Rolling Pivot Points

get_rolling_pivots(quotes, window_periods, offset_periods, point_type=PivotPointType.STANDARD)

Parameters

name	type	notes
`quotes`	Iterable[Quote]	Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame
`window_periods`	int	Number of periods (`W`) in the evaluation window. Must be greater than 0 to calculate; but is typically specified in the 5-20 range.
`offset_periods`	int	Number of periods (`F`) to offset the window from the current period. Must be greater than or equal to 0 and is typically less than or equal to `W`.
`point_type`	PivotPointType, default PivotPointType.STANDARD	Type of Pivot Point. See PivotPointType options below.

For example, a window of 8 with an offset of 4 would evaluate quotes like: W W W W W W W W F F F F C, where W is the window included in the Pivot Point calculation, and F is the distance from the current evaluation position C. A quotes with daily bars using W/F values of 20/10 would most closely match the month variant of the traditional Pivot Points indicator.

Historical quotes requirements

You must have at least W+F 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.

PivotPointType options

from stock_indicators.indicators.common.enums import PivotPointType

type	description
`PivotPointType.STANDARD`	Floor Trading (default)
`PivotPointType.CAMARILLA`	Camarilla
`PivotPointType.DEMARK`	Demark
`PivotPointType.FIBONACCI`	Fibonacci
`PivotPointType.WOODIE`	Woodie

Return

RollingPivotsResults[RollingPivotsResult]

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

RollingPivotsResult

name	type	notes
`date`	datetime	Date
`r3`	Decimal, Optional	Resistance level 3
`r2`	Decimal, Optional	Resistance level 2
`r1`	Decimal, Optional	Resistance level 1
`pp`	Decimal, Optional	Pivot Point
`s1`	Decimal, Optional	Support level 1
`s2`	Decimal, Optional	Support level 2
`s3`	Decimal, Optional	Support level 3

Utilities

See Utilities and Helpers for more information.

Example

from stock_indicators import indicators
from stock_indicators import PivotPointType     # Short path, version >= 0.8.1

# This method is NOT a part of the library.
quotes = get_historical_quotes("SPY")

# Calculate Woodie-style 14 period Rolling Pivot Points
results = indicators.get_rolling_pivots(quotes, 14, 0, PivotPointType.Woodie);

About Rolling Pivot Points

Created by Dave Skender, Rolling Pivot Points is a modern update to traditional fixed calendar window Pivot Points. It depicts support and resistance levels, based on a defined rolling window and offset. [Discuss] 💬