Vietnam stock technica analysis with mixed GP artificial intelligence numerical computations

Vietnam stock technica analysis with mixed GP artificial intelligence numerical computations Quang Long Trần Institute for Northeast Asian Studies, Vietnam Academy of Social Sciences, Hanoi, Vietnam Email:[email protected] ——————————————————————————————————————— Abstract This study employs the Ho Chi Minh Stock Exchange (HOSE) in Vietnam as the empirical setting, applying Genetic Programming (GP) in conjunction with technical indicator analysis to develop a stock price prediction and trading decision-making model. The five-day moving average (MA5) and the Relative Strength Index (RSI) were selected as core features, with visualized charts used to observe price breakouts, overbought conditions, and oversold signals, supplemented by evolutionary rule mining. The findings indicate that MA5 and RSI not only effectively capture short-term market trends and momentum shifts but also provide richer and more interpretable input variables for the model. Furthermore, the proposed method demonstrates adaptability to varying market conditions (bull, sideways, and bear markets), significantly improving both the accuracy of trading signals and the robustness of strategies. Keywords: Vietnamese Stock Market, Genetic Programming, Technical Indicator Analysis, 5-Day Moving Average, Relative Strength Index, Stock Price Trend Prediction, Trading Signal Generation, Emerging Market Investment, Evolutionary Algorithm, Intelligent Investment Decision-Making.