Enhanced Bollinger Band Stock Quantitative Trading Strategy Based on Random Forest
Keywords:stock price prediction, weighted moving average, random forest, stock quantitative trading, bollinger band strategy
Constructing applicable automated stock trading strategies has become one of the best ways that people can earn profits from their underlying assets' investments now. Automated stock trading, also called quantitative trading, contains sets of human-defined rules, which are written in codes to make decisions to go long or short on stocks on a computer. Investment banks, brokerages, private equity funds, and other financial institutions around the world are keen on investigating and developing quantitative trading strategies with sustainable profitability to yield higher returns than the normal market. This research aims to observe the trading performance and profits of financial banking stocks in the Hong Kong stock market by building a quantitative trading strategy named Enhanced Bollinger Band Strategy based on Random Forest and Bollinger Bands. In experiments, the Random Forest algorithm is applied to predict the Weighted Moving Average the next day. Meanwhile, Bollinger Bands are the trading signals used to make decisions on going long or short positions based on the historical moving average lines and standard deviation. Performances of the Enhanced Bollinger Band Strategy are evaluated by test sets of ten financial banking stocks. We also compare the performance of the Enhanced Bollinger Band Strategy and Traditional Bollinger Band Strategy and find that the Enhanced Bollinger Band Strategy can earn 10-30% profits on a variety of stocks although these stocks are losing 10-50% original amount of investment in Traditional Bollinger Band Strategy and basic investment. Therefore, a combination of Random Forest and Bollinger Bands in the quantitative trading strategy generates higher returns than simply investing in stocks.