KalmanFilter
Kalman Filter is a recursive algorithm that estimates the underlying state of a system from noisy observations.
To use the indicator, you need to use the KalmanFilter class.
Description
The Kalman Filter applies a prediction-correction cycle to smooth price data and reduce market noise. It adapts dynamically as new information becomes available, making it useful for tracking trends in volatile markets.
Parameters
- ProcessNoise – expected variance in the underlying process.
- ObservationNoise – expected variance in the observed data.
Calculation
At each step the filter performs:
- Prediction of the next state based on the previous estimate.
- Update of this prediction using the newest price observation and the noise estimates.
This yields an optimized estimate that reacts quickly to price changes while filtering out short-term fluctuations.
