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.

Bitcoin, the most popular cryptocurrency around the world, has had frequent and dramatic price changes in recent years. The price of bitcoin reached a new peak, nearly $65,000 in July 2021. Then, in the second half of 2022, the bitcoin price begins to decrease gradually and drops below $20,000. Such huge changes in the bitcoin price attract millions of people to invest and earn profits. This research focuses on the predictions of bitcoin price changes and provides a reference for trading bitcoin for investors. In this research, we consider a method in which we first apply several traditional machine learning regression models to predict the Changes of Moving Average in the bitcoin price, and then based on the predicted results, we set labels for bitcoin price changes to get the classification results. This research shows that the method of transforming regression results to the classification analysis can achieve higher accuracy than the corresponding machine learning classification models and the best accuracy is 0.81. Besides, according to this method, this research constructs a Machine Learning Trading Strategy to compare with the traditional Double Moving Average Strategy. In a simulation experiment, the Machine Learning Trading Strategy also has a better performance and earns a 68.73% annualized return.