Endpoint Moving Average (EPMA)

get_epma(quotes, lookback_periods)

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.

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.

Return

EPMAResults[EPMAResult]

This method returns a time series of all available indicator values for the quotes provided.
EPMAResults is just a list of EPMAResult.
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.

EPMAResult

name	type	notes
`date`	datetime	Date
`epma`	float, Optional	Endpoint moving average

Utilities

See Utilities and Helpers for more information.

Example

from stock_indicators import indicators

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

# Calculate 20-period EPMA
results = indicators.get_epma(quotes, 20)

About Endpoint Moving Average (EPMA)

Endpoint Moving Average (EPMA), also known as Least Squares Moving Average (LSMA), plots the projected last point of a linear regression lookback window. [Discuss] 💬