Alpha
Alpha, in a general sense, represents the excess return an investment generates relative to a benchmark or a risk-adjusted return. It can be positive (indicating the investment outperformed the benchmark) or negative (indicating underperformance).
The formula is as follows:
\[\text{Alpha} = \text{Asset's Actual Return} - \text{Benchmark's Actual Return}\]See definition: https://en.wikipedia.org/wiki/Alpha_(finance)
Also known as: excess return, outperformance, active return.
Alpha in Python
Install the Finance Toolkit with:
pip install financetoolkit -U
Then call get_alpha as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")
toolkit.performance.get_alpha()
Which returns:
| Date | AAPL | TSLA |
|---|---|---|
| 2021 | 0.0678 | 0.2272 |
| 2022 | -0.0735 | -0.4555 |
| 2023 | 0.2389 | 0.7743 |
| 2024 | 0.0677 | 0.3922 |
| 2025 | -0.0779 | -0.0499 |
| 2026 | 0.0431 | -0.2173 |
Parameters
The function accepts the following parameters:
- period (str, optional): The period to use for the calculation. Defaults to “quarterly” if the Toolkit is initialized with quarterly=True, otherwise “yearly”.
- rolling (int, optional): The rolling window size to use for the calculation. If set,
Alpha is calculated as the rolling mean excess return over this many periods across
the full return history instead of per
period. Defaults to None. - rounding (int, optional): The number of decimals to round the results to. Defaults to 4.
- growth (bool, optional): Whether to calculate the growth of the ratios. Defaults to False.
- lag (int | str, optional): The lag to use for the growth calculation. Defaults to 1.
- 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.