Exploration of Profitability in Rotational Trading Strategy Based on Monte Carlo Simulation

Abstract

Rotational trading strategy is an active trading strategy that adjusts positions to manage risk based on Value at Risk (VaR) in quantitative investment. Unpredictable and unusual Black Swan events make it difficult to predict and effectively mitigate the risks associated with such events. The importance of rational and efficient risk management has been highlighted by events such as the US-China trade war starting in 2018, the global spread of the COVID-19 pandemic from 2019 to 2022, stock market circuit breakers, and the oil crash in the Oil Fund. In this paper, we start from the perspective of VaR backtesting, count the occurrences of abnormal losses within a statistical interval, and establish a risk avoidance model for rotational trading to identify potential market risks and reallocate assets at suitable times. The aim of this paper is to explore whether this rotational trading strategy based on Monte Carlo simulation can effectively manage risks and achieve robust profitability in the US market under a volatile financial environment.