Name: Adaptive Trailing System
Price: 1 USD
Availability: InStock
Rating: 4.70 (20 reviews)

Adaptive Trailing System

อินดิเคเตอร์

102 ดาวน์โหลด

Version 1.0, Nov 2025

Windows, Mac

"Adaptive Trailing System" ภาพที่อัปโหลด

pinescriptlabs

ตั้งแต่ 03/10/2024

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Adaptive Trailing System 🚀

The Adaptive Trailing System is an advanced adaptive trailing stop that uses logarithmic normalized volatility to automatically adjust profit protection levels. 📊 Unlike traditional trailing stops with fixed distances, this system calculates dynamic distances based on the market's actual volatility, constantly adapting to changing conditions. 🔄

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Unique Adaptive Trailing Algorithm ⚙️

The system uses a statistical method that never generates the same trailing distances:
• Calculates the True Range of the price over a configurable period.
• Applies a logarithmic transformation to these values to normalize the distribution.
• Calculates the mean and standard deviation of these logarithmic values.
• Generates a unique offset distance based on volatility levels (0, 1, 2, or 3 standard deviations). 🎯
• The trailing stop is continuously adjusted using the typical price (average of High, Low, Close).

Result: Every market moment has its own trailing distance, calculated according to the specific volatility of that context. 🤖

Technical Foundation: Logarithmic Normalized Volatility 🧠

Why a logarithmic transformation? 🤔
Market volatility does not follow a normal (Gaussian) distribution; it tends to have extreme values. The logarithmic transformation:

Normalizes the distribution: Converts skewed data into a more symmetrical distribution.
Stabilizes variance: Reduces the impact of extreme volatility spikes.
Enables statistical comparison: Makes it possible to use standard deviation meaningfully.

Offset Calculation Process 🔢

Period True Range: Measures the maximum range, considering the High-Low of the period and gaps with the previous close.
Natural Logarithm: Applies ln(True Range) to normalize.
Normalization Window: Maintains the last N logarithmic values (normalization period).
Logarithmic Mean: Average of all ln(TR) values in the window.
Standard Deviation: Calculates the dispersion of the logarithmic values.
Volatility Level: Multiplies the deviation by 0, 1, 2, or 3 based on the configuration.
Final Offset: Offset = e^(mean + level × deviation) - converts back to the price scale. 💡

How It Works? ⚡

Bidirectional Trailing Mechanics

The system operates in two alternating modes:

BEARISH Mode (Short): 📉
• Trailing stop is placed ABOVE the price.
• It updates downward when the price falls (never rises).
• Reversal signal when the price CLOSES above the trailing stop.

BULLISH Mode (Long): 📈
• Trailing stop is placed BELOW the price.
• It updates upward when the price rises (never falls).
• Reversal signal when the price CLOSES below the trailing stop.

Reversal Signals:

• A circular dot marks the trend change (entry of a new position).
• Green color = New bullish trend (). ✅
• Red color = New bearish trend (). 🔴

Parameters and Impact 🎛️

Calculation Period (10)

Function: Defines how many bars are used to calculate the True Range.
↑ Higher (20-50): Measures longer-term volatility, smoother, less sensitive.
↓ Lower (5-8): Measures short-term volatility, more reactive, detects rapid changes.

Normalization Period (100)

Function: Window of logarithmic values to calculate the mean and standard deviation.
↑ Higher (200-500): More stable normalization, gradual changes in offset.
↓ Lower (50-80): More reactive normalization, offset adapts faster.
Critical Impact: This parameter determines how "memorized" the historical volatility context is. 🧠

Volatility Level (Level 2)

Function: Number of standard deviations applied to the offset calculation.
Level 0 (0σ): Offset = logarithmic mean = tightest distance, maximum sensitivity. 🎯
Level 1 (1σ): Offset includes 1 deviation = moderate distance, captures ~68% of variation.
Level 2 (2σ): Offset includes 2 deviations = wider distance, captures ~95% of variation (balanced). ⚖️
Level 3 (3σ): Offset includes 3 deviations = very wide distance, captures ~99% of variation, maximum tolerance. 🛡️