45 lines
1.6 KiB
Markdown
45 lines
1.6 KiB
Markdown
# How to Run
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## Prerequisites
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* MetaTrader 5 terminal installed.
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* Python 3.x installed.
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* The following Python libraries installed: `pandas`, `scikit-learn`, `skl2onnx`, `onnxruntime`.
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## Installation
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1. Clone the repository to your local machine.
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2. Install the required Python libraries using pip:
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```bash
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pip install pandas scikit-learn skl2onnx onnxruntime
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```
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## Running the project
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1. **Export Price Data**:
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* Open the MetaEditor in your MetaTrader 5 terminal.
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* Open the `Export_EURUSD_History.mq5` file.
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* Compile the script.
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* Run the script on a EUR/USD chart. This will create a `raw_price_data.csv` file in the `MQL5/Files` directory of your MetaTrader 5 installation.
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* Copy the `raw_price_data.csv` file to the root of the project directory.
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2. **Train the Models**:
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* Run the `create_benchmark_model.py` script to create the benchmark model.
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```bash
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python create_benchmark_model.py
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```
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* Run the `Model_Development.py` script to train the neural network model.
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```bash
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python Model_Development.py
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```
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3. **Run the Expert Advisors**:
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* Copy the `Benchmark_Model_EA.mq5` and `Neural_Network_Trader_EA.mq5` files to the `MQL5/Experts` directory of your MetaTrader 5 installation.
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* Copy the `benchmark_logistic_model.onnx` and `trading_neural_network_model.onnx` files to the `MQL5/Files` directory of your MetaTrader 5 installation.
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* Open the MetaEditor and compile the EAs.
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* Attach the EAs to a EUR/USD chart in your MetaTrader 5 terminal.
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