# Technical Documentation

## Project Overview

This project is a trading bot for MetaTrader 5 that uses a machine learning model to make trading decisions. The project consists of MQL5 scripts for interacting with the MetaTrader 5 terminal and Python scripts for developing and training the machine learning model.

## Technology Stack

*   **MQL5**: Used to create the Expert Advisors (EAs) that run in the MetaTrader 5 terminal.
*   **Python**: Used for data analysis, machine learning model development, and training.
*   **Pandas**: Used for data manipulation and analysis.
*   **scikit-learn**: Used for creating and training the machine learning models.
*   **ONNX**: Used to export the trained models to a format that can be used by the MQL5 EAs.
*   **skl2onnx**: Used to convert scikit-learn models to ONNX format.

## File Descriptions

*   `Export_EURUSD_History.mq5`: An MQL5 script that exports historical price data for the EUR/USD pair to a CSV file.
*   `Model_Development.py`: A Python script that loads the exported price data, performs feature engineering, and trains a neural network model. The trained model is then exported to an ONNX file.
*   `create_benchmark_model.py`: A Python script that creates and trains a simple logistic regression model to be used as a benchmark. The model is exported to an ONNX file.
*   `Benchmark_Model_EA.mq5`: An MQL5 Expert Advisor that uses the benchmark logistic regression model to make trading decisions.
*   `Neural_Network_Trader_EA.mq5`: An MQL5 Expert Advisor that uses the trained neural network model to make trading decisions.
*   `raw_price_data.csv`: A CSV file containing historical price data for the EUR/USD pair.
*   `benchmark_logistic_model.onnx`: The exported benchmark logistic regression model in ONNX format.
*   `trading_neural_network_model.onnx`: The exported trained neural network model in ONNX format.
*   `MT5-PY-AI-Tbot`: A file containing the concatenated code of all the project files.
*   `separate_codes/`: A directory containing the individual project files.

## Model

The project uses two machine learning models:

1.  **Benchmark Model**: A simple logistic regression model that predicts the direction of the next day's price change based on the previous day's price change.
2.  **Neural Network Model**: A multi-layer perceptron (MLP) classifier that predicts the direction of the next day's price change based on the previous day's price change. The model is trained using hyperparameter tuning with `RandomizedSearchCV` and `TimeSeriesSplit` to find the best parameters.