Pivot Points
Calculate the Pivot Points for a given price series.
Pivot Points are calculated from the previous period’s high, low and close prices and are used to identify potential support and resistance levels for the current period.
The formula is as follows:
\[\text{Pivot Point} = (\text{Previous High} + \text{Previous Low} + \text{Previous Close}) / 3\] \[\text{Resistance} 1 = (2 \cdot \text{Pivot Point}) - \text{Previous Low}\] \[\text{Support} 1 = (2 \cdot \text{Pivot Point}) - \text{Previous High}\] \[\text{Resistance} 2 = \text{Pivot Point} + (\text{Previous High} - \text{Previous Low})\] \[\text{Support} 2 = \text{Pivot Point} - (\text{Previous High} - \text{Previous Low})\] \[\text{Resistance} 3 = \text{Previous High} + 2 \cdot (\text{Pivot Point} - \text{Previous Low})\] \[\text{Support} 3 = \text{Previous Low} - 2 \cdot (\text{Previous High} - \text{Pivot Point})\]Also known as: pivot points, floor trader pivots.
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Calculate the Pivot Points in Python
The Pivot Points is available in the Technicals module of the open-source Finance Toolkit. Install it with:
pip install financetoolkit -U
Then call get_pivot_points as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(tickers=["AAPL", "MSFT"])
toolkit.technicals.get_pivot_points().xs("AAPL", level=1, axis="columns")
Which returns:
| Date | Pivot Point | Resistance 1 | Support 1 |
|---|---|---|---|
| 2026-06-18 | 300.742 | 305.53 | 295.954 |
| 2026-06-22 | 300.078 | 304.201 | 295.955 |
| 2026-06-23 | 298.585 | 303.09 | 294.08 |
| 2026-06-24 | 297.358 | 301.328 | 293.388 |
| 2026-06-25 | 294.781 | 299.176 | 290.386 |
| 2026-06-26 | 293.098 | 296.652 | 289.544 |
| 2026-06-29 | 291.684 | 295.571 | 287.796 |
| 2026-06-30 | 291.599 | 295.53 | 287.667 |
| 2026-07-01 | 291.799 | 295.81 | 287.788 |
| 2026-07-02 | 292.727 | 299.318 | 286.137 |
Parameters
get_pivot_points 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”.
- 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 Pivot Points. 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.
Related Overlap Indicators
The Technicals module page introduces the module, and the sidebar lists all of its functions.