README.md

---

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 (daily)
  results:
  - task:
      type: binary-classification
      name: Quantum Price Direction Prediction
    dataset:
      type: yahoo-finance
      name: XAUUSD Quantum Financial Data
    metrics:
    - type: accuracy
      value: 0.6424
    - type: precision
      value: 0.5882
    - type: recall
      value: 0.0901
    - type: f1
      value: 0.1562
---


# 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



```python

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):



```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