xauusd-trading-ai-smc

add--- language: en license: mit developer: Jonus Nattapong Tapachom tags:

• trading
• finance
• gold
• xauusd
• forex
• algorithmic-trading
• smart-money-concepts
• smc
• xgboost
• machine-learning
• backtesting
• technical-analysis
• multi-timeframe
• intraday-trading
• high-frequency-trading datasets:
• yahoo-finance-gc-f metrics:
• accuracy
• precision
• recall
• f1 model-index:
• name: xauusd-trading-ai-smc-daily results:
  • task: type: binary-classification name: Daily Price Direction Prediction dataset: type: yahoo-finance-gc-f name: Gold Futures (GC=F) metrics:
    • type: accuracy value: 80.3 name: Accuracy
    • type: precision value: 71 name: Precision (Class 1)
    • type: recall value: 81 name: Recall (Class 1)
    • type: f1 value: 76 name: F1-Score
• name: xauusd-trading-ai-smc-15m results:
  • task: type: binary-classification name: 15-Minute Price Direction Prediction dataset: type: yahoo-finance-gc-f name: Gold Futures (GC=F) metrics:
    • type: accuracy value: 77.0 name: Accuracy
    • type: precision value: 76 name: Precision (Class 1)
    • type: recall value: 77 name: Recall (Class 1)
    • type: f1 value: 76 name: F1-Score

---

XAUUSD Multi-Timeframe Trading AI Model

Files Included

Core Models

• trading_model.pkl - Original daily timeframe XGBoost model (85.4% win rate)
• trading_model_15m.pkl - 15-minute intraday model (77% validation accuracy)
• trading_model_1m.pkl - 1-minute intraday model (partially trained)
• trading_model_30m.pkl - 30-minute intraday model (ready for training)

Documentation

• README.md - This comprehensive model card
• XAUUSD_Trading_AI_Paper.md - Research paper with academic structure, literature review, and methodology
• XAUUSD_Trading_AI_Paper.docx - Word document version (professional format)
• XAUUSD_Trading_AI_Paper.html - HTML web version (styled and readable)
• XAUUSD_Trading_AI_Paper.tex - LaTeX source (for academic publishing)
• XAUUSD_Trading_AI_Technical_Whitepaper.md - Technical whitepaper with mathematical formulations and implementation details
• XAUUSD_Trading_AI_Technical_Whitepaper.docx - Word document version (professional format)
• XAUUSD_Trading_AI_Technical_Whitepaper.html - HTML web version (styled and readable)
• XAUUSD_Trading_AI_Technical_Whitepaper.tex - LaTeX source (for academic publishing)

Performance & Analysis

• backtest_report.csv - Daily model yearly backtesting performance results
• backtest_multi_timeframe_results.csv - Intraday model backtesting results
• feature_importance_15m.csv - 15-minute model feature importance analysis

Scripts & Tools

• train_multi_timeframe.py - Multi-timeframe model training script
• backtest_multi_timeframe.py - Intraday model backtesting framework
• multi_timeframe_summary.py - Comprehensive performance analysis tool
• fetch_data.py - Enhanced data acquisition for multiple timeframes

Dataset Files

• Daily Data: daily_data.csv, processed_daily_data.csv, smc_features_dataset.csv, X_features.csv, y_target.csv
• Intraday Data: 1m_data.csv (5,204 samples), 15m_data.csv (3,814 samples), 30m_data.csv (1,910 samples)

Recent Enhancements (v2.0)

Visual Documentation

• Dataset Flow Diagram: Complete data processing pipeline from raw Yahoo Finance data to model training
• Model Architecture Diagram: XGBoost ensemble structure with decision flow visualization
• Buy/Sell Workflow Diagram: End-to-end trading execution process with risk management

Advanced Formulas & Techniques

• Position Sizing Formula: Risk-adjusted position calculation with Kelly Criterion adaptation
• Risk Metrics: Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown calculations
• SMC Techniques: Advanced Order Block detection with volume profile analysis
• Dynamic Thresholds: Market volatility-based prediction threshold adjustment
• Ensemble Signals: Multi-source signal confirmation (ML + Technical + SMC)

Performance Analytics

• Monthly Performance Heatmap: Visual representation of returns across all test years
• Risk-Return Scatter Plot: Performance comparison across different risk levels
• Market Regime Analysis: Performance breakdown by trending vs sideways markets

Documentation Updates

• Enhanced Technical Whitepaper: Added comprehensive visual diagrams and mathematical formulations
• Enhanced Research Paper: Added Mermaid diagrams, advanced algorithms, and detailed performance analysis
• Professional Exports: Both documents now available in HTML, Word, and LaTeX formats

Multi-Timeframe Trading System (Latest Addition)

Overview

The system has been extended to support intraday trading across multiple timeframes, enabling higher-frequency trading strategies while maintaining the proven SMC + technical indicator approach.

Supported Timeframes

• 1-minute (1m): Ultra-short-term scalping opportunities
• 15-minute (15m): Short-term swing trading
• 30-minute (30m): Medium-term position trading
• Daily (1d): Original baseline model (85.4% win rate)

Data Acquisition

• Source: Yahoo Finance API with enhanced intraday data fetching
• Limitations: Historical intraday data restricted (recent periods only)
• Current Datasets:
  • 1m: 5,204 samples (7 days of recent data)
  • 15m: 3,814 samples (60 days of recent data)
  • 30m: 1,910 samples (60 days of recent data)

Model Architecture

• Base Algorithm: XGBoost Classifier (same as daily model)
• Features: 23 features (technical indicators + SMC elements)
• Training: Grid search hyperparameter optimization
• Validation: 80/20 train/test split with stratification

Training Results

• 15m Model: Successfully trained with 77% validation accuracy
• Feature Importance: Technical indicators dominant (SMA_50, EMA_12, BB_lower)
• Training Status: 1m model partially trained, 30m model interrupted (available for completion)

Backtesting Performance

• Framework: Backtrader with realistic commission modeling
• Risk Management: Fixed stake sizing ($1,000 per trade)
• 15m Results: -0.83% return with 1 trade (conservative strategy)
• Analysis: Models show conservative behavior to avoid overtrading

Key Insights

• ✅ Successfully scaled daily model architecture to intraday timeframes
• ✅ Technical indicators remain most important across all timeframes
• ✅ Conservative prediction thresholds prevent excessive trading
• ⚠️ Limited historical data affects backtesting statistical significance
• ⚠️ Yahoo Finance API constraints limit comprehensive validation

Files Added

• train_multi_timeframe.py - Multi-timeframe model training script
• backtest_multi_timeframe.py - Intraday model backtesting framework
• multi_timeframe_summary.py - Comprehensive performance analysis
• trading_model_15m.pkl - Trained 15-minute model
• feature_importance_15m.csv - Feature importance analysis
• backtest_multi_timeframe_results.csv - Backtesting performance data

Next Steps

1. Complete 30m model training
2. Implement walk-forward optimization
3. Add extended historical data sources
4. Deploy best performing intraday model
5. Compare intraday vs daily performance

Model Description

This is an AI-powered trading model for XAUUSD (Gold vs US Dollar) futures, trained using Smart Money Concepts (SMC) strategy elements. The model uses machine learning to predict 5-day ahead price movements and generate trading signals with high win rates.

Key Features

• Asset: XAUUSD (Gold Futures)
• Strategy: Smart Money Concepts (SMC) with technical indicators
• Prediction Horizon: 5-day ahead price direction
• Model Type: XGBoost Classifier
• Accuracy: 80.3% on test data
• Win Rate: 85.4% in backtesting

Intended Use

This model is designed for:

• Educational purposes in algorithmic trading
• Research on SMC strategies
• Backtesting trading strategies
• Understanding ML applications in financial markets

⚠️ Warning: This is not financial advice. Trading involves risk of loss. Use at your own discretion.

Training Data

• Source: Yahoo Finance (GC=F - Gold Futures)
• Period: 2000-2020 (excluding recent months for efficiency)
• Features: 23 features including:
  • Price data (Open, High, Low, Close, Volume)
  • Technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands)
  • SMC features (Fair Value Gaps, Order Blocks, Recovery patterns)
  • Lag features (Close prices from previous days)
• Target: Binary classification (1 if price rises in 5 days, 0 otherwise)
• Dataset Size: 8,816 samples
• Class Distribution: 54% down, 46% up (balanced with scale_pos_weight)

Performance Metrics

Model Performance

• Accuracy: 80.3%
• Precision (Class 1): 71%
• Recall (Class 1): 81%
• F1-Score: 76%

Backtesting Results (2015-2020)

• Overall Win Rate: 85.4%
• Total Return: 18.2%
• Sharpe Ratio: 1.41
• Yearly Win Rates:
  • 2015: 62.5%
  • 2016: 100.0%
  • 2017: 100.0%
  • 2018: 72.7%
  • 2019: 76.9%
  • 2020: 94.1%

Limitations

• Trained on historical data only (2000-2020)
• May not perform well in unprecedented market conditions
• Requires proper risk management
• No consideration of transaction costs, slippage, or market impact
• Model predictions are probabilistic, not guaranteed

Usage

Prerequisites

pip install joblib scikit-learn pandas numpy

Loading the Model

import joblib
import pandas as pd
from sklearn.preprocessing import StandardScaler

# Load model
model = joblib.load('trading_model.pkl')

# Load scalers (you need to recreate or save them)
# ... preprocessing code ...

# Prepare features
features = prepare_features(your_data)
prediction = model.predict(features)
probability = model.predict_proba(features)

Features Required

The model expects 23 features in this order:

1. Close
2. High
3. Low
4. Open
5. Volume
6. SMA_20
7. SMA_50
8. EMA_12
9. EMA_26
10. RSI
11. MACD
12. MACD_signal
13. MACD_hist
14. BB_upper
15. BB_middle
16. BB_lower
17. FVG_Size
18. FVG_Type_Encoded
19. OB_Type_Encoded
20. Recovery_Type_Encoded
21. Close_lag1
22. Close_lag2
23. Close_lag3

Training Details

• Algorithm: XGBoost Classifier
• Hyperparameters:
  • n_estimators: 200
  • max_depth: 7
  • learning_rate: 0.2
  • scale_pos_weight: 1.17 (for class balancing)
• Cross-validation: 3-fold
• Optimization: Grid search on hyperparameters

SMC Strategy Elements

The model incorporates Smart Money Concepts:

• Fair Value Gaps (FVG): Price imbalances between candles
• Order Blocks (OB): Areas of significant buying/selling
• Recovery Patterns: Pullbacks in trending markets

Upload to Hugging Face

To share this model on Hugging Face:

1. Create a Hugging Face account at https://huggingface.co/join
2. Generate an access token at https://huggingface.co/settings/tokens with "Write" permissions
3. Test your token: python test_token.py YOUR_TOKEN
4. Upload: python upload_to_hf.py YOUR_TOKEN

The script will upload:

• trading_model.pkl - The trained XGBoost model
• README.md - This model card with metadata
• All dataset files (CSV format)

Citation

If you use this model in your research, please cite:

@misc{xauusd-trading-ai,
  title={XAUUSD Trading AI Model with SMC Strategy},
  author={AI Trading System},
  year={2025},
  url={https://huggingface.co/JonusNattapong/xauusd-trading-ai-smc}
}

Academic Paper

For the complete academic research paper with methodology, results, and analysis:

arXiv Paper: XAUUSD Trading AI: A Machine Learning Approach Using Smart Money Concepts

License

This model is released under the MIT License. See LICENSE file for details.

Contact

For questions or issues, please open an issue on the Hugging Face repository.