Stock trading method based on reinforcement learning

Abstract

The invention discloses a stock trading method based on reinforcement learning, and relates to the field of machine learning and quantitative trading. According to the invention, stock transaction is carried out based on a cyclic reinforcement learning algorithm with adaptive capability; the method comprises the steps that a user interaction interface and a classification neural network N train a classification neural network; through a circular reinforcement learning RRL training stage, three types of operations of buying, holding and selling are respectively executed in recognized different stock market cycle scenes, after RRL training is completed, an automatic transaction execution and loss stopping stage is entered, dynamic loss stopping is performed according to a loss stopping strategy set by a user, and automatic transaction execution is performed. According to the invention, the specific preference of the user for risk earnings can be satisfied, the risk of manual transaction errors is reduced, and the cost of manual decision making is reduced; compared with a traditional linear model and a Q learning method, the price self-adaptability is higher, the made investment decision is timely and effective, and the transaction winning rate can be greatly improved.

Description

technical field [0001] The invention belongs to the field of machine learning and quantitative trading, in particular to a stock trading method based on reinforcement learning. Background technique [0002] With the development of society and the advancement of information technology, the global scale of funds for stock quantitative trading with the help of computers is increasing. Almost all financial institutions are taking the road of informatization, and now it is very common for various institutions to use computer-aided stock trading, which greatly saves the workload of investment decision-makers. However, most small and medium investors basically rely on manpower to check the market, and the buying and selling process is relatively casual. This not only increases the workload of stock trading, but the rate of return is not too high. In order to solve this problem, many auxiliary stock trading software have been developed and put into the market. However, the degree...

AI Technical Summary

Problems solved by technology

However, the degree of generalization of these software is relatively high, and the operation is relatively cumbersome, which still cannot meet the individual needs of the majority of small and medium investors.

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