advanced_risk_engine.py

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

@dataclass
class AdvancedRiskEngine:
    
    # Core Risk Parameters
    RISK_LIMITS = {
        'portfolio': {
            'max_drawdown': 0.15,          # 15% maximum portfolio drawdown
            'var_limit': 0.12,             # 12% Value at Risk limit
            'expected_shortfall': 0.20,     # 20% Expected Shortfall threshold
            'concentration_limit': 0.25     # 25% maximum sector concentration
        },
        'volatility': {
            'target': 0.12,                # 12% annualized volatility target
            'bands': (0.08, 0.15),         # Acceptable volatility range
            'scaling_limits': (0.5, 2.0),   # Position scaling bounds
            'regime_adjustments': {
                'low_vol': 1.2,            # Scale up in low vol
                'high_vol': 0.8,           # Scale down in high vol
                'crisis': 0.5              # Significant de-risking in crisis
            }
        },
        'position': {
            'max_single_name': 0.05,       # 5% single position limit
            'min_single_name': 0.01,       # 1% minimum position size
            'max_sector': 0.25,            # 25% sector limit
            'liquidity_threshold': 0.15    # 15% of ADV maximum
        }
    }

    # Dynamic Risk Adjustment Factors
    REGIME_PARAMETERS = {
        'lookback_window': 63,             # 3M lookback for regime detection
        'vol_threshold': {
            'low': 0.10,                   # Low volatility threshold
            'high': 0.20                   # High volatility threshold
        },
        'correlation_threshold': {
            'normal': 0.60,                # Normal market correlations
            'stress': 0.85                 # Stress correlation level
        },
        'momentum_signals': {
            'short_window': 21,            # 1M momentum window
            'long_window': 252,            # 12M momentum window
            'signal_threshold': 1.5        # Signal strength threshold
        }
    }

    # Risk Monitoring Thresholds
    MONITORING_CONFIG = {
        'intraday_alerts': {
            'drawdown': 0.02,              # 2% intraday drawdown alert
            'volatility': 1.5,             # 50% vol increase alert
            'volume': 2.0                  # 2x normal volume alert
        },
        'position_alerts': {
            'concentration': 0.8,          # Alert at 80% of limit
            'var_utilization': 0.9,        # Alert at 90% of VaR limit
            'correlation': 0.75            # Correlation break alert
        },
        'market_condition_alerts': {
            'spread_widening': 2.0,        # 2x normal spread alert
            'liquidity_reduction': 0.5,    # 50% liquidity drop alert
            'momentum_reversal': 2.0       # 2-sigma momentum shift
        }
    }

    def __post_init__(self):
        """Initialize risk monitoring systems"""
        self._validate_configuration()
        self._initialize_risk_matrices()
        self._setup_monitoring_system()
    
    def _validate_configuration(self) -> None:
        """Validate all risk parameters"""
        pass  # Implementation details hidden
    
    def _initialize_risk_matrices(self) -> None:
        """Initialize correlation and covariance matrices"""
        pass  # Implementation details hidden
    
    def _setup_monitoring_system(self) -> None:
        """Setup real-time risk monitoring"""
        pass  # Implementation details hidden

    def calculate_position_adjustments(self) -> Dict[str, float]:
        """Calculate required position adjustments"""
        pass  # Implementation details hidden

    def get_risk_report(self) -> Dict[str, Dict[str, float]]:
        """Generate comprehensive risk report"""
        pass  # Implementation details hidden

# Note: For implementation details and licensing, contact: contact@lucaskemper.com