All Performance Metrics
Calculates and collects all performance metrics.
All Performance Metrics in Python
collect_all_metrics is part of the Performance module of the open-source Finance Toolkit. Install it with:
pip install financetoolkit -U
Then call collect_all_metrics as shown below.
from financetoolkit import Toolkit
toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")
toolkit.performance.collect_all_metrics().xs("AAPL", level=1, axis=1)
Which returns:
| Win Rate | Upside Capture Ratio | Downside Capture Ratio | M2 Ratio | Tracking Error | |
|---|---|---|---|---|---|
| 2021 | 0.5253 | 1.4003 | 1.1039 | 0.0065 | 0.0108 |
| 2022 | 0.4781 | 1.3096 | 1.3186 | -0.1669 | 0.0115 |
| 2023 | 0.576 | 1.1815 | 0.9655 | 0.3293 | 0.009 |
| 2024 | 0.5 | 1.117 | 1.0492 | 0.1905 | 0.0121 |
| 2025 | 0.472 | 1.0324 | 1.1132 | 0.0709 | 0.0139 |
| 2026 | 0.5099 | 0.678 | 0.5418 | 0.0919 | 0.0169 |
Parameters
collect_all_metrics accepts the following parameters:
- period (str, optional): The period to use for the calculation. Defaults to “quarterly” if the Toolkit is initialised with quarterly=True, otherwise “yearly”.
- 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.