Tillson T3 Moving Average

get_t3(quotes, lookback_periods=5, volume_factor=0.7)


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, default 5 Number of periods (N) for the EMA smoothing. Must be greater than 0 and is usually less than 63.
volume_factor float, default 0.7 Size of the Volume Factor. Must be greater than 0 and is usually less than 2.

Historical quotes requirements

You must have at least 6×(N-1)+100 periods of quotes to cover the convergence periods. Since this uses a smoothing technique, we recommend you use at least 6×(N-1)+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 6×(N-1)+250 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
t3 float, Optional T3 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 5-period T3
results = indicators.get_t3(quotes, 5, 0.7)

About Tillson T3 Moving Average

Created by Tim Tillson, the T3 indicator is a smooth moving average that reduces both lag and overshooting. [Discuss] 💬