Market traders often buy and sell volatile assets with the goal of maximizing total returns. In order to provide traders with the optimal trading strategy, this paper establishes a trading model based on neural network and Apriori algorithm. Firstly, a neural network model is established according to the given data to predict the price data on the trading day. The Apriori algorithm is then used to obtain the frequent term set, which complements the neural network model to obtain the trading strategy to determine whether to add or close a position and the size of the trade. The model is then further optimized to obtain a stable trading strategy considering the risk and cost of trading.

Kairui Liu, Yao Wang, Jifeng Fan. Implementing Trading Strategies for Gold and Bchain Based on Neural Networks and Apriori Algorithms. Academic Journal of Business & Management (2022) Vol. 4, Issue 7: 63-67. https://doi.org/10.25236/AJBM.2022.040710.

[1] Ma SH,Liu JW,Zuo X., A review of graph neural networks. Computer Research and Development, 2022. 

[2] Huang WJ, Li YT, Huang Y, Chaotic time series prediction based on hybrid neural network and attention mechanism. Journal of Physics, 2021. 

[3] Yu, Haibo, Jiang, Research on the application of data mining in cross-selling in banks, 2010.

[4] Yao Haixiang, Li Junwei, Xia Shenghao, Chen Shumin Fuzzy trading decision based on Apriori algorithm and neural network [J] Systems Science and mathematics, 2021,41 (10): 2868-2891