Double Exponential Moving Average (DEMA)
||Iterable[Quote]||Iterable(such as list or an object having
• Need help with pandas.DataFrame?
||int||Number of periods (
Historical quotes requirements
You must have at least
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.
- This method returns a time series of all available indicator values for the
DEMAResultsis just a list of
- 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-1periods will have
Nonevalues since there’s not enough data to calculate.
Convergence warning: The first
2×N+100periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.
||float, Optional||Double exponential moving average|
See Utilities and Helpers for more information.
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]
See related EMA and Triple EMA.