This article delves into the syntax, application, and key considerations of this function, providing a clear guide for incorporating it into your Pine Script strategies.

## Syntax Overview

The `ta.percentile_linear_interpolation()`

function is structured as follows:

ta.percentile_linear_interpolation(dataStream, reviewSpan, targetPercent) → series float

### Arguments Explained

**dataStream (series int/float):**This represents the series of values you wish to analyze. It can be any series of numbers, such as closing prices, trading volumes, or even computed indicators.**reviewSpan (series int):**Specifies the number of bars to look back from the current bar to calculate the percentile. This length determines the dataset’s size over which the percentile is computed.**targetPercent (simple int/float):**The percentile you want to calculate, expressed as a number from 0 to 100. This defines the position within the sorted data set for which the value is sought.

### Return Value

- The function returns the value at the P-th percentile of the
`dataStream`

series for the last`reviewSpan`

bars, calculated via linear interpolation between the two nearest ranks.

## Example

Let’s apply `ta.percentile_linear_interpolation()`

to a simple scenario: calculating the 90th percentile of the closing prices over the past 20 bars.

//@version=5 indicator("90th Percentile Closing Price", overlay=true) closingPrices = close length = 20 percentile = 90 percentileValue = ta.percentile_linear_interpolation(closingPrices, length, percentile) plot(percentileValue, color=color.red, linewidth=2, title="90th Percentile Line")

### Code Walkthrough

**Indicator Declaration:**`//@version=5`

: This line specifies the version of Pine Script being used, in this case, version 5. It’s essential for compatibility and accessing the latest features.`indicator("90th Percentile Closing Price", overlay=true)`

: This line declares a new indicator titled “90th Percentile Closing Price”. The`overlay=true`

argument means that the indicator will be plotted directly on the price chart, overlaying the price action.

**Variable Initialization:**`closingPrices = close`

: Initializes a variable`closingPrices`

to hold the series of closing prices (`close`

). This variable is then used as the source data for the percentile calculation.`length = 20`

: Sets the`length`

variable to 20, indicating that the percentile calculation will consider the past 20 bars.`percentile = 90`

: Assigns the value 90 to the`percentile`

variable, specifying that the calculation aims to find the 90th percentile value.

**Percentile Calculation:**`percentileValue = ta.percentile_linear_interpolation(closingPrices, length, percentile)`

: This line is where the percentile calculation happens. The`ta.percentile_linear_interpolation`

function is called with three arguments: the series of closing prices (`closingPrices`

), the number of bars to look back (`length`

), and the target percentile (`percentile`

). The function returns the 90th percentile value of the closing prices over the last 20 bars, which is then stored in the`percentileValue`

variable.

**Plotting the Percentile Value:**`plot(percentileValue, color=color.red, linewidth=2, title="90th Percentile Line")`

: This line plots the calculated 90th percentile value on the chart. The`percentileValue`

is plotted with specific styling options: the line color is set to red (`color=color.red`

), the line width is set to 2 (`linewidth=2`

), and the title of the plot is “90th Percentile Line” (`title="90th Percentile Line"`

). This makes the 90th percentile line easily identifiable on the chart.

## Key Features and Takeaways

**Function Useability:**The`ta.percentile_linear_interpolation()`

function is versatile and can be applied to any numerical series, offering a detailed percentile calculation that goes beyond simple ranking.**Syntax and Application:**Understanding the syntax is crucial for accurate implementation. The function’s parameters allow for dynamic analyses over variable periods and conditions.**Interpolation vs. Actual Data Points:**It’s important to note that the function may return values not present in the original data, offering interpolated insights that could be more relevant for certain analyses.

This exploration of `ta.percentile_linear_interpolation()`

in Pine Script highlights its utility in financial analysis, providing a foundation for incorporating sophisticated statistical measures into your trading strategies.