Ash
35 lines
2.5 KiB
Markdown
35 lines
2.5 KiB
Markdown
# Technical Documentation
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## Project Overview
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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.
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## Technology Stack
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* **MQL5**: Used to create the Expert Advisors (EAs) that run in the MetaTrader 5 terminal.
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* **Python**: Used for data analysis, machine learning model development, and training.
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* **Pandas**: Used for data manipulation and analysis.
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* **scikit-learn**: Used for creating and training the machine learning models.
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* **ONNX**: Used to export the trained models to a format that can be used by the MQL5 EAs.
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* **skl2onnx**: Used to convert scikit-learn models to ONNX format.
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## File Descriptions
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* `Export_EURUSD_History.mq5`: An MQL5 script that exports historical price data for the EUR/USD pair to a CSV file.
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* `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.
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* `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.
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* `Benchmark_Model_EA.mq5`: An MQL5 Expert Advisor that uses the benchmark logistic regression model to make trading decisions.
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* `Neural_Network_Trader_EA.mq5`: An MQL5 Expert Advisor that uses the trained neural network model to make trading decisions.
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* `raw_price_data.csv`: A CSV file containing historical price data for the EUR/USD pair.
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* `benchmark_logistic_model.onnx`: The exported benchmark logistic regression model in ONNX format.
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* `trading_neural_network_model.onnx`: The exported trained neural network model in ONNX format.
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* `MT5-PY-AI-Tbot`: A file containing the concatenated code of all the project files.
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* `separate_codes/`: A directory containing the individual project files.
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## Model
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The project uses two machine learning models:
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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.
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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.
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