Discussing the article: "The Kalman Filter for Forex Mean-Reversion Strategies"

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The Kalman filter is a recursive algorithm used in algorithmic trading to estimate the true state of a financial time series by filtering out noise from price movements. It dynamically updates predictions based on new market data, making it valuable for adaptive strategies like mean reversion. This article first introduces the Kalman filter, covering its calculation and implementation. Next, we apply the filter to a classic mean-reversion forex strategy as an example. Finally, we conduct various statistical analyses by comparing the filter with a moving average across different forex pairs.

The Kalman filter, introduced by Rudolf E. Kalman in 1960, is an optimal recursive estimator used for tracking and predicting dynamic systems. Originally developed for aerospace and control systems, it has been widely applied in finance, robotics, and signal processing. The filter operates in two steps: a prediction step, where it estimates the system’s next state, and an update step, where it refines the estimate based on new observations while minimizing noise. 

In the field of algorithmic trading, one can simply see it as a common regime filter that traders normally use, akin to moving average or linear regression models. The Kalman filter adapts dynamically to new data, reduces noise, and efficiently updates estimates in real-time, making it effective for detecting market regime shifts. However, it assumes linear dynamics, requires careful parameter tuning, may lag in detecting abrupt changes, and is computationally more complex than simpler filters like moving averages.

Some common usages for using the Kalman filter in algorithmic trading:

  • Mean Reversion Trading: Using current price compared to estimated price as an entry filter.
  • Pairs Trading: Dynamically estimates the spread between correlated assets and adjusts hedge ratios based on changing market conditions.
  • Trend Following: Filters short-term noise to detect long-term price trends more accurately.
  • Volatility Estimation: Provides adaptive estimates of market volatility for risk management and position sizing.


    Author: Zhuo Kai Chen

     

    Like your presentation.

    Thank you very much. Please keep it up.

      
    Low Q and moderate R yield stable predictions, while high Q and low R make the filter more reactive but noisier.

    What is your view in optimizing these inputs (Q and R)?

    How would you decide their values for the EA?

     
    Too Chee Ng #:

    Like your presentation.

    Thank you very much. Please keep it up.

    Thank you! I'm will keep improving my article quality as I'm learning more.

     
    Too Chee Ng #:

    What is your view in optimizing these inputs (Q and R)?

    How would you decide their values for the EA?

    Great question! I would say do not try too hard to optimize the values specifically. Try to select some standard values and optimize the threshold rather than optimize the indicator parameters. I would recommend you choose the measurement variance from 1000, 100, and 10, and choose the process variance from 1, 0.1 and 0.01.

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