McGinley Dynamic

get_dynamic(quotes, lookback_periods, k_factor=0.6)

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.
`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.

Returns

DynamicResults[DynamicResult]

This method returns a time series of all available indicator values for the quotes provided.
DynamicResults is just a list of DynamicResult.
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 period will have a None value since there’s not enough data to calculate.

⚞ 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.

DynamicResult

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

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 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