McGinley Dynamic

get_dynamic(quotes, lookback_periods, k_factor=0.6)


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.
k_factor float, default 0.6 Range adjustment factor (K). Must be greater than 0.

Historical quotes requirements

You must have at least 2 periods of quotes, to cover the initialization periods. Since this uses a smoothing technique, we recommend you use at least 4×N 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.

Pro tips

Use a k_factor value of 1 if you do not want to adjust the N value.

McGinley suggests that using a K value of 60% (0.6) allows you to use a N equivalent to other moving averages. For example, DYNAMIC(20,0.6) is comparable to EMA(20); conversely, DYNAMIC(20,1) uses the raw 1:1 N value and is not equivalent.



Convergence warning: The first 4×N periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.


name type notes
date datetime Date
dynamic float, Optional McGinley Dynamic


See Utilities and Helpers for more information.


from stock_indicators import indicators

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

# calculate 14-period McGinley Dynamic
results = indicators.get_dynamic(quotes, 14)

About McGinley Dynamic

Created by John R. McGinley, the McGinley Dynamic is a more responsive variant of exponential moving average. [Discuss] 💬

chart for McGinley Dynamic