Jurik Moving Average

Jurik Moving Average (JMA) – Precision Smoothing with Adaptive Filtering

📌 Version: This script is written in Pine Script v6, utilizing advanced array handling and dynamic filtering for improved performance.

📌 Overview:
The Jurik Moving Average (JMA) was originally developed by Mark Jurik and is widely recognized for its ability to provide smooth trend-following signals with minimal lag. Unlike traditional moving averages, which suffer from a tradeoff between responsiveness and smoothness, JMA employs an adaptive smoothing algorithm that dynamically adjusts based on market conditions, reducing false signals while maintaining trend accuracy.

This version of JMA has been implemented in Pine Script v6 with enhancements that make it even more efficient for TradingView users. By utilizing advanced array-based calculations, logarithmic scaling, and cycle-based filtering, this implementation delivers an optimized, customizable, and high-performance smoothing indicator.

📌 How It Works:
✅ Adaptive Filtering: Dynamically adjusts smoothing based on price volatility.
✅ Cycle-Based Adjustments: Uses historical price action to fine-tune lag vs. responsiveness.
✅ Advanced Phase Control: Traders can shift the moving average forward or backward to optimize signal alignment.

Unlike existing open-source JMA implementations, this version features:
🔹 Enhanced Array-Based Calculations for better memory management & performance.
🔹 Logarithmic and Square Root Scaling to dynamically adjust phase & smoothing.
🔹 Improved Noise Reduction Techniques to minimize false breakouts.

📌 How to Use:
🔹 Trend Confirmation: Use JMA to validate trend direction and avoid whipsaws.
🔹 Trade Entries & Exits: Combine with price action or momentum indicators for refined entry/exit points.
🔹 Scalping & Swing Trading: Ideal for short-term and long-term strategies due to its adaptability.

📌 Why This Version is Unique:
This JMA expands on standard implementations by incorporating multi-level cycle smoothing, phase correction, and adaptive noise filtering. The result? A more precise, stable, and robust trend indicator that performs better than existing open-source versions.