# Correlation Coefficient

##
**get_correlation**(*quotes_a, quotes_b, lookback_periods*)

## Parameters

name | type | notes |
---|---|---|

`quotes_a` |
Iterable[Quote] | Iterable(such as list or an object having `__iter__()` ) of the Quote class or its sub-class. • Need help 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_history_from_feed("SPX")
quotes_tsla = get_history_from_feed("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]