Open AccessTrading strategy model based on LSTM neural network and Extreme Value-Dynamic programming
Author(s) -
Wan Yue
Publication year - 2022
Publication title -
bcp business and management
Language(s) - English
Resource type - Journals
ISSN - 2692-6156
DOI - 10.54691/bcpbm.v18i.570
Subject(s) - computer science , trading strategy , dynamic programming , volatility (finance) , artificial neural network , value (mathematics) , database transaction , transaction cost , investment strategy , investment value , artificial intelligence , process (computing) , investment (military) , operations research , machine learning , economics , microeconomics , econometrics , algorithm , engineering , profit (economics) , politics , political science , law , programming language , operating system
The advantage of a trading strategy is that it can help us identify potential trading opportunities. Traders need to use the best investment strategy to take into account factors such as market volatility and transaction costs, so as to get the maximum benefit on the target date. this paper uses LSTM neural network to make predictions. The predicted value is the price of gold and bitcoin five days after the investment date, the model fits well. By analyzing the problem, the goal is to find the optimal value, which can be regarded as a multi-stage decision-making process optimization problem. Therefore, this paper selects the Extreme value-DP model for planning and decision-making, and uses the maximum (minimum) value to evaluate the strategy.