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An intelligent market making strategy in algorithmic trading

Frontiers of Computer Science 2014年第4期8卷 596-608页

作者： Xiaodong LI Xiaotie DENG Shanfeng ZHU Feng WANG Haoran XIE Department of Computer Science City University of Hong Kong Hong Kong China AIMS Lab Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai 200240 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai 200433 China School of Computer Science Fudan University Shanghai 200433 China State Key Lab of Software Engineering School of Computer Science Wuhan University Wuhan 430072 China Department of Computer Science Hong Kong Baptist University Hong Kong China

market making （MM） strategies have played an important role in the electronic stock market. However, the MM strategies without any forecasting power are not safe while trading. In this paper, we design and implement a twotier framework, which includes a trading signal generator based on a supervised learning approach and an event-driven MM strategy. The proposed generator incorporates the information within order book microstructure and market news to provide directional predictions. The MM strategy in the second tier trades on the signals and prevents itself from profit loss led by market trending. Using half a year price tick data from Tokyo Stock Exchange （TSE） and Shanghai Stock Exchange （SSE）, and corresponding Thomson Reuters news of the same time period, we conduct the back-testing and simulation on an industrial near-to-reality simulator. From the empirical results, we find that 1） strategies with signals perform better than strategies without any signal in terms of average daily profit and loss （PnL） and sharpe ratio （SR）, and 2） correct predictions do help MM strategies readjust their quoting along with market trending, which avoids the strategies triggering stop loss procedure that further realizes the paper loss.

关键词： algorithmic trading market making strategy or- der book microstructure news impact analysis market simulation

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