Design Of Quantitative Trading Strategy For Shanghai And Shenzhen 300 Stock Index Futures Under High Frequency Data

Since September 8,2006,China Financial Futures Exchange(CFFEX)was listed in Shanghai Futures Building,followed by Shanghai and Shenzhen 300 stock index futures contracts,which were officially listed on April 16,2010.This is a deepening of China's capital market.Reform,the function of the capital market and the improvement of the system all have long-term significance.At the same time,it also marks the development of China's financial derivatives market into a new era.At present,stock index futures are still in their infancy in China.Many systems are still being improved and perfected.China's quantitative transactions were in the golden age of development in 2014 and 2015,and the scale expanded rapidly.After the June 2015 stock market crash,the stock market and futures markets have fallen.Since 2017,the A-share market has gone out of the market,and with the end of 2016,the futures trading has been further restricted,and the arbitrage and neutral strategies have failed.In this poor quantitative investment environment,the overall encounter with the "ice period." Since 2018,with the continuous opening of the domestic economic market,the use of mathematical modeling and statistical methods to build and optimize trading strategies,and constantly adapt to the development of the economic situation,excellent quantitative trading strategies will certainly bring to investors A substantial gain.In recent years,with the rapid development of Internet technology,China's network speed-up is about to enter the 5G era.Through the massive computing power of computers,it provides faster and more effective practical means for quantitative trading under various financial strategies and high-frequency data.In the traditional manual manual strategy design and ordering process,it is often impossible to avoid the low reaction force and execution speed of the manual,the inability to avoid artificial errors and omissions,the inability to overcome the greed of human nature,and the inability to catch some fleeting Profitable space.Therefore,in the current high-frequency environment,by designing highfrequency minute data to design a quantitative trading strategy,the computer language can solidify the trader's strategy and ideas,and automatically issue trading instructions,which can more effectively improve data acquisition and processing capabilities.At the best time to grasp the transaction,we will accumulate more stable income and risk control capabilities through higher transaction frequency in high frequency environment.This paper starts with bilateral trading,chooses the model of arbitrage trading,and uses the aggregation of stock index futures market volatility to establish ARCH model for high frequency data in the current high frequency data environment,and designs the intertemporal arbitrage strategy to carry out the volatility of its spread.Forecast,the goal of achieving stable returns at a certain cost.In the inter-temporal arbitrage trading strategy based on the CSI 300 stock index futures market constructed in this paper,by setting up the improved AR(4)-EGARCH(1,1)model,the retracement is reduced to very high by setting the stop loss point.Investors who are small and match different risk preferences according to the size of the transaction threshold have a stable and substantial return.However,with the adjustment of the fee adjustment,nowadays,the current fee is 30 times the normal handling fee,and the considerable gains in the original highfrequency transaction will be diluted by the high current and current handling fees.Based on the horizontal comparison,we have listed the different transaction thresholds,all of which are charged according to the normal handling fee and compared with the two incomes at 30 times,which is enough to illustrate the exchange's good intention to set the rate.It can be seen that the research direction of the follow-up strategy is in the case of high handling fees,and it is the best policy to suppress the trading of the current and future positions.In addition,the test set data is compared vertically according to the data frequency of one minute,three minutes and five minutes.The annualized rate of return of the strategy is reduced from 5.44% under one-minute data transaction to 2.66% under fiveminute data.Period trading requires the use of one minute or more of high frequency data to make the transaction more profitable.Finally,based on the full text,the above planned schemes are summarized and forecasted.