Correlation Coefficient

get_correlation(quotes_a, quotes_b, lookback_periods)

Parameters

name	type	notes
`quotes_a`	Iterable[Quote]	Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame
`quotes_b`	Iterable[Quote]	Historical quotes (B) must have at least the same matching date elements of `quotes_a`.
`lookback_periods`	int	Number of periods (`N`) in the lookback period. Must be greater than 0 to calculate; however we suggest a larger period for statistically appropriate sample size.

Historical quotes requirements

You must have at least N periods for both versions of quotes to cover the warmup periods. Mismatch histories will produce a InvalidQuotesException. Historical price quotes should have a consistent frequency (day, hour, minute, etc).

quotes_a 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

CorrelationResults[CorrelationResult]

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

CorrelationResult

name	type	notes
`date`	datetime	Date
`variance_a`	float, Optional	Variance of A based on `N` lookback periods
`variance_b`	float, Optional	Variance of B based on `N` lookback periods
`covariance`	float, Optional	Covariance of A+B based on `N` lookback periods
`correlation`	float, Optional	Correlation `R` based on `N` lookback periods
`r_squared`	float, Optional	R-Squared (R²), aka Coefficient of Determination. Simple linear regression models is used (square of Correlation).

Utilities

See Utilities and Helpers for more information.

Example

from stock_indicators import indicators

# This method is NOT a part of the library.
quotes_spx = get_historical_quotes("SPX")
quotes_tsla = get_historical_quotes("TSLA")

# Calculate 20-period Correlation
results = indicators.get_correlation(quotes_spx, quotes_tsla, 20)

About Correlation Coefficient

Correlation Coefficient between two quote histories, based on Close price. R-Squared (R²), Variance, and Covariance are also output. [Discuss] 💬