JonusNattapong/xauusd-trading-v4-quantum-daily

XAUUSD Trading AI V4 - Quantum Neural Ensemble (daily)

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: 0.6424
• Precision: 0.5882
• Recall: 0.0901
• F1-Score: 0.1562

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. bb_trend: 0.0319
2. momentum_superposition: 0.0315
3. fractal_dimension: 0.0310
4. volume_weighted_price: 0.0308
5. wavelet_variance: 0.0304
6. returns: 0.0303
7. stoch_rsi: 0.0302
8. quantum_correlation_2: 0.0301
9. price_impact: 0.0299
10. log_returns: 0.0299

Quantum Training Data

• Asset: XAUUSD (Gold Futures)
• Timeframe: daily
• Samples: 2,010
• Quantum Features: 39
• Training Date: 2025-09-19T08:51:10.460110

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

import joblib
import pandas as pd
import numpy as np

# Load V4 quantum ensemble
ensemble = joblib.load('trading_model_v4_quantum_daily.pkl')

# Load quantum feature processor
scalers = joblib.load('quantum_scaler_v4_daily.pkl')
pca = joblib.load('quantum_pca_v4_daily.pkl')

with open('quantum_features_v4_daily.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

Full model card — loading the full ensemble (trees + Keras)

This repository contains both the tree-only safe artifacts (pickles) and the neural network artifacts saved as native Keras models.

Recommended TensorFlow: 2.20.x (or the TF version used when training). If you encounter load errors for the .keras files, try matching the exact TF/Keras version used during training.

Loading example (Python):

import joblib
import json
import numpy as np
from inference_v4 import V4Predictor

# Load tree-only (optional)
trees = joblib.load('trading_model_v4_quantum_daily.pkl')

# Use the combined predictor which will attempt to load the Keras artifacts
# Make sure you have tensorflow installed in the same environment
pred = V4Predictor('daily', use_keras=True, weights={'trees':0.6,'neural':0.4})

# Prepare final features using the provided quantum feature pipeline
# (See quantum_features_v4_daily.json and the scalers/pca pickles)

# X: numpy array shape (n_samples, n_features)
proba = pred.predict_proba(X)

Notes:

• The V4Predictor will look for a folder named models_v4_fresh/trading_model_v4_quantum_daily_keras/ locally. If present it will attempt to load transformer.keras and lstm_attention.keras.
• If your environment cannot load Keras models, the predictor will fall back to tree-only probabilities.
• Large files are stored with Git LFS on Hugging Face; ensure you have git-lfs configured when cloning.

If you want a one-shot example to reproduce the integrated backtest locally, see run_backtest_with_nn.py in the repository root.

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