# Endpoint Moving Average (EPMA)

get_epma(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` ) in the moving average. 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

```
EPMAResults[EPMAResult]
```

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

provided. `EPMAResults`

is just a list of`EPMAResult`

.- 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.

### EPMAResult

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

`date` | datetime | Date |

`epma` | float, Optional | Endpoint moving average |

### 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 EPMA
results = indicators.get_epma(quotes, 20)
```

## About Endpoint Moving Average (EPMA)

Endpoint Moving Average (EPMA), also known as Least Squares Moving Average (LSMA), plots the projected last point of a linear regression lookback window. [Discuss] 💬