Обновлено
Description:
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

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.
Информация о релизе:
v2

Change log:
1. Removed Starting window
2. The size is determined based on a user-defined value, providing greater flexibility in managing the data for the bars, with the possibility of exceeding the value 2000 if necessary.
removed few variables

Parameters:
src (float)
lookback (simple int)
relativeWeight (simple float)
Информация о релизе:
v3

Change log:
1. Removed default plot (to avoid during importing into indicator)
2. Updated an example with full implementation of library.

Example:
```// @version=5
indicator(title="Relational Quadratic Kernel Estimate , shorttitle="RQKE-V", overlay=true, timeframe = "", timeframe_gaps = false)

// Import (latest) external library
import vinayakavajiraya/RationalQuadraticKernelFunction/2 as kernel

// Input parameters for the kernel estimate
length = input.int(title="Lookback Window", defval=10, minval=1, step=1, group="Kernel Estimate", tooltip="The number of previous bars to consider when calculating the rationalQuadraticEstimate. Higher values capture longer-term trends.")
relativeWeight = input.float(title="Relative Weight", defval=8, minval=1, step=1, group="Kernel Estimate", tooltip="A factor that controls the importance of each data point in the calculation. A higher value emphasizes recent data.")
source = input.source(title="Source", defval=close, group="Kernel Estimate"), // Data source (e.g., close price) for the calculation

// Calculate the rational quadratic estimate using the imported library
source,           // Data source for calculation
length,           // Lookback window
relativeWeight    // Relative weight of data points
)

// Plot the rational quadratic estimate on the chart