All Momentum Indicators
Calculates and collects various momentum indicators based on the provided data.
All Momentum Indicators in Python
collect_momentum_indicators is part of the Technicals module of the open-source Finance Toolkit. Install it with:
pip install financetoolkit -U
Then call collect_momentum_indicators as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(tickers=["AAPL", "MSFT"])
toolkit.technicals.collect_momentum_indicators().xs("AAPL", level=1, axis="columns")
Which returns:
| Date | Ichimoku Leading Span A | Ichimoku Leading Span B | Stochastic %K | Stochastic %D |
|---|---|---|---|---|
| 2026-06-18 | 307.225 | 286.8 | 35.4097 | 34.4881 |
| 2026-06-22 | 306.55 | 287.03 | 32.0786 | 32.012 |
| 2026-06-23 | 302.59 | 287.295 | 23.0513 | 30.1799 |
| 2026-06-24 | 302.39 | 287.605 | 18.9873 | 24.7058 |
| 2026-06-25 | 302.39 | 289.335 | 3.2073 | 15.082 |
| 2026-06-26 | 302.39 | 291.235 | 22.9782 | 15.0576 |
| 2026-06-29 | 302.39 | 291.235 | 27.8689 | 18.0181 |
| 2026-06-30 | 302.39 | 291.235 | 54.4472 | 35.0981 |
| 2026-07-01 | 302.39 | 291.235 | 71.9567 | 51.4243 |
| 2026-07-02 | 302.39 | 291.235 | 97.7853 | 74.7297 |
Parameters
collect_momentum_indicators 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”.
- window (int, optional): The window size for calculating indicators. Defaults to 14.
- close_column (str, optional): The name of the column containing the close prices. Defaults to “Adj Close”.
- 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 indicator values. 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.