Research On Optimization Of Double Moving Average Strategy Based On The Shanghai And Shenzhen 300 Index And Its Component Stocks

Quantitative strategy trading is a trading method that uses programming to implement investment strategy programming and issue buying and selling instructions.It has strong data processing and information mining capabilities.Among them,the double moving average strategy,as a classic quantitative timing trading strategy,is highly favored by domestic small and medium-sized investors due to its limited professional knowledge required and clear trading logic of buying and selling points generated by the intersection of long and short cycle moving averages.However,the traditional double moving average strategy has the following shortcomings:(1)the calculation method of the moving average only considers the closing price as one price indicator,without considering other price indicators;(2)The existence of the "whip effect" makes the two moving averages of long and short cycles cross and entangle with each other,resulting in invalid trading signals,especially in volatile markets.This article takes the Shanghai and Shenzhen 300 Index and its constituent stocks as samples,and proposes two corresponding optimization methods based on the shortcomings of the traditional double moving average strategy.As a trend tracking strategy,the double moving average strategy has a better guiding role in trend based markets.Based on the characteristics of the strategy and the trend of the Shanghai and Shenzhen 300 Index over the research time interval,it is roughly divided into longer period of volatility and shorter period of trend.The long and short moving average cycles of the strategy are selected as 5days,10 days,20 days,30 days,60 days,and 90 days,in pairs,to obtain a total of 15 sets of parameters.This not only allows for a more comprehensive study of the effectiveness of the strategy,but also helps to find more guiding parameter groups.For the deficiency of considering only the closing price as a price indicator,this article uses principal component analysis to study the impact of the four basic price indicators on daily returns for each trading day.According to the analysis results,these 4 basic price indicators Weight distribution,and the price index after using the right to be distributed with the right to replace the closing price calculation of the mobile average,which will make the extracted information more comprehensive,so as to optimize the moving average calculation method.From the empirical performance indicators,the optimization of the moving average calculation method has achieved ideal results during the trend period.Among the 15 parameter groups,over 70% of the parameter groups have higher return on retesting than before optimization.The parameter group with the largest increase in return on retesting after optimization is(5,20).For the "whip effect",this article sets a threshold and combines the positive and negative momentum of the return rate as a double-layer filter,making the trading variable signals in some time intervals with weak upward or downward trends zero,in order to filter out some invalid trading signals.This method is used to optimize the existence of the "whip effect".From empirical performance indicators,the optimization method of setting thresholds has achieved certain results during periods of volatility.Setting a threshold will filter out some invalid trading signals,making the annualized volatility of the strategy under each parameter group smaller than the benchmark annualized volatility of the stock itself,reducing risk while reducing transaction costs.At the same time,the mean value of the optimized backtesting return is 0.57% higher than before,calculated based on equal weights for 15 sets of parameters.The parameter group with the largest increase in backtesting return after optimization was(30,90).Finally,a total of 30 Shanghai and Shenzhen 300 Index constituent stocks are randomly selected as sample stocks for backtesting to test the applicability of the two optimization methods in specific stock prices.After setting a threshold optimization for 15 sample stocks that are mainly in volatile market conditions,80% of the optimized sample stocks have an annualized yield higher than traditional strategies,and the annualized volatility of the optimized strategy is smaller than traditional strategies.After using the moving average calculation method to optimize the 15 sample stocks that are mainly in the trend market,the optimized annualized yield of 66.67% of the sample stocks is higher than that of traditional strategies.Based on the results of the backtesting test,setting thresholds in volatile markets and optimizing the moving average calculation method in trend markets have certain effects.