Kent Directional Filter

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🧭 Kent Directional Filter
Author: GabrielAmadeusLau
Type: Filter

📖 What It Is
The Kent Directional Filter is a directionality-sensitive smoothing tool inspired by the Kent distribution, a probability model used to describe directional and elliptical shapes on a sphere. In this context, it's repurposed for analyzing the angular trajectory of price movements and smoothing them for actionable insights.

It’s ideal for:

Detecting directional bias with probabilistic weighting

Enhancing momentum or trend-following systems

Filtering non-linear price action

🔬 How It Works
Price Angle Estimation:
Computes a rough angular shift in price using atan(src - src[1]) to estimate direction.

Kent Distribution Weighting:

κ (kappa) controls concentration strength (how sharply it prefers a direction).

β (beta) controls ellipticity (bias toward curved vs. linear moves).

These parameters influence how strongly the indicator favors movements at ~45° angles, simulating a directional “lens.”

Smoothing:

A Simple Moving Average (SMA) is applied over the raw directional probabilities to reduce noise and highlight the underlying trend signal.

⚙️ Inputs
Source: Price series used for angle calculation (default: close)

Smoothing Length: Window size for the moving average

Pi Divisor: Pi / 4 would be 45 degrees, you can change the 4 to 3, 2, etc.

Kappa (κ): Controls how focused the directionality is (higher = sharper filter)

Beta (β): Adds curvature sensitivity; higher values accentuate asymmetrical moves

🧠 Tips for Best Results
Use κ = 1–2 for moderate directional filtering, and β = 0.3–0.7 for smooth elliptical bias.

Combine with volume-based indicators to confirm breakout strength.

Works best in higher timeframes (1h–1D) to capture macro directional structure.

I might revisit this.

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