Tillson T3 Moving Average

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

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, 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.

Return

T3Results[T3Result]

This method returns a time series of all available indicator values for the quotes provided.
T3Results is just a list of T3Result.
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 6×(N-1) periods will have None values since there’s not enough data to calculate.

⚞ 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.

T3Result

name	type	notes
`date`	datetime	Date
`t3`	float, Optional	T3 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_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] 💬