Smoothed Moving Average (SMMA)

get_smma(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 2×N or 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 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

SMMAResults[SMMAResult]

Convergence warning: The first 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.

SMMAResult

name type notes
date datetime Date
smma float, Optional Smoothed 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 SMMA
results = indicators.get_smma(quotes, 20)

About Smoothed Moving Average (SMMA)

Smoothed Moving Average is the average of Close price over a lookback window using a smoothing method. SMMA is also known as modified moving average (MMA) and running moving average (RMA). [Discuss] 💬

image

Sources