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

ArXiv ID: 2410.21291 “View on arXiv”

Authors: Unknown

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.

Keywords: LSTM, Gold vs USD, trading bot, time series forecasting, commodity trading, Commodities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper uses standard LSTM architecture and technical indicators (RSI/EMA) with minimal novel math, but includes a 23-day live trading bot test with specific profit figures, detailed data preprocessing, and operational implementation details.

  flowchart TD
    A["Research Goal<br>Can ML predict Gold vs USD<br>for profitable automated trading?"] --> B{"Data & Methodology"}
    
    B --> C["Dataset: Gold vs USD<br>Minute-by-minute historical data"]
    B --> D["Model Architecture<br>LSTM Neural Network"]
    
    C --> E["Computational Process<br>Model Training & Minute-by-Minute Prediction"]
    D --> E
    
    E --> F["Trading Bot Implementation<br>23-day live test (weekdays only)"]
    
    F --> G["Key Findings & Outcomes<br>Profit: $1623.52<br>ML successfully applied to commodity trading"]