# Slope and Linear Regression

get_slope(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 | Number of periods (`N` ) for the linear regression. Must be greater than 0. |

### Historical quotes requirements

You must have at least `N`

periods of `quotes`

to cover the warmup periods.

`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

```
SlopeResults[SlopeResult]
```

- This method returns a time series of all available indicator values for the
`quotes`

provided. `SlopeResults`

is just a list of`SlopeResult`

.- 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 for`slope`

since there’s not enough data to calculate.

👉

Repaint warning: the`line`

will be continuously repainted since it is based on the last quote and lookback period.

### SlopeResult

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

`date` | datetime | Date |

`slope` | float, Optional | Slope `m` of the best-fit line of Close price |

`intercept` | float, Optional | Y-Intercept `b` of the best-fit line |

`stdev` | float, Optional | Standard Deviation of Close price over `N` lookback periods |

`r_squared` | float, Optional | R-Squared (R²), aka Coefficient of Determination |

`line` | Decimal, Optional | Best-fit line `y` over the last ‘N’ periods (i.e. `y=mx+b` using last period values) |

### 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 Slope
results = indicators.get_slope(quotes, 20)
```

## About Slope and Linear Regression

Slope of the best fit line is determined by an ordinary least-squares simple linear regression on Close price. It can be used to help identify trend strength and direction. Standard Deviation, R², and a best-fit `Line`

(for last lookback segment) are also output. See also Standard Deviation Channels for an alternative depiction. [Discuss] 💬