Hilbert Transform Instantaneous Trendline

get_ht_trendline(quotes)

Parameters

name	type	notes
`quotes`	Iterable[Quote]	Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame

Historical quotes requirements

You must have at least 100 periods of quotes to cover the warmup periods.

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

HTTrendlineResults[HTTrendlineResult]

This method returns a time series of all available indicator values for the quotes provided.
HTTrendlineResults is just a list of HTTrendlineResult.
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 periods will have None values for smooth_price since there’s not enough data to calculate.
The first 7 periods will have None values for dc_periods since there is not enough data to calculate; and are generally unreliable for the first ~25 periods.

⚞ Convergence warning: The first 100 periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.

HTTrendlineResult

name	type	notes
`date`	datetime	Date
`trendline`	float, Optional	HT Trendline
`dc_periods`	int, Optional	Dominant cycle periods (smoothed)
`smooth_price`	float, Optional	Weighted moving average of `(H+L)/2` price

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 HT Trendline
results = indicators.get_ht_trendline(quotes)

About Hilbert Transform Instantaneous Trendline

Created by John Ehlers, the Hilbert Transform Instantaneous Trendline is a 5-period trendline of high/low price that uses signal processing to reduce noise. [Discuss] 💬