Jorge Morais - Full Stack Developer

# quantum_elliott_ml.py
import numpy as np
from sklearn.ensemble import HistGradientBoostingClassifier
from dataclasses import dataclass

@dataclass
class QuantumIndicators:
    """Quantum Market Indicators"""
    hurst_exponent: float           # Persistence (0=mean reversion, 1=trending)
    fractal_dimension: float        # Complexity (1=simple, 2=chaotic)
    momentum_quantum: float         # Combined momentum (-1 to 1)
    volume_quantum: float           # Volume strength (0-3x)
    market_regime: str              # Current regime
    regime_confidence: float        # Confidence (0-1)

class QuantumElliottWaveML:
    """
    Quantum Elliott Wave ML System
    ML-powered Elliott Wave detection using quantum indicators
    """

    def __init__(self):
        # Specialized ML models
        self.wave_classifier = HistGradientBoostingClassifier(
            max_iter=300,
            learning_rate=0.05,
            max_depth=12,
            l2_regularization=0.1
        )
        self.entry_predictor = MLPClassifier(
            hidden_layer_sizes=(256, 128, 64),
            activation='tanh'
        )

    def extract_quantum_features(self, features: dict) -> QuantumIndicators:
        """Extract Quantum Indicators from Raw Features"""
        # Hurst Exponent (Persistence Analysis)
        hurst = features.get('regime_hurst_exponent', 0.5)

        # Fractal Dimension (Complexity Analysis)
        fractal = features.get('regime_fractal_dimension', 1.5)

        # Quantum Momentum (Multi-period combination)
        momentum_3p = features.get('momentum_3p', 0)
        momentum_5p = features.get('momentum_5p', 0)
        momentum_accel = features.get('core_momentum_accel', 0)
        momentum_quantum = (momentum_3p + momentum_5p + momentum_accel/10) / 3

        # Determine Market Regime
        market_regime = self._determine_quantum_regime(features)

        return QuantumIndicators(
            hurst_exponent=hurst,
            fractal_dimension=fractal,
            momentum_quantum=momentum_quantum,
            volume_quantum=features.get('core_volume_strength', 1.0),
            market_regime=market_regime,
            regime_confidence=0.9
        )

    def _determine_quantum_regime(self, features: dict) -> str:
        """Quantum Regime Classification"""
        hurst = features.get('regime_hurst_exponent', 0.5)
        vol_ratio = features.get('regime_vol_ratio', 1.0)
        adx = features.get('regime_adx', 25)
        trend_strength = features.get('regime_trend_strength', 0)

        # Chaotic regime
        if vol_ratio > 1.8 and adx < 20:
            return 'CHAOTIC'

        # Trending regimes
        elif hurst > 0.6 and adx > 25:
            if trend_strength > 0.02:
                return 'TRENDING_BULL'
            elif trend_strength < -0.02:
                return 'TRENDING_BEAR'

        # Mean reverting
        elif hurst < 0.4:
            return 'MEAN_REVERTING'

        return 'CONSOLIDATING'

    def analyze_wave_2_entry(self, elliott_context, quantum_data, price) -> dict:
        """
        Wave 2 Analysis - BEST OPPORTUNITY

        Wave 2 characteristics:
        - Mean reversion (Hurst < 0.45)
        - Fibonacci retracement (38.2% - 61.8%)
        - Volume declining
        - RSI oversold recovery
        """
        confidence = 0.6
        reasoning = []

        # Check Hurst (must show mean reversion)
        if quantum_data.hurst_exponent < 0.45:
            confidence += 0.2
            reasoning.append("Hurst indicates mean reversion - perfect for Wave 2")

        # Check volume
        if quantum_data.volume_quantum > 0.8:
            confidence += 0.1
            reasoning.append("Volume adequate for support")

        # Check wave progress (ideal 50-80%)
        if 0.5 < elliott_context.wave_progress < 0.8:
            confidence += 0.2
            reasoning.append(f"Wave 2 at {elliott_context.wave_progress:.1%} - ideal zone")

        # Decision
        if confidence > 0.7:
            return {
                'decision': 'BUY',
                'action_type': 'CONSERVATIVE',
                'confidence': confidence,
                'reasoning': reasoning,
                'opportunity_score': 85,
                'position_size': 4.0,  # Wave 2 allows larger position
                'stop_loss': price * 0.97,
                'take_profits': [(price * 1.08, 50.0), (price * 1.15, 50.0)]
            }
        else:
            return {'decision': 'WAIT', 'confidence': confidence}

# Quantum Portfolio Optimizer
class QuantumPortfolioOptimizer:
    """
    Quantum-inspired Portfolio Optimization
    Uses superposition and interference to explore optimal position sizes
    """

    def __init__(self, max_position_size=0.1, risk_tolerance=0.02):
        self.max_position_size = max_position_size
        self.risk_tolerance = risk_tolerance
        self.quantum_states = 16  # Superposition states

    def optimize_position_size(self, confidence, risk_score, volatility,
                                elliott_wave_stage, fib_confluence, mtf_alignment):
        """Quantum Position Sizing Optimization"""
        # Create quantum superposition of solutions
        quantum_solutions = self._create_quantum_superposition(
            confidence, risk_score, volatility,
            elliott_wave_stage, fib_confluence, mtf_alignment
        )

        # Apply quantum interference (amplify high-Sharpe solutions)
        optimized_solutions = self._apply_quantum_interference(quantum_solutions)

        # Quantum measurement (collapse to final solution)
        final_position = self._quantum_measurement(optimized_solutions)

        return final_position

    def _create_quantum_superposition(self, confidence, risk_score, volatility,
                                       elliott_stage, fib_confluence, mtf_alignment):
        """Create superposition of different position sizes"""
        solutions = []

        for i in range(self.quantum_states):
            # Quantum amplitude (probability)
            amplitude = np.sin(i * np.pi / self.quantum_states) ** 2
            base_size = amplitude * self.max_position_size

            # Elliott Wave multiplier
            elliott_multiplier = {
                'wave_2': 1.2,  # Best entry
                'wave_3': 1.5,  # Strongest wave
                'wave_5': 0.7   # Dangerous
            }.get(elliott_stage, 1.0)

            # Fibonacci bonus + Multi-timeframe factor
            fib_bonus = min(fib_confluence * 0.1, 0.3)
            mtf_factor = 0.5 + (mtf_alignment - 0.5) * 0.8

            adjusted_size = base_size * elliott_multiplier * (1 + fib_bonus) * mtf_factor

            # Calculate Sharpe estimate
            expected_return = confidence * 0.05
            expected_risk = risk_score * volatility * 0.1
            sharpe = expected_return / max(expected_risk, 0.01)

            solutions.append({
                'size': adjusted_size,
                'amplitude': amplitude,
                'sharpe': sharpe,
                'confidence': confidence
            })

        return solutions

    def _apply_quantum_interference(self, solutions):
        """Apply constructive interference to amplify good solutions"""
        for solution in solutions:
            # Quantum energy
            energy = (
                solution['sharpe'] * 0.4 +
                solution['confidence'] * 0.3
            )
            solution['quantum_energy'] = energy

        # Interference boost
        max_energy = max(s['quantum_energy'] for s in solutions)
        for solution in solutions:
            boost = (solution['quantum_energy'] / max_energy) ** 0.8
            solution['amplitude'] *= boost

        return solutions