Machine Learning Methods in Algorithmic Trading: An Experimental Evaluation of Supervised Learning Techniques for Stock Price

In the dynamic world of financial markets, accurate price predictions are essential for informed decision-making. This research proposal outlines a comprehensive study aimed at forecasting stock and currency prices using state-of-the-art Machine Learning (ML) techniques. By delving into the intricacies of models such as Transformers, LSTM, Simple RNN, NHits, and NBeats, we seek to contribute to the realm of financial forecasting, offering valuable insights for investors, financial analysts, and researchers. This article provides an in-depth overview of our methodology, data collection process, model implementations, evaluation metrics, and potential applications of our research findings.The research indicates that NBeats and NHits models exhibit superior performance in financial forecasting tasks, especially with limited data, while Transformers require more data to reach full potential.Our findings offer insights into the strengths of different ML techniques for financial prediction, highlighting specialized models like NBeats and NHits as top performers - thus informing model selection for real-world applications.To enhance readability, all acronyms used in the paper are defined below:ML: Machine LearningLSTM: Long Short-Term MemoryRNN: Recurrent Neural NetworkNHits: Neural Hierarchical Interpolation for Time Series ForecastingNBeats: Neural Basis Expansion Analysis for Time SeriesARIMA: Autoregressive Integrated Moving AverageGARCH: Generalized Autoregressive Conditional HeteroskedasticitySVMs: Support Vector MachinesCNNs: Convolutional Neural NetworksMSE: Mean Squared ErrorMAE: Mean Absolute ErrorRMSE: Recurrent Mean Squared ErrorAPI: Application Programming InterfaceF1-score: F1 ScoreGRU: Gated Recurrent Unityfinance: Yahoo Finance (a Python library for fetching financial data)