Pivots

get_pivots(quotes, left_span=2, right_span=2, max_trend_periods=20, end_type=EndType.HIGH_LOW)

Parameters

name	type	notes
`quotes`	Iterable[Quote]	Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame
`left_span`	int, default 2	Left evaluation window span width (`L`). Must be at least 2.
`right_span`	int, default 2	Right evaluation window span width (`R`). Must be at least 2.
`max_trend_periods`	int, default 20	Number of periods (`N`) in evaluation window. Must be greater than `leftSpan`.
`end_type`	EndType, default EndType.HIGH_LOW	Determines whether `close` or `high/low` are used to find end points. See EndType options below.

The total evaluation window size is L+R+1.

Historical quotes requirements

You must have at least L+R+1 periods of quotes to cover the warmup periods; however, more is typically provided since this is a chartable candlestick pattern.

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.

EndType options

from stock_indicators.indicators.common.enums import EndType

type	description
`CLOSE`	Chevron point identified from `close` price
`HIGH_LOW`	Chevron point identified from `high` and `low` price (default)

Return

PivotsResults[PivotsResult]

This method returns a time series of all available indicator values for the quotes provided.
PivotsResults is just a list of PivotsResult.
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 L and last R periods in quotes are unable to be calculated since there’s not enough prior/following data.

👉 Repaint warning: this price pattern looks forward and backward in the historical quotes so it will never identify a pivot in the last R periods of quotes. Fractals are retroactively identified.

PivotsResult

name	type	notes
`date`	datetime	Date
`high_point`	Decimal, Optional	Value indicates a high point; otherwise `None` is returned.
`low_point`	Decimal, Optional	Value indicates a low point; otherwise `None` is returned.
`high_line`	Decimal, Optional	Drawn line between two high points in the `max_trend_periods`
`low_line`	Decimal, Optional	Drawn line between two low points in the `max_trend_periods`
`high_trend`	PivotTrend, Optional	Enum that represents higher high or lower high. See PivotTrend values below.
`low_trend`	PivotTrend, Optional	Enum that represents higher low or lower low. See PivotTrend values below.

PivotTrend values

from stock_indicators.indicators.common.enums import PivotTrend

type	description
`PivotTrend.HH`	Higher high
`PivotTrend.LH`	Lower high
`PivotTrend.HL`	Higher low
`PivotTrend.LL`	Lower low

Utilities

See Utilities and Helpers for more information.

Example

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

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

# Calculate Pivots(2,2,20) using High/Low values
results = indicators.get_pivots(quotes, 2, 2, 20, EndType.HIGH_LOW);

About Pivots

Pivots is an extended version of Williams Fractal that includes identification of Higher High, Lower Low, Higher Low, and Lower Low trends between pivots in a lookback window. [Discuss] 💬