Time Series Artificial Neural Network based Classification Model for Asset Trading Strategy

Abstract

Best daily trading strategy is meaningful to investors to obtain the maximum return. Financial market plays a key role in the social development. Sufficient funds are of great significance to promote the development of all sectors of society. From the perspective of investors, their purpose is to obtain the maximum income through investing assets in the market. At the same time, the benefit of investors can attract more investors and funds. However, there are huge risks in investment in financial markets. This is mainly because the changes of assets' prices are very complex, and traders cannot predict the changes of assets' prices. Therefore, it is of great significance for investors to determine the trading behavior according to the historical data of asset's price. Aiming at the problem of asset investment strategy, we study from three aspects: asset value accounting, optimal investment strategy and investment decision prediction. A novel method is proposed to represent trading action based on asset state, which represents the current asset state through a 0-1 variable. Then, the change of state indicates the occurrence of transaction. Based on this variable, we propose an evaluation model of the total value of the asset on every day. By maximizing the value of the combination, we establish an optimization model and the Genetic Algorithm (GA) is used to search the best investment combination. Using the prior information of the price, we first solve the combination of asset states of the investor corresponding to the best trading strategy. Then, taking the sequence of states as the supervision information, an Artificial Neural Network (ANN) based classification model for asset state prediction is established. The historical data of Bitcoin daily price is used to predict the asset state on the current day, to realize the decision-making of daily trading strategy. Extensive experiments have been conducted, and the experimental results verify our model can effectively find potential pattern for asset investment and achieve good profits based on the predict investment decisions.