"""Dynamic Hedging System using Options Model."""
import numpy as np
import pandas as pd
from typing import Dict, Optional
import warnings
warnings.filterwarnings('ignore')


class DynamicHedgingEngine:
    """Dynamic hedging using options model integration."""
    
    def __init__(self, 
                 max_hedge_ratio: float = 0.5,
                 hedge_cost: float = 0.002,
                 delta_threshold: float = 0.1):
        self.max_hedge_ratio = max_hedge_ratio
        self.hedge_cost = hedge_cost
        self.delta_threshold = delta_threshold
        self.hedge_history = []
        
    def compute_hedge_ratio(self, 
                            portfolio_delta: float,
                            portfolio_gamma: float,
                            volatility: float,
                            risk_aversion: float = 2.0) -> float:
        """
        Compute optimal hedge ratio.
        
        Args:
            portfolio_delta: Current delta exposure
            portfolio_gamma: Current gamma exposure
            volatility: Current volatility
            risk_aversion: Risk aversion parameter
        
        Returns:
            Hedge ratio (0 to max_hedge_ratio)
        """
        # Base hedge from delta
        delta_hedge = abs(portfolio_delta) if portfolio_delta > self.delta_threshold else 0
        
        # Gamma adjustment (more hedging when gamma is negative/high)
        gamma_hedge = abs(portfolio_gamma) * volatility * 0.5 if portfolio_gamma > 0 else 0
        
        # Combine with risk aversion
        raw_hedge = (delta_hedge + gamma_hedge) * risk_aversion / 2.0
        
        # Cap at max
        hedge_ratio = np.clip(raw_hedge, 0, self.max_hedge_ratio)
        
        self.hedge_history.append({
            'hedge_ratio': hedge_ratio,
            'delta_hedge': delta_hedge,
            'gamma_hedge': gamma_hedge
        })
        
        return hedge_ratio
    
    def compute_portfolio_greeks(self, 
                                  positions: Dict[str, float],
                                  option_greeks: pd.DataFrame) -> Dict[str, float]:
        """Compute portfolio-level Greeks."""
        total_delta = 0
        total_gamma = 0
        total_theta = 0
        total_vega = 0
        
        for ticker, position in positions.items():
            if ticker in option_greeks.index:
                row = option_greeks.loc[ticker]
                total_delta += row.get('delta', 0) * position
                total_gamma += row.get('gamma', 0) * position
                total_theta += row.get('theta', 0) * position
                total_vega += row.get('vega', 0) * position
            else:
                # Equity position: delta = 1 per share
                total_delta += position
        
        return {
            'delta': total_delta,
            'gamma': total_gamma,
            'theta': total_theta,
            'vega': total_vega
        }
    
    def compute_delta_neutral_weights(self,
                                       portfolio_greeks: Dict[str, float],
                                       hedge_instruments: Dict[str, float]) -> Dict[str, float]:
        """Compute delta-neutral hedge weights."""
        total_delta = portfolio_greeks['delta']
        
        if abs(total_delta) < 0.01:
            return {k: 0 for k in hedge_instruments}
        
        # Distribute hedge proportionally
        hedge_weights = {}
        total_hedge_delta = sum(abs(v) for v in hedge_instruments.values())
        
        for instrument, delta in hedge_instruments.items():
            if total_hedge_delta > 0:
                hedge_weights[instrument] = -total_delta * abs(delta) / total_hedge_delta
            else:
                hedge_weights[instrument] = 0
        
        return hedge_weights
    
    def get_hedge_performance(self) -> Dict:
        """Get hedging performance statistics."""
        if not self.hedge_history:
            return {}
        
        hedge_ratios = [h['hedge_ratio'] for h in self.hedge_history]
        
        return {
            'avg_hedge_ratio': np.mean(hedge_ratios),
            'max_hedge_ratio': np.max(hedge_ratios),
            'hedge_utilization': np.mean(hedge_ratios) / self.max_hedge_ratio,
            'n_rebalances': len(self.hedge_history)
        }