Quantitative trading portfolio optimization based on machine learning

Abstract

The capital asset pricing model (CAPM) is to study the quantitative relationship between the expected return rate of assets and risky assets in the securities market. With the development of computer network technology and the trading market, quantitative trading has gradually become a new way of investment. Its essence lies in the decision of trading strategy. This paper obtains the relative price data of the subsequent short-term investment period through the time series method, then uses the PA algorithm in machine learning to update the portfolio and verify the effectiveness of the strategy in the data set. Based on the exchange rate change trend of the US dollar, gold, and bitcoin within five years given in the question, we constructed dynamic portfolio models for the three assets to ensure maximum profits. First, we use the time series model to predict the relative price data of the next period in a continuous trading period. Then, we implement the PA passive attack algorithm by calling the Sklearn package in Python to update the asset components in real-time every day and calculate the average annual return rate in 5 years. Based on the above analysis, we used the ADF unit root to test the stationarity of the time series. Meanwhile, we also carried out a white noise test of time series. The specific test indexes include: According to CAPM's theory, this product has a high yield, with an annual interest rate of 195.849%, which is entirely objective. However, the annual standard deviation reached 0.919164, and the maximum retractable rate exceeded 50%, indicating a sizeable overall risk. Despite this, the composite product's Calmar ratio is 3.556, Sharpe ratio is 2.104623, and its return per unit risk is still high. Compared with other quantitative trading models, this paper combines time series, machine learning, Monte Carlo, and other algorithms to effectively solve the portfolio investment optimization problem of nonlinear time-varying correlation structure among multiple assets. It enriches the results of portfolio investment structure decisions.