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_historical_quotes("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.