Research On Paired Transaction Based On Reinforcement Learning Algorithm

Statistical arbitrage is a trading strategy that can generate risk-free profits and avoi.d market risks.On March 31,2010,the margin and margin business was officially launched,and the Chinese stock market also ushered in a short-selling mechanism.Thus a series of statistical arbitrage strategies are derived.Among them,pairing trading strategy has been widely paid attention to and applied in statistical arbitrage investment strategy,so it is the most important trading strategy in statistical arbitrage.A pairing is a return on converging asset spreads by building long-short positions in paired assets.One significant advantage of this trading strategy is that it can hedge against the systemic risk of investment effectively,even if the market as a whole is in a downward state,the matching trade can profit from it.With the development of the market,the matching trading is becoming more and more familiar,but the profit opportunities of the matching trading strategy are far less than before.Moreover,in the transaction model studied in the past,some parameters,such as opening threshold,closing threshold and so on,are not fixed,so it is difficult to guarantee the maximum profit of the matching transaction.Moreover,some more ideal conditions such as variance homogeneity of residuals are often difficult to implement in operation.With the development of the market economy,the fixed parameter transaction model is unable to meet the increasing market demand.Therefore,a transaction model with artificial intelligence attributes has been developed,and the discovery of this new model is of great significance for the improvement of the indexes in all aspects of the traditional paired trading model.The essence of the new model is to introduce the idea of reinforcement learning algorithm into the traditional pairing trading strategy.The reinforcement learning algorithm is combined with the traditional pairing transaction model,and the fixed parameter method in the traditional pairing transaction model is improved to dynamic parameter optimization method to increase the profit opportunity.This paper will take the banking stocks in the A-share market as the research goal,through the correlation analysis and the VaR risk assessment model to carry on the efficient primary election to the alternative banking stock,after determining the matching stock,Cointegration based on the theory of stock unit root test,followed by the cointegration of the relationship test,then in the reinforcement learning algorithm based on the paired trading model empirical research,and the conclusion is summarized and analyzed.The results show that compared with the traditional model,the pairing transaction model based on reinforcement learning algorithm increases the return rate and reduces the investment risk,and has the ability of continuous learning,which can promote the increase of the return rate.The results also show that the efficiency of pairing trading based on reinforcement learning algorithm in Chinese stock market can obtain significant positive returns.