Achilles, Neural Network to Predict the Gold Vs US Dollar Integration with Trading Bot for Automatic Trading
This addresses the challenge of deploying ML models for stock market prediction, specifically for gold trading, but appears incremental as it applies a standard LSTM to a specific commodity.
The paper tackled predicting the Gold vs USD commodity using an LSTM neural network and a trading bot, resulting in $1,623.52 in profit over 23 days of testing.
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.