#!/usr/bin/env python3

"""
Risk Analyzer for Quantum Risk Toolkit.
"""

import numpy as np
from typing import Dict, Any

class RiskAnalyzer:
    """
    Performs risk analysis (classical and quantum).
    """
    def __init__(self, quantum_executor):
        self.quantum_executor = quantum_executor

    def analyze(self, market_data, symbol: str, min_shots: int, max_shots: int, precision_target: float, adaptive_shots: bool = False) -> Dict[str, Any]:
        # Calculate classical VaR as variance of price list
        prices = [entry.get("price", 0.0) for entry in market_data]
        classical_var = float(np.var(prices)) if prices else 0.0
        # Placeholder quantum adjustment
        quantum_var = classical_var * 0.9
        # Track shot usage if adaptive shots enabled
        shot_usage: Dict[str, int] = {}
        if adaptive_shots:
            # Determine shot allocation for quantum execution and comparison
            # Assign mid-range shots for the quantum propagation step
            shots_quantum = (min_shots + max_shots) // 2
            result_quantum = self.quantum_executor.execute(None, shots=shots_quantum)
            shot_usage["quantum_execution"] = result_quantum.get("shots", shots_quantum)
            # Assign max shots for the comparison step
            shots_comparison = max_shots
            result_comparison = self.quantum_executor.execute(None, shots=shots_comparison)
            shot_usage["comparison_execution"] = result_comparison.get("shots", shots_comparison)
        return {"classical_var": classical_var, "quantum_var": quantum_var, "shot_usage": shot_usage}

