Radial Basis Kernal ATR [BackQuant]Radial Basis Kernel ATR
The Radial Basis Kernel ATR is a trading indicator that combines the classic Average True Range (ATR) with advanced Radial Basis Function (RBF) kernel smoothing . This innovative approach creates a highly adaptive and precise tool for detecting volatility, identifying trends, and providing dynamic support and resistance levels.
With its configurable parameters and ability to adjust to market conditions, this indicator offers traders a robust framework for making informed decisions across various assets and timeframes.
Key Feature: Radial Basis Function Kernel Smoothing
The Radial Basis Function (RBF) kernel is at the heart of this indicator, applying sophisticated mathematical techniques to smooth price data and calculate an enhanced version of ATR. By weighting data points dynamically, the RBF kernel ensures that recent price movements are given appropriate emphasis without overreacting to short-term noise.
The RBF kernel uses a gamma factor to control the degree of smoothing, making it highly adaptable to different asset classes and market conditions:
Gamma Factor Adjustment :
For low-volatility data (e.g., indices), a smaller gamma (0.05–0.1) ensures smoother trends and avoids overly sharp responses.
For high-volatility data (e.g., cryptocurrencies), a larger gamma (0.1–0.2) captures the increased price fluctuations while maintaining stability.
Experimentation is Key : Traders are encouraged to backtest and visually compare different gamma values to find the optimal setting for their specific asset and strategy.
The gamma factor dynamically adjusts based on the variance of the source data, ensuring the indicator remains effective across a wide range of market conditions.
Average True Range (ATR) with Dynamic Bands
The ATR is a widely used volatility measure that captures the degree of price movement over a specific period. This indicator enhances the traditional ATR by integrating the RBF kernel, resulting in a smoothed and adaptive ATR calculation.
Dynamic bands are created around the RBF kernel output using a user-defined ATR factor , offering valuable insights into potential support and resistance zones. These bands expand and contract based on market volatility, providing a visual representation of potential price movement.
Moving Average Confluence
For additional confirmation, the indicator includes the option to overlay a moving average on the smoothed ATR. Traders can choose from several moving average types, such as EMA , SMA , or Hull , and adjust the lookback period to suit their strategy. This feature helps identify broader trends and potential confluence areas, making the indicator even more versatile.
Long and Short Trend Detection
The indicator provides long and short signals based on the directional movement of the smoothed ATR:
Long Signal : Triggered when the ATR crosses above its previous value, indicating bullish momentum.
Short Signal : Triggered when the ATR crosses below its previous value, signaling bearish momentum.
These trend signals are visually highlighted on the chart with green and red bar coloring (optional), providing clear and actionable insights.
Customization Options
The Radial Basis Kernel ATR offers extensive customization options, allowing traders to tailor the indicator to their preferences:
RBF Kernel Settings
Source : Select the price data (e.g., close, high, low) used for the kernel calculation.
Kernel Length : Define the lookback period for the RBF kernel, controlling the smoothing effect.
Gamma Factor : Adjust the smoothing sensitivity, with smaller values for smoother trends and larger values for responsiveness.
ATR Settings
ATR Period : Set the period for ATR calculation, with shorter periods capturing more short-term volatility and longer periods providing a broader view.
ATR Factor : Adjust the scaling of ATR bands for dynamic support and resistance levels.
Confluence Settings
Moving Average Type : Choose from various moving average types for additional trend confirmation.
Moving Average Period : Define the lookback period for the moving average overlay.
Visualization
Trend Coloring : Enable or disable bar coloring based on trend direction (green for long, red for short).
Background Highlighting : Add optional background shading to emphasize long and short trends visually.
Line Width : Customize the thickness of the plotted ATR line for better visibility.
Alerts and Automation
To help traders stay on top of market movements, the indicator includes built-in alerts for trend changes:
Kernel ATR Trend Up : Triggered when the ATR indicates a bullish trend.
Kernel ATR Trend Down : Triggered when the ATR signals a bearish trend.
These alerts ensure traders never miss important opportunities, providing timely notifications directly to their preferred device.
Suggested Gamma Values
The effectiveness of the gamma factor depends on the asset type and the selected kernel length:
Low Volatility Assets (e.g., indices): Use a smaller gamma factor (approximately 0.05–0.1) for smoother trends.
High Volatility Assets (e.g., crypto): Use a larger gamma factor (approximately 0.1–0.2) to capture sharper price movements.
Experimentation : Fine-tune the gamma factor using backtests or visual comparisons to optimize for specific assets and strategies.
Trading Applications
The Radial Basis Kernel ATR is a versatile tool suitable for various trading styles and strategies:
Trend Following : Use the smoothed ATR and dynamic bands to identify and follow trends with confidence.
Reversal Trading : Spot potential reversals by observing interactions with dynamic ATR bands and moving average confluence.
Volatility Analysis : Analyze market volatility to adjust risk management strategies or position sizing.
Final Thoughts
The Radial Basis Kernel ATR combines advanced mathematical techniques with the practical utility of ATR, offering traders a powerful and adaptive tool for volatility analysis and trend detection. Its ability to dynamically adjust to market conditions through the RBF kernel and gamma factor makes it a unique and indispensable part of any trader's toolkit.
By combining sophisticated smoothing , dynamic bands , and customizable visualization , this indicator enhances the ability to read market conditions and make more informed trading decisions. As always, backtesting and incorporating it into a broader strategy are recommended for optimal results.
Kernal
RationalQuadraticKernelFunctionDescription:
An optimised library for non-repainting Rational Quadratic Kernel Library. Added lookbackperiod and a validation to prevent division by zero.
Thanks to original author jdehorty.
Usage:
1. Import the library into your Pine Script code using the library function.
import vinayakavajiraya/RationalQuadraticKernelFunction/1
2. Call the Main Function:
Use the rationalQuadraticKernel function to calculate the Rational Quadratic Kernel estimate.
Provide the following parameters:
`_src` (series float): The input series of float values, typically representing price data.
`_lookback` (simple int): The lookback period for the kernel calculation (an integer).
`_relativeWeight` (simple float): The relative weight factor for the kernel (a float).
`startAtBar` (simple int): The bar index to start the calculation from (an integer).
rationalQuadraticEstimate = rationalQuadraticKernel(_src, _lookback, _relativeWeight, startAtBar)
3. Plot the Estimate:
Plot the resulting estimate on your TradingView chart using the plot function.
plot(rationalQuadraticEstimate, color = color.red, title = "Rational Quadratic Kernel Estimate")
Parameter Explanation:
`_src`: The input series of price data, such as 'close' or any other relevant data.
`_lookback`: The number of previous bars to consider when calculating the estimate. Higher values capture longer-term trends.
`_relativeWeight`: A factor that controls the importance of each data point in the calculation. A higher value emphasizes recent data.
`startAtBar`: The bar index from which the calculation begins.
Example Usage:
Here's an example of how to use the library to calculate and plot the Rational Quadratic Kernel estimate for the 'close' price series:
//@version=5
library("RationalQuadraticKernelFunctions", true)
rationalQuadraticEstimate = rationalQuadraticKernel(close, 11, 1, 24)
plot(rationalQuadraticEstimate, color = color.orange, title = "Rational Quadratic Kernel Estimate")
This example calculates the estimate for the 'close' price series, considers the previous 11 bars, assigns equal weight to all data points, and starts the calculation from the 24th bar. The result is plotted as an orange line on the chart.
Highly recommend to customize the parameters to suit your analysis needs and adapt the library to your trading strategies.
