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]

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_history_from_feed("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] 💬

image

See related EMA and Triple EMA.

Sources