Straud
Copyright 2026 The Straud Authors

Straud is proprietary software, licensed and not sold. Your rights are set out
in the LICENSE file distributed with this work; terms of purchase are in
TERMS.md. The third-party components listed below are NOT covered by that
licence — each remains under its own, reproduced in THIRD_PARTY_LICENSES.txt.

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THIRD-PARTY SOFTWARE
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This product depends on the following third-party packages. Each is used
unmodified and under its own licence.

⚠️ The STANDALONE BUILD additionally SHIPS several of these inside the
distributable. Their full licence texts are reproduced in
THIRD_PARTY_LICENSES.txt, which accompanies every binary release —
BSD-3-Clause requires a binary redistribution to reproduce the copyright
notice and licence text, and naming a licence does not satisfy that.

  NumPy                BSD 3-Clause      https://numpy.org
  pandas               BSD 3-Clause      https://pandas.pydata.org
  SciPy                BSD 3-Clause      https://scipy.org
  python-dateutil      BSD 3-Clause / Apache 2.0
  setuptools           MIT
  PyArrow (optional)   Apache 2.0        https://arrow.apache.org
  mcp (optional)       MIT               https://modelcontextprotocol.io

Additionally embedded in the standalone build only:

  CPython              PSF License 2.0   https://docs.python.org/3/license.html
  OpenSSL              Apache 2.0        https://www.openssl.org/source/license.html

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ACADEMIC METHODS
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Straud implements statistical methods from published academic literature. The
authors of that work are credited here; the implementations are Straud's own.

  Probabilistic Sharpe Ratio, Deflated Sharpe Ratio, Minimum Track Record
  Length, Minimum Backtest Length
    Bailey, D. H., & López de Prado, M. (2014).
    "The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest
     Overfitting and Non-Normality." SSRN 2460551.
    Bailey, D. H., & López de Prado, M. (2012).
    "The Sharpe Ratio Efficient Frontier." Journal of Risk.

  Probability of Backtest Overfitting (Combinatorially Symmetric
  Cross-Validation)
    Bailey, D. H., Borwein, J. M., López de Prado, M., & Zhu, Q. J. (2015).
    "The Probability of Backtest Overfitting." Journal of Computational
     Finance.

  Cluster-robust standard errors
    Standard econometric method; small-sample correction after
    Cameron, A. C., & Miller, D. L. (2015), "A Practitioner's Guide to
    Cluster-Robust Inference." Journal of Human Resources.

The following checks are Straud's own and are not drawn from the literature:
frozen-assumption ("stale derived column") detection; effective-sample-size
reporting applied to event studies; the absence-defined event fabrication
probe; shuffled-null concentration testing; implied trial capacity; and the
input-integrity layer.
