Exponential Moving Average (EMA)
Calculate the Exponential Moving Average (EMA) for a given price series.
EMA is a technical indicator that gives more weight to recent price data, providing a smoothed moving average that reacts faster to price changes.
The formula is as follows:
\[\text{EMA} = (\text{Close} - \text{Previous EMA}) \cdot (2 / (1 + \text{Window})) + \text{Previous EMA}\]Also known as: EMA.
Exponential Moving Average (EMA) in Python
get_exponential_moving_average is part of the Technicals module of the open-source Finance Toolkit. Install it with:
pip install financetoolkit -U
Then call get_exponential_moving_average as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(tickers=["AAPL", "MSFT"])
toolkit.technicals.get_exponential_moving_average()
Which returns:
| Date | AAPL | MSFT | Benchmark |
|---|---|---|---|
| 2026-06-18 | 298.807 | 400.817 | 744.491 |
| 2026-06-22 | 298.567 | 396.353 | 744.477 |
| 2026-06-23 | 297.998 | 393.365 | 743.024 |
| 2026-06-24 | 297.342 | 389.644 | 741.72 |
| 2026-06-25 | 294.383 | 384.736 | 740.73 |
| 2026-06-26 | 292.969 | 383.167 | 739.165 |
| 2026-06-29 | 291.472 | 381.221 | 739.41 |
| 2026-06-30 | 291.191 | 380.127 | 740.391 |
| 2026-07-01 | 291.616 | 380.681 | 741.107 |
| 2026-07-02 | 293.884 | 381.989 | 741.597 |
Parameters
get_exponential_moving_average accepts the following parameters:
- period (str, optional): The time period to consider for historical data. Can be “daily”, “weekly”, “quarterly”, or “yearly”. Defaults to “daily”.
- close_column (str, optional): The column name for closing prices in the historical data. Defaults to “Adj Close”.
- window (int, optional): Number of periods for EMA calculation. The number of periods (time intervals) over which to calculate the EMA.
- rounding (int | None, optional): The number of decimals to round the results to. Defaults to 4.
- growth (bool, optional): Whether to calculate the growth of the EMA. Defaults to False.
- lag (int | list[int], optional): The lag to use for the growth calculation.
- standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False. Defaults to 1.