Understanding ta.wma() Function in Pine Script

In this tutorial, we delve into the utilization of the ta.wma() function within Pine Script, an essential tool for traders and analysts using the TradingView platform. The ta.wma() function computes the weighted moving average (WMA) of a given data series over a specified number of periods, with an emphasis on recent data points by assigning them greater weights. This method contrasts with simple moving averages (SMA) by providing a more responsive measure to recent price changes.

Introduction to ta.wma()

Syntax

ta.wma(source, length) → series float

Arguments

• source (series int/float): The series of values to process. This can be any series of numbers, such as the closing price of a stock.
• length (series int): The number of bars, or periods, over which to calculate the WMA.

Example

//@version=5
indicator("Custom WMA Example")
plot(ta.wma(close, 15), "Weighted Moving Average")

// Alternative implementation using Pine Script, less efficient but educational
customWma(series, periodLength) =>
    normalizationFactor = 0.0
    weightedSum = 0.0
    for index = 0 to periodLength - 1
        weight = (periodLength - index) * periodLength
        normalizationFactor := normalizationFactor + weight
        weightedSum := weightedSum + series[index] * weight
    weightedSum / normalizationFactor
plot(customWma(close, 15), "Custom WMA")

Deep Dive into the Example

1. Indicator Declaration:
   • //@version=5: Specifies the version of Pine Script used, which is version 5 in this case. This line is essential for compatibility and feature support.
   • indicator("Custom WMA Example"): Declares a new indicator with the name “Custom WMA Example”. This name appears on the chart where the indicator is applied.
2. Built-in WMA Calculation and Plotting:
   • plot(ta.wma(close, 15), "Weighted Moving Average"): This line does two things:
     • It calculates the Weighted Moving Average of the close price over the past 15 bars using Pine Script’s built-in ta.wma() function.
     • It plots this calculated WMA on the chart with the label “Weighted Moving Average”.
3. Custom WMA Function Definition:
   • customWma(series, periodLength) =>: Defines a custom function named customWma that takes two parameters:
     • series: A series of values (e.g., closing prices) to calculate the WMA on.
     • periodLength: The number of bars to look back for the calculation.
   • Inside this function, two variables are initialized to zero: normalizationFactor and weightedSum. These variables are used to calculate the normalization factor and the weighted sum of the series values, respectively.
4. For Loop for WMA Calculation:
   • The for loop for index = 0 to periodLength - 1 iterates over each bar in the specified period length, calculating the weight for each bar and accumulating the weighted sum and normalization factor.
   • weight = (periodLength - index) * periodLength: Calculates the weight of each bar. The weight decreases in arithmetic progression as the index increases.
   • normalizationFactor := normalizationFactor + weight: Accumulates the total weight to normalize the weighted sum.
   • weightedSum := weightedSum + series[index] * weight: Calculates the weighted sum of the series values.
5. WMA Calculation and Return:
   • weightedSum / normalizationFactor: After the loop, the function calculates the final WMA by dividing the weighted sum by the normalization factor. This result is the output of the customWma function.
6. Plotting the Custom WMA:
   • plot(customWma(close, 15), "Custom WMA"): This line plots the WMA calculated by the customWma function on the chart with the label “Custom WMA”. It uses the same close price series and period length as the built-in function for comparison.

Key Features and Takeaways

• Function Usability: ta.wma() provides a simple and efficient way to include weighted moving averages in your trading strategies or analytical models on TradingView.
• Syntax and Application: The function syntax is straightforward, requiring only the data series and the length of the period for calculation.
• Manual Calculation Insight: The manual implementation offers insight into the underlying calculation of WMA, which can be educational for users seeking to understand the mechanics of weighted averages in financial analysis.