Wma.md

title	Weighted Moving Average (WMA)
permalink	/indicators/Wma/
type	moving-average
layout	indicator

{{ page.title }}

get_wma(quotes, lookback_periods, candle_part=CandlePart.CLOSE)

Parameters

name	type	notes
quotes	Iterable[Quote]	Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame
lookback_periods	int	Number of periods (N) in the moving average. Must be greater than 0.
candle_part	CandlePart, default CandlePart.CLOSE	Specify candle part to evaluate. See CandlePart options below.

Historical quotes requirements

You must have at least N periods of quotes to cover the warmup periods.

quotes is an Iterable[Quote] collection of historical price quotes. It should have a consistent frequency (day, hour, minute, etc). See the Guide for more information.

{% include candlepart-options.md %}

Return

WMAResults[WMAResult]

• This method returns a time series of all available indicator values for the quotes provided.
• WMAResults is just a list of WMAResult.
• It always returns the same number of elements as there are in the historical quotes.
• It does not return a single incremental indicator value.
• The first N-1 periods will have None values since there's not enough data to calculate.

WMAResult

name	type	notes
date	datetime	Date
wma	float, Optional	Weighted moving average

Utilities

• .condense()
• .find(lookup_date)
• .remove_warmup_periods()
• .remove_warmup_periods(qty)

See Utilities and Helpers for more information.

Example

from stock_indicators import indicators
from stock_indicators import CandlePart     # Short path, version >= 0.8.1

# This method is NOT a part of the library.
quotes = get_historical_quotes("SPY")

# Calculate 20-period WMA
results = indicators.get_wma(quotes, 20, CandlePart.CLOSE)

About {{ page.title }}

Weighted Moving Average is the linear weighted average of close price over N lookback periods. This also called Linear Weighted Moving Average (LWMA). [Discuss] 💬

Sources

• C# core
• Python wrapper