Price Momentum Oscillator (PMO)

get_pmo(quotes, time_periods=35)

Parameters

name	type	notes
`quotes`	Iterable[Quote]	Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame
`time_periods`	int, default 35	Number of periods (`T`) for ROC EMA smoothing. Must be greater than 1.

Historical quotes requirements

You must have at least N periods of quotes, where N is the greater of T+S,2×T, or T+100 to cover the convergence periods. Since this uses multiple smoothing operations, 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.

Return

PMOResults[PMOResult]

This method returns a time series of all available indicator values for the quotes provided.
PMOResults is just a list of PMOResult.
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 T+S-1 periods will have None values for PMO since there’s not enough data to calculate.

⚞ Convergence warning: The first T+S+250 periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.

PMOResult

name	type	notes
`date`	datetime	Date
`pmo`	float, Optional	Price Momentum Oscillator
`signal`	float, Optional	Signal line is EMA of PMO

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 20-period PMO
results = indicators.get_pmo(quotes, 35,20,10)

About Price Momentum Oscillator (PMO)

Created by Carl Swenlin, the DecisionPoint Price Momentum Oscillator is double-smoothed ROC based momentum indicator. [Discuss] 💬