] / df['volume'].shift(1))
+                
+            if 'obv' in self.config['features']:
+                features['obv'] = self._calculate_obv(df)
+                
+            # Add ticker identifier if combining
+            if self.config['combine_tickers']:
+                features['ticker'] = ticker
+                
+            all_features.append(features.dropna())
+            
+        combined = pd.concat(all_features).sort_index()
+        
+        # Encode tickers if combining
+        if self.config['combine_tickers']:
+            combined = pd.get_dummies(combined, columns=['ticker'], prefix='', prefix_sep='')
+            
+        # Scale features
+        scaled = pd.DataFrame(
+            self.scaler.fit_transform(combined),
+            index=combined.index,
+            columns=combined.columns
+        )
+        return scaled