Using an Autoregressive Kalman Filter to Estimate Price Direction

Summary

The article introduces the Kalman filter as a way to smooth noisy price observations and estimate an underlying market movement. It explains the filter’s two stages: predicting a state from a process model, then updating that estimate using the latest observation and an adaptive gain based on estimated process and measurement uncertainty.

For its trading implementation, the author builds an autoregressive model from historical close-price ratios and uses its coefficients in a Kalman filter, then develops an indicator and an Expert Advisor. The reported test for a selected period shows a profit factor of 1.56, but the text also identifies losing sequences during range-bound movement and exits that occur too late. The result is period-specific and does not establish robustness or live profitability. The article presents the approach as an experimental example requiring further work, rather than a ready-made trading system.

Key ideas

• The Kalman filter alternates between predicting a system state and correcting it with new observations.
• Its gain controls how much the updated estimate relies on the prediction versus the latest measurement.
• The implementation uses an autoregressive model of price ratios to define the state transition behavior.
• The reported test had a profit factor of 1.56 for the selected period, with losses in flat markets and delayed exits identified as weaknesses.
• The article treats the system as an example for further development, not proof of dependable live performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.