Calculate the Gain-to-Pain Ratio of an investment portfolio or asset’s returns.

The Gain-to-Pain Ratio, popularized by Jack Schwager, divides the sum of all returns by the sum of the absolute value of all losses, summarizing the entire return history into a single measure of return earned per unit of pain endured.

The formula is as follows:

\[\text{Gain-to-Pain Ratio} = \operatorname{SUM}(\text{Returns}) / \operatorname{SUM}(| \text{Losses} |)\]

No programming experience? With the Finance Toolkit MCP server, AI assistants such as Claude and ChatGPT can calculate the Gain-to-Pain Ratio for you. Just ask in plain English.

Calculate the Gain-to-Pain Ratio in Python

The Gain-to-Pain Ratio is available in the Performance module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_gain_to_pain_ratio as shown below.

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.performance.get_gain_to_pain_ratio()

Which returns:

  AAPL TSLA
2021 0.2354 0.1945
2022 -0.108 -0.1871
2023 0.4034 0.3043
2024 0.2462 0.2098
2025 0.0873 0.0871
2026 0.2062 -0.0642

Parameters

get_gain_to_pain_ratio 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”.
  • within_period (bool, optional): Whether to calculate the Gain-to-Pain Ratio within the specified period or for the entire period. Thus whether to look at the Gain-to-Pain Ratio within a specific year (if period = ‘yearly’) or look at the entirety of all years. Defaults to True.
  • 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.
Information Ratio Upside Capture Ratio Downside Capture Ratio Win Rate Kappa Ratio Omega Ratio Compound Growth Rate Returns Excess Return Correlation Matrix Covariance Matrix Carhart Four-Factor Model

The Performance module page introduces the module, and the sidebar lists all of its functions.

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