Research on the Investment Strategy of Quantitative Trading Based on Random Forest and Genetic Algorithm

Abstract

Gold and Bitcoin are two common investments in the market. Investors profit from the capital market through specific trading strategies. Based on previous price data, this paper established a prediction-decision model, which provides investors with an optimal trading strategy and effectively improves the return on investment. To begin with, we use the past daily price of gold and bitcoin to calculate the Bollinger band, DMA, and MACD characteristics. We used the Random Forest algorithm to predict the next day's price and established a loop to predict all price data. Finally, we got the prediction data with the goodness of fit ( ) as high as 0.98. We intuitively see that the prediction effect is pretty good from the predictive value-real value-line chart. Then, we established the transaction decision-making objective model, used the Genetic Algorithm to select the daily transaction amount, obtained the optimal transaction strategy, and then established a loop to obtain all the optimal strategies. The final total assets were 243,424.76, and the annual return rate reached 6085.62%.