"""Macro Features - FRED data, yield curve, VIX, credit spreads"""
import numpy as np
import pandas as pd
from typing import List

class MacroFeatures:
    """Macroeconomic and sentiment overlay features"""
    
    @staticmethod
    def fetch_fred_data(series_ids, start='2020-01-01', end='2024-01-01'):
        """Fetch macro data from FRED. Series: DGS10, DGS2, T10Y2Y, VIXCLS, UNRATE, BAA10YM"""
        try:
            from fredapi import Fred
            import os
            fred = Fred(api_key=os.environ.get('FRED_API_KEY', ''))
            data = {}
            for sid in series_ids:
                try:
                    data[sid] = fred.get_series(sid, observation_start=start, observation_end=end)
                except Exception as e:
                    print(f"FRED {sid}: {e}")
            return pd.DataFrame(data)
        except ImportError:
            return MacroFeatures._synthetic_macro(start, end)
    
    @staticmethod
    def _synthetic_macro(start='2020-01-01', end='2024-01-01'):
        dates = pd.bdate_range(start=start, end=end)
        n = len(dates)
        np.random.seed(42)
        level = np.cumsum(np.random.normal(0, 0.01, n)) + 2.0
        return pd.DataFrame({
            'DGS10': level + np.random.normal(0, 0.05, n),
            'DGS2': level * 0.6 + np.random.normal(0, 0.03, n),
            'T10Y2Y': level * 0.4 + np.random.normal(0, 0.02, n),
            'VIXCLS': 18 + np.random.normal(0, 3, n).clip(10, 45),
            'BAA10YM': 2.5 + np.random.normal(0, 0.2, n),
        }, index=dates)
    
    @staticmethod
    def yield_curve_features(treasury_10y, treasury_2y):
        features = pd.DataFrame(index=treasury_10y.index)
        features['yc_spread'] = treasury_10y - treasury_2y
        features['yc_slope'] = features['yc_spread'].diff(21)
        features['yc_inversion'] = (features['yc_spread'] < 0).astype(float)
        features['yc_zscore'] = (features['yc_spread'] - features['yc_spread'].rolling(252).mean()) / features['yc_spread'].rolling(252).std().replace(0, 1)
        return features
    
    @staticmethod
    def vix_features(vix):
        features = pd.DataFrame(index=vix.index)
        features['vix_level'] = vix
        features['vix_change'] = vix.pct_change()
        features['vix_zscore'] = (vix - vix.rolling(63).mean()) / vix.rolling(63).std().replace(0, 1)
        features['vix_term'] = vix.rolling(5).mean() - vix.rolling(21).mean()
        return features
    
    @staticmethod
    def credit_spread_features(spread):
        features = pd.DataFrame(index=spread.index)
        features['credit_spread'] = spread
        features['credit_change'] = spread.diff(21)
        features['credit_zscore'] = (spread - spread.rolling(252).mean()) / spread.rolling(252).std().replace(0, 1)
        return features