Abstract
Quantitative investments can be effective in reducing losses. It plays an important role in the pre-diction of the market. Due to this, more and more investors are paying attention to it. Trade Strategy Model and other models are used to predict price and make the decision. In Data Mining (DM), quantile values, advance rate, and decline rate are utilized to determine relative likelihood. Furthermore, the Apriori algorithm is used to calculate the number of continuous rises and falls that occur with a probability greater than 90%. To determine the amount of dollars to be purchased or sold in the shipment space, we establish a Position-Management Model (PMM). Finally, we introduce the Decision-Making Model (DMM) as a tool for trading decisions. We then plug $1000 into the model to arrive at $1958.37 in 2021. Based on the comparison of the predicted price and the real price from 2019-9-1 to 2019-10-31, we found a slight deviation between the predicted and the real price. As part of our verification process, we used the BackTesting method and selected the indicator "annual return rate," whose test showed an average of 17.687%, an excellent result. Afterward, we provide a summary of the strategy process for assisting its quantitative trading investment. To conclude, our models are distinguished by their precise values.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Education, Humanities and Social Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
