A Review on High-Frequency Trading Forecasting Methods: Opportunity and Challenges for Quantum Based Method

Abstract

High frequency trading, often known as HFT, is a subset of algorithmic trading, which is one of the most significant improvements to the trading environment in recent years. Algorithmic trading gives traders the ability to trade or receive orders within extremely brief time intervals, such as minutes or seconds based. The very nature of high-frequency trading necessitates the use of high-speed data feeds, which deliver information in real time as well as trade executions for high-frequency traders. Due to the unpredictable nature of the market, several different methods are utilised in order to forecast the HFT time series. However none of these methods have been demonstrated to be a consistently acceptable forecast tool. The majority of HFT trading is conducted using traditional arbitrage-based methods.The conventional method of forecasting using time series does not have the capability to take into account seasonality or outliers in a dataset. The HFT time series data is consistently expanding from day to day, thereby becoming big and enormous. To solve this problem, we require a model that is both more precise and less time-consuming. The purpose of this paper work is to investigate historical models, contemporary models and hypothetical future models that can be used to anticipate HFT time series. Within the domain of HFT forecasting where accuracy and productivity are of the utmost importance, it also becomes critical to investigate state-of-the-art technologies. In light of the inherent difficulties that classical computing methods encounter when confronted with the intricacies of time series data, our focus shifts to quantum computing as a prospective resolution. This undertaking is driven by the imperative to confront prevailing obstacles in the field of forecasting, including enhancing precision, managing extensive datasets and alleviating the consequences of noise and uncertainty.The progression of quantum technologies is expected to bring significant changes that will enhance the reliability, precision and scalability of forecasting methodologies. The synergy between quantum computing and HFT forecasting holds the promise of reshaping how we approach and derive insights from time-varying datasets. The exploration and use of quantum computing for HFT forecasting marks a significant stride toward overcoming current limitations.

Keywords

• HFT
• high frequency trading
• time series forecasting
• machine learning
• quantum computing