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Intelligent stock trading system by turning point confirming and probabilistic reasoning

机译：通过转折点确认和概率推理的智能股票交易系统

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摘要

Financial engineering such as trading decision is an emerging research area and also has great commercial potentials. A successful stock buying/selling generally occurs near price trend turning point. Traditional technical analysis relies on some statistics (i.e. technical indicators) to predict turning point of the trend. However, these indicators can not guarantee the accuracy of prediction in chaotic domain. In this paper, we propose an intelligent financial trading system through a new approach: learn trading strategy by probabilistic model from high-level representation of time series - turning points and technical indicators. The main contributions of this paper are two-fold. First, we utilize high-level representation (turning point and technical indicators). High-level representation has several advantages such as insensitive to noise and intuitive to human being. However, it is rarely used in past research. Technical indicator is the knowledge from professional investors, which can generally characterize the market. Second, by combining high-level representation with probabilistic model, the randomness and uncertainty of chaotic system is further reduced. In this way, we achieve great results (comprehensive experiments on S&P500 components) in a chaotic domain in which the prediction is thought impossible in the past.
机译：金融工程（例如交易决策）是一个新兴的研究领域，也具有巨大的商业潜力。成功的股票买卖通常发生在价格趋势转折点附近。传统的技术分析依靠一些统计数据（即技术指标）来预测趋势的转折点。但是，这些指标不能保证混沌域中预测的准确性。在本文中，我们通过一种新方法提出了一种智能金融交易系统：通过概率模型从时间序列的高级表示形式（转折点和技术指标）中学习交易策略。本文的主要贡献有两个方面。首先，我们利用高级表示（拐点和技术指标）。高级表示具有一些优点，例如对噪声不敏感和对人类直观。但是，在过去的研究中很少使用它。技术指标是专业投资者的知识，通常可以描述市场特征。其次，通过将高级表示与概率模型相结合，进一步减少了混沌系统的随机性和不确定性。这样，我们就可以在过去认为无法进行预测的混沌领域中取得出色的结果（针对S＆P500组件的综合实验）。

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