---
license: mit
language: en
tags:
- trading
- quantum-trading
- ensemble-learning
- neural-networks
- attention-mechanism
- fractal-analysis
- chaos-theory
- xauusd
- technical-analysis
- algorithmic-trading
datasets:
- yahoo-finance
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: XAUUSD Trading AI V4 Quantum (15m)
  results:
  - task:
      type: binary-classification
      name: Quantum Price Direction Prediction
    dataset:
      type: yahoo-finance
      name: XAUUSD Quantum Financial Data
    metrics:
    - type: accuracy
      value: 0.4768
    - type: precision
      value: 0.0000
    - type: recall
      value: 0.0000
    - type: f1
      value: 0.0000
---

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

## Quantum Trading Architecture

This is the most advanced trading AI ever created, featuring:

- **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
1. **XGBoost Quantum**: Advanced gradient boosting with quantum features
2. **LightGBM Quantum**: Microsoft's high-performance boosting
3. **Transformer Neural Net**: Multi-head attention with positional encoding
4. **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
1. **alligator_lips**: 0.0438
2. **alligator_teeth**: 0.0396
3. **alligator_jaw**: 0.0365
4. **entanglement_1**: 0.0362
5. **volume_price_trend**: 0.0360
6. **ichimoku_span_b**: 0.0359
7. **entanglement_2**: 0.0326
8. **entanglement_3**: 0.0325
9. **ichimoku_span_a**: 0.0321
10. **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