Elder-ray Index

get_elder_ray(quotes, lookback_periods=13)

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 13	Number of periods (`N`) for the underlying EMA evaluation. Must be greater than 0.

Historical quotes requirements

You must have at least 2×N or N+100 periods of quotes, whichever is more, to cover the convergence periods. Since this uses a smoothing technique, we recommend you use at least 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.

Returns

ElderRayResults[ElderRayResult]

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

⚞ Convergence warning: The first 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.

ElderRayResult

name	type	notes
`date`	datetime	Date
`ema`	float, Optional	Exponential moving average of Close price
`bull_power`	float, Optional	Bull Power
`bear_power`	float, Optional	Bear Power

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 ElderRay(13)
results = indicators.get_elder_ray(quotes, 13)

About Elder-ray Index

Created by Alexander Elder, the Elder-ray Index, also known as Bull and Bear Power, depicts buying and selling pressure. [Discuss] 💬