numerai-tools

A collection of open-source tools to help interact with Numerai, model data, and automate submissions.

Installation

pip install numerai-tools

Structure

  • The scoring.py module contains critical functions used to score submissions. We use this code in our scoring system system. Leverage this to optimize your models for the tournaments.

    The Signals payout scores are built from two of its functions:

    • neutral_correlation ranks, gaussianizes, and neutralizes predictions against a neutralizer matrix, then correlates them with the target. Unlike numerai_corr it applies no 1.5 power, and unlike feature_neutral_corr it does not re-rank and re-power the predictions after neutralizing them.
    • neutral_contribution is correlation_contribution with that same neutralization step inserted after the rank/gaussianize step. It neutralizes both the submissions and the meta model against the same neutralizers before orthogonalizing. Predictions identical to the meta model have zero neutral contribution, up to floating-point precision.

  • The submissions.py module provides helper functions to ensure your submissions are valid and formatted correctly. Use this in your automated prediction pipelines to ensure uploads don't fail.

  • The signals.py module provides code specific to Numerai Signals such as churn and turnover. neutral_churn measures churn after applying each era's neutralizers, calculate_mean_neutral_churn averages that metric across the provided recent submissions, and neutral_churn_penalty calculates the positive-payout retention multiplier described by the Signals v3 churn penalty.

Project links

Key dates

PyPI data
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Latest release

3 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for joshterrill from gravatar.com joshterrill Avatar for ndharasz from gravatar.com ndharasz Avatar for numerai from gravatar.com numerai

Credits

Author: Numerai Engineering

License

MIT License (MIT)

Requires

Python >=3.11

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