1.7 KiB
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title, description, keywords
| title | description | keywords | ||||||||||||
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| ema | Learn how to calculate the Exponential Moving Average (EMA) in Python using the openbb library. Understand its benefits as a cumulative calculation and how it maintains data responsiveness. Find details on parameters like data, target column, index column, length, and offset. Get code examples to implement EMA calculations in your projects. |
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import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';
Exponential Moving Average.
EMA is a cumulative calculation, including all data. Past values have a diminishing contribution to the average, while more recent values have a greater contribution. This method allows the moving average to be more responsive to changes in the data.
Parameters
data : List[Data]
The data to use for the calculation.
target : str
Target column name.
index : str, optional
Index column name to use with data, by default "date"
length : int, optional
The length of the calculation, by default 50.
offset : int, optional
The offset of the calculation, by default 0.
Returns
OBBject[List[Data]] The calculated data.
Examples
from openbb import obb
stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp")
ema_data = obb.technical.ema(data=stock_data.results,target="close",length=50,offset=0)
Parameters
This function does not take standardized parameters.
Returns
This function does not return a standardized model