Triple Exponential Moving Average (TEMA)

get_tema(quotes, lookback_periods)


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 4×N or 3×N+100 periods of quotes, whichever is more, to cover the warmup periods. Since this uses a smoothing technique, we recommend you use at least 3×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.



Convergence warning: The first 3×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.


name type notes
date datetime Date
tema float, Optional Triple 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_historical_quotes("SPY")

# calculate 20-period TEMA
results = indicators.get_tema(quotes, 20)

About Triple Exponential Moving Average (TEMA)

Triple exponential moving average of the Close price over a lookback window. Note: TEMA is often confused with the alternative TRIX oscillator. [Discuss] 💬


See related EMA and Double EMA.