| Summary: | 碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 106 === This study attempts to analyze the dynamic behavior of the financial trading market based on behavioral economics and price analysis theory, and attempts to establish trading strategies using machine learning algorithms. We first tried to find suitable entry opportunities from the event study. We used K-means to cluster prices and volume, and based on the theory of excessive optimism and excessive panic in the behavioral economics market, we took the increase in volume and the fall in volume as an event, and we normalized the volume before clustering. In the normalization calculation, different experiments were designed to analyze the price changes before and after the events they found. In the part of the timing of closing the position, we choose reinforcement learning algorithm. The concept of reinforcement learning is based on observation of the environment and interaction with the environment to obtain reward, and has the characteristics of delayed reward. We have mapped this algorithm to the trading strategy. In the decision-making, we hope to train a model and find a good time for closing the position. We focus on the Observation design experiments in the reinforcement learning and analyze the effects of different observations on the model. Finally, we will backtest our established trading strategies and buy and hold strategies and compare their performance.
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