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Trend without hiccups: a Kalman filter approach

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  • Eric Benhamou

Abstract

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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  • Eric Benhamou, 2018. "Trend without hiccups: a Kalman filter approach," Papers 1808.03297, arXiv.org.
    • Handle: RePEc:arx:papers:1808.03297
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    Cited by:

    1. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & François Chareyron, 2021. "Distinguish the indistinguishable: a Deep Reinforcement Learning approach for volatility targeting models," Working Papers hal-03202431, HAL.
    2. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & Franc{c}ois Chareyron, 2021. "Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting," Papers 2104.10483, arXiv.org, revised Apr 2021.
    3. Eric Benhamou & Beatrice Guez, 2021. "Computation of the marginal contribution of Sharpe ratio and other performance ratios," Working Papers hal-03189299, HAL.
    4. Eric Benhamou, 2021. "Distribution and statistics of the Sharpe Ratio," Working Papers hal-03207169, HAL.

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