From e1d7a439a74513e63cac8a504f86685b3d6b2360 Mon Sep 17 00:00:00 2001 From: Ash Date: Thu, 23 Oct 2025 23:23:42 +0100 Subject: [PATCH] docs: Add project flow documentation --- flow.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 flow.md diff --git a/flow.md b/flow.md new file mode 100644 index 0000000..f875c36 --- /dev/null +++ b/flow.md @@ -0,0 +1,18 @@ +# Project Flow + +This document outlines the workflow of the AI-Enhanced MQL5 Trading Bot project. + +1. **Data Export**: + * The process starts with the `Export_EURUSD_History.mq5` script, which is an MQL5 script that runs on the MetaTrader 5 terminal. + * This script exports historical price data for the EUR/USD pair to a CSV file named `raw_price_data.csv`. + +2. **Model Training**: + * The `raw_price_data.csv` file is then used by the Python scripts to train the machine learning models. + * The `create_benchmark_model.py` script trains a logistic regression model and exports it to `benchmark_logistic_model.onnx`. + * The `Model_Development.py` script trains a neural network model using hyperparameter tuning and exports it to `trading_neural_network_model.onnx`. + +3. **Trading**: + * The exported ONNX models are used by the MQL5 Expert Advisors (EAs) to make trading decisions. + * The `Benchmark_Model_EA.mq5` EA uses the `benchmark_logistic_model.onnx` model. + * The `Neural_Network_Trader_EA.mq5` EA uses the `trading_neural_network_model.onnx` model. + * The EAs run on the MetaTrader 5 terminal, and on each new bar, they get the latest price data, feed it to the ONNX model, and execute a trade based on the model's prediction.