Exploring Forex Data with Ensemble Learning Models Unleashing the future of forex, gold, and stock trading with cutting-edge AI! This project harnesses Python, Deep Learning, and advanced ML techniques to automate trading across 50+ currency pairs, delivering over 100,000 data points from MetaTrader5.

• Project Overview
• Features
• Data & Methodology
• Models & Workflow
• Results
• Getting Started
• Installation

• Real-time automated trading for forex, gold, and stocks.
• Advanced predictive modeling with a suite of ML and DL algorithms.
• Dynamic data visualization to track market trends.
• Scalable deployment on VPS for MT5 integration.
• Daily performance reports and strategy optimization.

This project aggregates data from over 50 currency pairs, gold, and various stocks, totaling nearly 100,000 data points collected via MetaTrader5. The methodology includes:

• Data Collection: Gathering high-quality market data.
• Preprocessing & Exploration: Analyzing trends and correlations across currency pairs.
• Correlation Fusion: Combining data from highly correlated assets for enhanced predictions.
• Deployment: Running the system on a VPS to execute trades on MT5.

A robust pipeline powers this project, featuring:

1. Data Ingestion: Collecting and cleaning raw data.
2. Exploratory Analysis: Identifying common trends across currency pairs.
3. Data Integration: Merging correlated assets for robust modeling.
4. Model Implementation: Deploying a diverse set of algorithms:
   • Traditional ML: Random Forest, XGBoost, AdaBoost, CatBoost.
   • Deep Learning: LSTM, Bi-LSTM, Transformer, CNN-LSTM, GRU, Temporal Fusion Transformer (TFT).
5. Model Filtering: Eliminating low-accuracy models and those predicting significant losses.
6. Ensemble Prediction: Combining outputs from multiple models for optimal results.
7. Post-Processing: Adjusting bet ratios based on accuracy thresholds.
8. Automation: Deploying a self-running trading system with daily reports.

The models shine on test datasets, achieving:

• Binary Classification: Over 80% accuracy across 50+ currency pairs.
• Ternary Classification: 84% accuracy (up, down, sideways) on multi-label data. These results highlight the project's potential for profitable, automated trading strategies.

To set up and run the Automation-Forex project on your local machine, follow these steps:

Download the project files to your system.

git clone https://github.com/AlexNhat/Automation-Forex.git

Move into the cloned folder.

cd Automation-Forex

Ensure you have Python installed (preferably 3.8+), then install the required libraries from the requirements.txt file.

pip install -r requirements.txt

Note: The requirements.txt file contains all necessary packages, including libraries for machine learning (e.g., TensorFlow, Scikit-learn), data handling (e.g., Pandas, NumPy), and MetaTrader5 integration.

Set up your MT5 account and ensure the MT5 Python API is properly linked (details in the docs/ folder if provided).

Execute the main script to start the automation process.

python main.py