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

TL;DR

This paper introduces Achilles, an LSTM-based neural network model that predicts Gold vs USD prices and is integrated into a trading bot, achieving profitable results over a 23-day test period.

Contribution

The paper presents a novel application of LSTM neural networks for gold price prediction and demonstrates its effectiveness through a practical trading bot implementation.

Findings

Achieved a profit of $1623.52 over 23 days

LSTM model successfully predicts Gold vs USD prices

Trading bot outperforms baseline strategies

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.