Force Index

get_force_index(quotes, lookback_periods)

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	Lookback window (`N`) for the EMA of Force Index. Must be greater than 0 and is commonly 2 or 13 (shorter/longer view).

Historical quotes requirements

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

ForceIndexResults[ForceIndexResult]

This method returns a time series of all available indicator values for the quotes provided.
ForceIndexResults is just a list of ForceIndexResult.
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 periods for will be None since they cannot be calculated.

⚞ 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.

ForceIndexResult

name	type	notes
`date`	datetime	Date
`force_index`	float, Optional	Force Index

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 ForceIndex(13)
results = indicators.get_force_index(quotes, 13)

About Force Index

Created by Alexander Elder, the Force Index depicts volume-based buying and selling pressure. [Discuss] 💬