Parabolic SAR

get_parabolic_sar(quotes, acceleration_step=0.02, max_acceleration_factor=0.2)

More overloaded interfaces

get_parabolic_sar(quotes, acceleration_step, max_acceleration_factor, initial_factor)


name type notes
quotes Iterable[Quote] Iterable of the Quote class or its sub-class.
See here for usage with pandas.DataFrame
acceleration_step float, default 0.02 Incremental step size for the Acceleration Factor. Must be greater than 0.
max_acceleration_factor float, default 0.2 Maximum factor limit. Must be greater than acceleration_step.
initial_factor float Initial Acceleration Factor. Must be greater than 0 and not larger than max_acceleration_factor. Default is acceleration_step.

Historical quotes requirements

You must have at least two historical quotes to cover the warmup periods; however, we recommend at least 100 data points. Initial Parabolic SAR values prior to the first reversal are not accurate and are excluded from the results. Therefore, provide sufficient quotes to capture prior trend reversals, before your intended usage period.

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.




name type notes
date datetime Date
sar float, Optional Stop and Reverse value
is_reversal bool, Optional Indicates a trend reversal


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 ParabolicSar(0.02,0.2)
results = indicators.get_parabolic_sar(quotes, 0.02, 0.2)

About Parabolic SAR

Created by J. Welles Wilder, Parabolic SAR (stop and reverse) is a price-time based indicator used to determine trend direction and reversals. [Discuss] 💬