英文标题：
《Trend without hiccups: a Kalman filter approach》
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作者：
Eric Benhamou
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最新提交年份：
2018
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英文摘要：
  Have you ever felt miserable because of a sudden whipsaw in the price that triggered an unfortunate trade? In an attempt to remove this noise, technical analysts have used various types of moving averages (simple, exponential, adaptive one or using Nyquist criterion). These tools may have performed decently but we show in this paper that this can be improved dramatically thanks to the optimal filtering theory of Kalman filters (KF). We explain the basic concepts of KF and its optimum criterion. We provide a pseudo code for this new technical indicator that demystifies its complexity. We show that this new smoothing device can be used to better forecast price moves as lag is reduced. We provide 4 Kalman filter models and their performance on the SP500 mini-future contract. Results are quite illustrative of the efficiency of KF models with better net performance achieved by the KF model combining smoothing and extremum position.
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中文摘要：
你有没有因为价格突然下跌而导致不幸的交易而感到痛苦？为了消除这种噪音，技术分析师使用了各种类型的移动平均值（简单、指数、自适应或使用奈奎斯特标准）。这些工具可能表现得不错，但我们在本文中表明，由于卡尔曼滤波器（KF）的最优滤波理论，这一点可以得到显著改善。我们解释了KF的基本概念及其优化准则。我们为这个新的技术指标提供了一个伪代码，从而揭开了它的复杂性。我们表明，这种新的平滑设备可以用来更好地预测价格变动，因为滞后减少。我们提供了4个卡尔曼滤波器模型及其在SP500小型期货合约上的性能。结果很好地说明了KF模型的效率，通过将平滑和极值位置相结合的KF模型实现了更好的净性能。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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