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
- 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
- bb_trend: 0.0319
- momentum_superposition: 0.0315
- fractal_dimension: 0.0310
- volume_weighted_price: 0.0308
- wavelet_variance: 0.0304
- returns: 0.0303
- stoch_rsi: 0.0302
- quantum_correlation_2: 0.0301
- price_impact: 0.0299
- 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
V4Predictorwill look for a folder namedmodels_v4_fresh/trading_model_v4_quantum_daily_keras/locally. If present it will attempt to loadtransformer.kerasandlstm_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-lfsconfigured 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