/forex-price-prediction

Predict and analyze historical XAU/USD Forex prices using deep learning; make decisions on short/long positions with target profit and stop loss values.

Primary LanguageJupyter Notebook

Forex Price Prediction using LSTM

Forex Price Prediction

Overview

Welcome to the Forex Price Prediction project! This repository contains a Jupyter Notebook that utilizes deep learning techniques, specifically Long Short-Term Memory (LSTM) networks, to predict the price of XAU/USD (Gold/US Dollar) for a short period of time. The goal of the prediction is to assist in deciding whether to open a short or long position with target profit (TP) and stop loss (SL) values of 1$ or 2$. Please note that the dataset used for training and testing is simulated and consists of 300,000 minutes' worth of candle data.

Project Structure

The project repository is organized as follows:

  • notebooks/: Contains the Jupyter Notebook forex_price_prediction.ipynb, where the LSTM model is implemented and the prediction process is explained in detail.
  • data/: This directory holds the simulated forex data in CSV format. It includes data for training, testing, and validation.
  • images/: Contains any images used in the README or the Jupyter Notebook.
  • README.md: The file you are currently reading, providing an overview of the project.

Requirements

To run the Jupyter Notebook and execute the LSTM model, you need the following dependencies:

  • Python 3.x
  • Jupyter Notebook
  • Pandas
  • NumPy
  • Matplotlib
  • TensorFlow
  • Keras

You can install these packages using pip:

pip install jupyter pandas numpy matplotlib tensorflow keras

## Usage

1. Clone the repository to your local machine:

```bash
git clone https://github.com/Ilia-Abolhasani/forex-price-prediction.git
cd forex-price-prediction

  1. Install the required dependencies as mentioned above.

  2. Open the Jupyter Notebook file, forex_price_prediction.ipynb, using Jupyter Notebook or Jupyter Lab.

  3. Follow the instructions provided in the notebook to preprocess the data, train the LSTM model, and make predictions.

  4. Feel free to modify the code and experiment with different hyperparameters to improve the model's performance.

  5. If you encounter any issues or have suggestions for improvements, please create an issue or submit a pull request.

License

This project is licensed under the MIT License.

You are free to use, modify, and distribute the code in this repository for personal or commercial purposes. However, we provide no warranty or support for any potential consequences of using this code.

Please see the LICENSE file for more details.

Contributing

We welcome contributions to enhance the project! If you find any issues or have ideas for improvements, please create an issue or submit a pull request.

Acknowledgments

We would like to thank the open-source community for their valuable contributions and support.