VTCGAN: A Proposed Multimodal Approach to Financial Time Series and Chart Pattern Generation for Algorithmic Trading

Abstract

This paper presents a novel coupled Generative Adversarial Network (GAN) for the optimization of algorithmic trading techniques, termed Visio- Temporal Conditional Generative Adversarial Network (VTCGAN). The termed Visio- Temporal Conditional Generative Adversarial Network combines an Image Generative Adversarial Network and a Multivariate Time Series Generative Adversarial Network, offering an innovative approach for producing realistic and high-quality financial time series and chart patterns. By utilizing the generated synthetic data, the resilience and flexibility of algorithmic trading models can be enhanced, leading to improved decision-making and decreased risk exposure. Although empirical analyses have not yet been conducted, the termed Visio- Temporal Conditional Generative Adversarial Network shows promise as a valuable tool for optimizing algorithmic trading techniques, potentially leading to better performance and generalizability when applied to actual financial records.