Composite anti Risk Trading Strategy Model for Gold and Bitcoin

Abstract

In recent decades, quantitative trading has been widely applied in both individual and institutional contexts through algorithms and automated trading. Price prediction and strategy decision-making are two crucial components of quantitative trading. While these two aspects have garnered extensive attention, the exploration and improvement of how to more effectively integrate them have been ongoing pursuits. In this paper, we construct a composite anti-risk trading strategy model based on short-term volatility identification and long-term trend prediction. The perfect combination of short-term fluctuations and long-term trends is achieved through the construction of parameters, such as Rpv, related to long-term trends. Simultaneously, the introduction of risk assessment indicators enhances the model’s ability to withstand risks. Integrating various modules, we obtain the SLRD model, mapping historical data to trading strategies. Utilizing real data from financial markets, we apply this model to cases involving gold and Bitcoin. The results show significant improvements when compared to previous models.