Double Exponential Moving Average (DEMA)

get_dema(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 3×N or 2×N+100 periods of quotes, whichever is more, to cover the convergence periods. Since this uses a smoothing technique, we recommend you use at least 2×N+250 data points prior to the intended usage date for better precision.

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

DEMAResults[DEMAResult]

This method returns a time series of all available indicator values for the quotes provided.
DEMAResults is just a list of DEMAResult.
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 2×N-1 periods will have None values since there’s not enough data to calculate.

⚞ Convergence warning: The first 2×N+100 periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.

DEMAResult

name	type	notes
`date`	datetime	Date
`dema`	float, Optional	Double exponential 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 DEMA
results = indicators.get_dema(quotes, 20)

About Double Exponential Moving Average (DEMA)

Double exponential moving average of the Close price over a lookback window. [Discuss] 💬

See related EMA and Triple EMA.