# Exponential Moving Average (EMA)

get_ema(quotes, lookback_periods, candle_part=CandlePart.CLOSE)

## 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. |

`candle_part` | CandlePart, default CandlePart.CLOSE | Specify candle part to evaluate. See CandlePart options below. |

### Historical quotes requirements

You must have at least `2×N`

or `N+100`

periods of `quotes`

, whichever is more, to cover the convergence periods. Since this uses a smoothing technique, 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.

#### CandlePart options

```
from stock_indicators.indicators.common.enums import CandlePart
```

type | description |
---|---|

`CandlePart.OPEN` | `open` price |

`CandlePart.HIGH` | `high` price |

`CandlePart.LOW` | `low` price |

`CandlePart.CLOSE` | `close` price |

`CandlePart.VOLUME` | `volume` |

`CandlePart.HL2` | `(high+low)/2` |

`CandlePart.HLC3` | `(high+low+close)/3` |

`CandlePart.OC2` | `(open+close)/2` |

`CandlePart.OHL3` | `(open+high+low)/3` |

`CandlePart.OHLC4` | `(open+high+low+close)/4` |

## Returns

```
EMAResults[EMAResult]
```

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

provided. `EMAResults`

is just a list of`EMAResult`

.- 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.

⚞

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.

### EMAResult

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

`date` | datetime | Date |

`ema` | float, Optional | Exponential moving average |

### Utilities

See Utilities and Helpers for more information.

## Example

```
from stock_indicators import indicators
from stock_indicators import CandlePart # Short path, version >= 0.8.1
# This method is NOT a part of the library.
quotes = get_history_from_feed("SPY")
# calculate 20-period EMA
results = indicators.get_ema(quotes, 20, CandlePart.CLOSE)
```

### About Exponential Moving Average (EMA)

Exponentially weighted moving average price over a lookback window. [Discuss] 💬

See also related Double EMA and Triple EMA.