Variational autoencoder-based dimension reduction of Ichimoku features for improved financial market analysis

Abstract

Financial markets are complex and dynamic, and accurately predicting market trends is crucial for traders and financial analysts. Ichimoku-based features have gained significant attention in financial market analysis due to their ability to capture essential market signals and patterns. This significant compression retains essential patterns related to trends, support/resistance levels, and trading signals. The reduced dimensionality improves computational efficiency and could allow for more accurate predictive modeling by traders. However, real-world testing is needed because compressing data risks losing useful nuances. In this study, we utilize an autoencoder for the dimensionality reduction of Ichimoku-based features in financial market analysis. The autoencoder, a neural network architecture, compresses high-dimensional data into a lower-dimensional representation by learning important features and patterns. The experiments conducted on a Euro/Dollar market dataset spanning 1990, comprising 16 columns with Ichimoku features, reveal the remarkable reduction of dataset size from 2,269,500 to 756,375, equivalent to a decrease of 66.67 %. These results highlight the efficiency of the proposed approach in reducing the dimensionality of financial market data, suggesting its potential as a valuable tool for traders and financial analysts to predict market trends and make informed decisions in the financial markets.