Future market forecasting is a challenging task in the financial time-series study field. Factors such as the economic environment, political policies, and market news affect it. Although only a few people can profit from it, many researchers and professional investors are eager to spend considerable effort studying the future market in an attempt to develop profitable methods. Certainly, many investment strategies exist in the market including traditional technical analysis and modern emerging artificial intelligence analysis. However, most methods are too complicated or heavily require investors to select and analyze many technical indicators. Moreover, most individual investors do not understand financial statements, the meaning of various technical indicators, and their influence on the future market. These non-professional investors are inevitably in need of a simple and intelligible investment method. Hence, this study proposes a head-and-shoulder combined pattern recognition model based on perceptually important point identification matching (PIP), template matching, and the floating weighted method using two basic technical indicators (5MA and 20MA). A four-year period (2017~2020) of the Crude Oil, NASDAQ100, and S&P500 index was analyzed as the experimental dataset to validate the proposed model. The experimental results show that the proposed model is effective in forecasting accuracy and can be applied to develop investment strategies.