Kalman Trend Estimator [KTE] -FibonacciFlux OVERVIEW
Optimal linear trend filter using scalar Kalman filtering. Treats price as a noisy
measurement of a hidden trend state and recursively computes the optimal estimate. The Kalman
gain adapts automatically — no manual threshold tuning required.
HOW IT WORKS
The filter balances two noise sources:
- Process Noise (Q): how fast the true trend can change
- Measurement Noise (R): how noisy price observations are
The Kalman gain adjusts every bar:
- During clean trends: gain tightens, tracks price closely
- During choppy markets: gain loosens, smooths out noise
Innovation magnitude (prediction error) serves as a built-in regime classifier: small
innovations = trending, large innovations = noisy/ranging.
FEATURES
- Adaptive Kalman gain — zero manual tuning
- Innovation-based regime coloring (green = trend, red = noise)
- Confidence bands derived from estimation uncertainty (P matrix)
- Signal dots at trend direction changes
- Works on all timeframes and instruments
SETTINGS
Process Noise Q: Controls trend tracking speed. Higher = faster adaptation. Default 0.01.
Measurement Noise R: Controls price smoothing. Higher = smoother output. Default 1.0.
Show Bands: Toggle confidence bands on/off.
Based on: R.E. Kalman (1960), "A New Approach to Linear Filtering and Prediction Problems",
Journal of Basic Engineering.
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