advanced_regime_engine.py

from dataclasses import dataclass
from typing import Dict, Optional, List, Tuple
import numpy as np

@dataclass
class AdvancedRegimeEngine:
    
    REGIME_PARAMETERS = {
        'calm': {
            'probability': 0.55,
            'volatility_threshold': 0.10,  # 10% annualized vol
            'correlation_threshold': 0.3,
            'risk_scaling': 1.0,
            'description': 'Normal Market Conditions'
        },
        'stress': {
            'probability': 0.30,
            'volatility_threshold': 0.20,  # 20% annualized vol
            'correlation_threshold': 0.6,
            'risk_scaling': 0.7,
            'description': 'Elevated Market Stress'
        },
        'crisis': {
            'probability': 0.15,
            'volatility_threshold': 0.35,  # 35% annualized vol
            'correlation_threshold': 0.8,
            'risk_scaling': 0.4,
            'description': 'Crisis Conditions'
        }
    }

    DETECTION_CONFIG = {
        'estimation_windows': [21, 63, 252],  # Multiple time horizons
        'minimum_history': 252,
        'decay_factor': 0.94,
        'confidence_threshold': 0.75,
        'regime_persistence': 5,  # Minimum days in regime
        'transition_smoothing': True
    }

    def __post_init__(self):
        """Initialize regime detection system"""
        self._validate_parameters()
        self._initialize_detection_matrices()
        
    def detect_regime(
        self,
        returns: np.ndarray,
        volumes: Optional[np.ndarray] = None,
        market_data: Optional[Dict] = None
    ) -> Dict[str, float]:
        """Detect current market regime and transition probabilities"""
        pass  # Implementation details available in full version

# For commercial licensing: contact@lucaskemper.com