Research And Application Of Volatility Prediction Based On High-frequency Trading Data In Financial Markets Using Deep Learning

The volatility of high-frequency trading data in finance is an important indicator in financial markets,which reflects the fluctuation of asset prices in a short period of time.Investors can determine the level of risk based on the volatility of assets and adjust their investment portfolios accordingly.In addition,investors can also predict the trend of asset price changes based on volatility to develop corresponding trading strategies,thereby avoiding market risks and obtaining higher returns.With the development of information technology,many online trading platforms can provide high-frequency real-time trading data,providing a foundation for research on the volatility of highfrequency trading data based on big data.Many scholars have begun to use machine learning and deep learning algorithms to analyze large amounts of trading data and establish volatility prediction models to help investors better grasp market risks and opportunities.However,financial high-frequency trading data factors have high-dimensional sparsity,nonlinearity,high correlation,and non-stationarity,and the data also contains a lot of noise,all of which result in poor predictive performance of the model.To address the above issues,this paper takes the real trading data of the Shanghai Stock Exchange 50 ETF and stocks as the research object to study the method of predicting the volatility of high-frequency financial trading data.The research contents of this paper mainly include the following three aspects:(1)A reinforcement learning-based swarm optimization method for financial factor feature selection is proposed.This paper proposes a feature selection method for financial high-frequency trading data based on reinforcement learning and bee colony optimization,targeting the characteristics of highdimensional sparsity,nonlinearity,and high autocorrelation of financial high-frequency trading data factors.Firstly,the bee colony optimization algorithm is introduced to perform distributed search on the feature data of the factors,obtaining multiple feature subspaces.Then,based on the adaptive learning and experience replay buffer mechanism of reinforcement learning,the fitness of the feature sets in each space is calculated to adjust the optimization direction of the optimization algorithm in each space,and finally,the optimal solution of each subspace is searched globally to obtain the optimal feature subset.The distributed search method solves the problem of highdimensional sparsity of financial factor features,and the adaptive learning and experience replay buffer mechanism for training can effectively solve the problems of nonlinearity and high correlation among features.Finally,experiments show that compared with the traditional feature selection method,the feature subset selected by this method is more representative.(2)A high-frequency trading data volatility prediction model based on denoising autoencoder and unstable attention mechanism is constructed.In response to the characteristics of noise and non-stationarity in high-frequency trading data,this paper constructs a high-frequency trading data volatility prediction model based on denoising autoencoder and unstable attention mechanism.Firstly,the noisy data is trained with a denoising autoencoder module to enable the encoder to learn the noise in the data and output denoised data,solving the problem of a large amount of noise in financial high-frequency data.Secondly,by introducing an unstable attention mechanism to learn the sequence information,the non-stationary attribute in the data is restored by changing the weight size.Moreover,a mixed convolution layer is introduced after the encoder attention layer in the Transformer to help the unstable attention mechanism capture the spatiotemporal relationships in the sequence,thus enhancing the performance of the non-stationary transformer and solving the problem of non-stationary data affecting model performance,thereby improving the performance of the model.Finally,the real high-frequency trading data set is used to verify that this method can solve the impact of noise and non-stationary data in high-frequency trading data without destroying the original data.(3)A high-frequency financial trading data volatility prediction system based on Django was designed and implemented.This paper designs and implements a high-frequency financial trading data volatility prediction system based on Django.The system is developed with front-end and back-end separation.The main purpose of the system is to apply the models and methods proposed in this paper to actual financial markets and provide information reference for financial investors.The system’s functional modules mainly include user management module,data management module,data visualization module,position management module,and volatility prediction module.It helps investors analyze market data using volatility to determine the pattern and trend of financial markets and adjust trading strategies accordingly to achieve maximum returns.