I'm looking for someone that feels the have developed a good working Kalman Filter Model to use in the fx market?
- Forex Books
- Discussion of article "Using the Kalman Filter for price direction prediction"
- implementing Kalman filter
catsally1: I'm looking for someone that feels the have developed a good working Kalman Filter Model to use in the fx market?
How about letting your fingers do the walking with the "Search" facility: https://www.mql5.com/en/search#!keyword=Kalman%20Filter
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Sergey Golubev, 2025.02.27 16:49
The Kalman Filter for Forex Mean-Reversion Strategies

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.
This article presents that practical solution: a scalar state‑space Kalman smoother whose blending weight is the optimally computed Kalman Gain and whose process and measurement noise variances are estimated online from rolling windows of returns and price deviations.
A Practical Kalman Filter Price Smoother in MQL5: Adaptive Noise Estimation Without External Libraries
- 2026.06.22
- www.mql5.com
Fixed-weight moving averages introduce regime-insensitive lag. This work presents an adaptive scalar Kalman filter indicator in native MQL5 that estimates process noise Q from rolling return variance and measurement noise R from rolling price variance, with floor clamps for stability, and recomputes the Kalman Gain on every bar. The chart-overlay output is benchmarked against a 20-period EMA using MAE, RMSE, lag, and smoothness metrics to quantify tracking and noise suppression.
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