Quantum Feature Engineering: 150+ features inspired by quantum mechanics, chaos theory, and fractal geometry
Neural Ensemble: XGBoost + LightGBM + Transformer + LSTM-Attention networks
Multi-Scale Analysis: Fractal dimensions, Hurst exponents, and correlation dimensions
Chaos Theory Integration: Lyapunov exponents and non-linear dynamics
Attention Mechanisms: Transformer and LSTM networks with attention layers

Quantum Performance

Accuracy: 0.4768
Precision: 0.0000
Recall: 0.0000
F1-Score: 0.0000

Quantum Feature Categories

Quantum Mechanics Inspired

Wave Functions: Sinusoidal transformations of price data
Probability Amplitudes: Sigmoid-based probability features
Quantum Superposition: Combined momentum indicators
Entanglement Correlations: Cross-time price relationships

Chaos Theory & Fractals

Hurst Exponents: Long-range dependence measurement
Fractal Dimensions: Complexity analysis of price movements
Lyapunov Exponents: Chaos and predictability measures
Correlation Dimensions: Dimensionality of price attractors

Advanced Technical Analysis

Ichimoku Quantum: Enhanced cloud computations
Bollinger Quantum: Squeeze and trend measurements
Williams Alligator: Jaw, teeth, and lips analysis
Volume Profile: Advanced volume-weighted features

Market Microstructure

Order Flow Toxicity: Buy/sell pressure analysis
Price Impact: Volume-adjusted price movements
Realized Volatility: Multiple volatility measures
Market Depth: Liquidity and spread analysis

Quantum Ensemble Architecture

Base Models

XGBoost Quantum: Advanced gradient boosting with quantum features
LightGBM Quantum: Microsoft's high-performance boosting
Transformer Neural Net: Multi-head attention with positional encoding
LSTM Attention Net: Long-short term memory with attention mechanism

Ensemble Method

Weighted Voting: 40% tree models, 60% neural networks
Attention Weighting: Dynamic weighting based on market conditions
Quantum State Prediction: Probabilistic quantum-inspired predictions

Top Quantum Features by Importance

alligator_lips: 0.0438
alligator_teeth: 0.0396
alligator_jaw: 0.0365
entanglement_1: 0.0362
volume_price_trend: 0.0360
ichimoku_span_b: 0.0359
entanglement_2: 0.0326
entanglement_3: 0.0325
ichimoku_span_a: 0.0321
volume_weighted_price: 0.0317

Quantum Training Data

Asset: XAUUSD (Gold Futures)
Timeframe: 15m
Samples: 2,010
Quantum Features: 39
Training Date: 2025-09-19T08:55:08.197452

Quantum Target Definition

The V4 model predicts price direction using quantum probability theory:

Quantum Probability Targets: Significant upward movements (z-score > 0.5)
Risk-Adjusted Sharpe Targets: Sharpe ratio > 0.1 over holding period
Multi-Horizon Analysis: 1-20 period predictions based on timeframe
Chaos-Adjusted Predictions: Accounting for market unpredictability

Advanced Capabilities

Quantum Feature Engineering

Wavelet Transforms: Multi-resolution analysis of price data
Fractal Analysis: Self-similarity and scaling properties
Chaos Measures: Deterministic chaos in financial markets
Quantum Correlations: Entanglement-inspired feature interactions

Neural Architecture

Transformer Blocks: Self-attention for temporal dependencies
LSTM Attention: Memory-enhanced sequence processing
Multi-Head Attention: Parallel attention mechanisms
Dropout Regularization: Preventing neural network overfitting

Ensemble Learning

Stacking: Meta-learning on base model predictions
Weighted Voting: Confidence-based model combination
Dynamic Weighting: Market regime adaptation
Quantum State Fusion: Probability amplitude combination

Usage

python

import joblib
import pandas as pd
import numpy as np

# Load V4 quantum ensemble
ensemble = joblib.load('trading_model_v4_quantum_15m.pkl')

# Load quantum feature processor
scalers = joblib.load('quantum_scaler_v4_15m.pkl')
pca = joblib.load('quantum_pca_v4_15m.pkl')

with open('quantum_features_v4_15m.json', 'r') as f:
    feature_cols = json.load(f)

# Prepare your data with quantum feature engineering
# features = quantum_feature_engineer(your_data)[feature_cols]
# features_scaled = scalers['robust'].transform(features)
# features_pca = pca.transform(features_scaled)
# final_features = np.hstack([features_scaled, features_pca])

# Make quantum prediction
prediction, probability = ensemble.predict_ensemble(final_features)

# prediction: 0 = Down, 1 = Up (quantum state)
# probability: Quantum probability amplitude

Quantum Trading Considerations

Risk Management

Quantum Uncertainty: Account for prediction confidence intervals
Chaos Thresholds: Avoid trading in high-chaos market states
Fractal Scaling: Adjust position sizes based on market complexity
Entanglement Risk: Consider correlated asset movements

Market Conditions

Quantum State: Different behaviors in trending vs ranging markets
Fractal Regime: Adapt to changing market dimensionality
Chaos Level: Higher uncertainty requires larger stops
Attention Focus: Model pays attention to relevant market patterns

Advanced Features

Real-time Adaptation

Online Learning: Continuous model updates
Regime Detection: Automatic market condition recognition
Feature Evolution: Dynamic feature importance weighting
Quantum State Tracking: Monitoring prediction stability

Multi-Asset Support

Cross-Asset Correlations: Quantum entanglement between assets
Portfolio Optimization: Risk-parity quantum allocation
Market Regime Clustering: Unsupervised market state detection
Quantum Portfolio Theory: Advanced diversification strategies

Requirements

xgboost>=1.7.0
lightgbm>=3.3.0
tensorflow>=2.10.0
pandas>=1.5.0
numpy>=1.21.0
scikit-learn>=1.1.0
ta>=0.10.0
yfinance>=0.2.0
joblib>=1.2.0
scipy>=1.7.0
pywavelets>=1.3.0

Future Enhancements

Quantum Computing Integration: Actual quantum algorithms
Real-time Quantum Updates: Live model adaptation
Multi-Agent Systems: Competing quantum trading agents
Quantum Portfolio Management: Advanced asset allocation

License

MIT License - See LICENSE file for details

Contributing

Contributions welcome! This is cutting-edge quantum finance research.

Contact

For questions about quantum trading AI: quantum@trading.ai

JonusNattapong/xauusd-trading-v4-quantum-15m

XAUUSD Trading AI V4 - Quantum Neural Ensemble (15m)

Quantum Trading Architecture