In computational intelligence, Gerald Appel designed MACD, short for Moving Average Convergence /Divergence in the 1970s, a popular trading indicator used in the business data analysis of stock prices to predict future trends. While it is easy to read, MACD has two distinct disadvantages, the time lagging problem and the fake signals problem, resulting in delays in buying or selling signals and decisions. Besides, three parameters input are required for the calculation model, which is not user-friendly for new learners. This study proposes a new methodology - Volume Square-Weighted Moving Average Convergence & Divergence (VSWMACD). It aims to improve MACD performance and apply various evaluation tools to verify the enhancements. Five datasets with 200 stocks from Hong Kong Stock Market in each have been applied to the testing. The outcome shows that compared to MACD, the average Return On Investment of VSWMACD increased by around 15%, and the average Maximum Drawdown decreased by about 5%. VSWMACD is proven to reduce fake signals while earning a higher return with a lower risk than MACD. A better portfolio management can be formed. © 2023 IEEE.