Ehlers Fisher Transform

get_fisher_transform(quotes, lookback_periods=10)

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 10	Number of periods (`N`) in the lookback window. Must be greater than 0.

Historical quotes requirements

You must have at least N 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

FisherTransformResults[FisherTransformResult]

This method returns a time series of all available indicator values for the quotes provided.
FisherTransformResults is just a list of FisherTransformResult.
It always returns the same number of elements as there are in the historical quotes.
It does not return a single incremental indicator value.

⚞ Convergence warning: The first N+15 warmup periods will have unusable decreasing magnitude, convergence-related precision errors that can be as high as ~25% deviation in earlier indicator values.

FisherTransformResult

name	type	notes
`date`	datetime	Date
`fisher`	float, Optional	Fisher Transform
`trigger`	float, Optional	FT offset by one period

Utilities

For pruning of warmup periods, we recommend using the following guidelines:

indicators.get_fisher_transform(quotes, lookback_periods)
  .remove_warmup_periods(lookback_periods + 15)

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 10-period FisherTransform
results = indicators.get_fisher_transform(quotes, 10)

About Ehlers Fisher Transform

Created by John Ehlers, the Fisher Transform converts prices into a Gaussian normal distribution. [Discuss] 💬