A Quantitative Trading Strategy Based on A Position Management Model

With the rapid development of economic globalization, various financial products have appeared in domestic and foreign markets. How to adopt ideal trading strategies meet demands market traders for high returns low risks has become focus investors research goal scholars. In order solve this problem, paper establishes a quantitative strategy based on position management model.First, we performed price forecasting gold bitcoin Time-series ARIMA method. A differential autoregressive moving average model was developed data at different cycle times, respectively, used predict next day. Error analysis with actual prices, it found that prediction error smallest when 60 days, relative value (APV) could be controlled 0.003016.Then, establish model. We use Apriori algorithm Association rules study rising falling assets. According rule " High throw bargain - hunting" investment market, established achieved dynamic stable returns. After is established, continue introduce evaluation indexes assets, among which first-class are profitability safety. Analytic Hierarchy Process (AHP) determine weight each index, allocate daily through ratio two asset indexes. This strategy, AHP, can not only stabilize income but also avoid risks, reaching an annualized rate return 25%. On September 10th, 2021, accumulated reach 223,640.58 USD.Further, evaluate risk resistance using Principal component analysis. Model validation by varying parameter values selecting parameters yielded locally optimal solution, consistent our initial proof solution model.Finally, conducted sensitivity The variable commission fluctuation principal varied up down results show as increases or decreases, number trades under gradually decreases curve shows sensitive meets expectations.