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Achilles, Neural Network to Predict the Gold Vs US Dollar Integration with Trading Bot for Automatic Trading

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  • Angel Varela

Abstract

Predicting the stock market is a big challenge for the machine learning world. It is known how difficult it is to have accurate and consistent predictions with ML models. Some architectures are able to capture the movement of stocks but almost never are able to be launched to the production world. We present Achilles, with a classical architecture of LSTM(Long Short Term Memory) neural network this model is able to predict the Gold vs USD commodity. With the predictions minute-per-minute of this model we implemented a trading bot to run during 23 days of testing excluding weekends. At the end of the testing period we generated $1623.52 in profit with the methodology used. The results of our method demonstrate Machine Learning can successfully be implemented to predict the Gold vs USD commodity.

Suggested Citation

  • Angel Varela, 2024. "Achilles, Neural Network to Predict the Gold Vs US Dollar Integration with Trading Bot for Automatic Trading," Papers 2410.21291, arXiv.org, revised Oct 2024.
    • Handle: RePEc:arx:papers:2410.21291
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    References listed on IDEAS

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    1. Xiaobin Tang & Nuo Lei & Manru Dong & Dan Ma & Atila Bueno, 2022. "Stock Price Prediction Based on Natural Language Processing1," Complexity, Hindawi, vol. 2022, pages 1-15, May.
    2. Opeyemi Sheu Alamu & Md Kamrul Siam, 2024. "Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals," Papers 2410.07220, arXiv.org.
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