Metadata-Version: 1.1
Name: pyRMT
Version: 0.1.0
Summary: Python for Random Matrix Theory: cleaning schemes for noisy correlation matrices
Home-page: https://github.com/GGiecold/pyRMT
Author: Gregory Giecold
Author-email: g.giecold@gmail.com
License: MIT License
Download-URL: https://github.com/GGiecold/pyRMT
Description: # pyRMT
        Python for Random Matrix Theory. Implements several cleaning schemes for noisy correlation matrices, 
        including the optimal shrinkage, rotationally-invariant estimator
        to an underlying correlation matrix (as proposed by Joel Bun, 
        Jean-Philippe Bouchaud, Marc Potters and colleagues).
        
        Such cleaned correlation matrix are known to improve factor-decomposition
        via Principal Component Analysis (PCA) and could be of relevance in a variety 
        of contexts, including computational biology.
        
        Cleaning schemes also result in much improved out-of-sample risk
        of Markowitz optimal portfolios, as established over the years
        in several papers by Jean-Philippe Bouchaud, Marc Potters and collaborators.
        
        Some cleaning schemes can be easily adapted from the various shrinkage
        estimators implemented in the sklearn.covariance module 
        (see the various publications by O. Ledoit and M. Wolf listed below).
        
        In addition, it might make sense to perform an empirical estimate
        of a correlation matrix robust to outliers before proceeding with
        the cleaning schemes of the present module. Some of those robust estimates
        have been implemented in the sklearn.covariance module as well. 
        
        
        References
        ----------
        * "DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES",
          V. A. Marcenko and L. A. Pastur, Mathematics of the USSR-Sbornik, Vol. 1 (4), pp 457-483
        * "A well-conditioned estimator for large-dimensional covariance matrices",
          O. Ledoit and M. Wolf, Journal of Multivariate Analysis, Vol. 88 (2), pp 365-411
        * "Improved estimation of the covariance matrix of stock returns with "
          "an application to portfolio selection",
          O. Ledoit and M. Wolf, Journal of Empirical Finance, Vol. 10 (5), pp 603-621
        * "Financial Applications of Random Matrix Theory: a short review",
          J.-P. Bouchaud and M. Potters, arXiv: 0910.1205 [q-fin.ST]
        * "Eigenvectors of some large sample covariance matrix ensembles",
          O. Ledoit and S. Peche, Probability Theory and Related Fields, Vol. 151 (1), pp 233-264
        * "NONLINEAR SHRINKAGE ESTIMATION OF LARGE-DIMENSIONAL COVARIANCE MATRICES",
          O. Ledoit and M. Wolf, The Annals of Statistics, Vol. 40 (2), pp 1024-1060 
        * "Rotational invariant estimator for general noisy matrices",
          J. Bun, R. Allez, J.-P. Bouchaud and M. Potters, arXiv: 1502.06736 [cond-mat.stat-mech]
        * "Cleaning large Correlation Matrices: tools from Random Matrix Theory",
          J. Bun, J.-P. Bouchaud and M. Potters, arXiv: 1610.08104 [cond-mat.stat-mech]
          
        Installing from PyPI
        --------------------
        
        Try
        
        ```pip install pyRMT```
        
        To install manually from the git repo, try this:
        
        ```python setup.py install```
        
        The pyRMT codebase supports Python 2 and 3.
        
        Usage
        -----
        
        For more information on cleaning your correlation matrices, please refer to the docstrings to the various schemes implemented within pyRMT.
          
        Attribution
        -----------
        
        If you happen to use pyRMT in your work or research, please cite its GitHub repository:
        
        G. Giecold, pyRMT, (2017), GitHub repository, https://github.com/GGiecold/pyRMT
        
        The respective BibTex entry is
        
        ```@misc{GregoryGiecold2017, author = {G. Giecold}, title = {pyRMT}, year = {2017}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/GGiecold/pyRMT}}}```
        
        
        License
        -------
        
        Copyright 2017-2022 Gregory Giecold and contributors.
        
        pyRMT is free software made available under the MIT License. For details see the LICENSE file.
        
Keywords: applied-mathematics cleaning correlation-matrices noise-reduction random-matrix-theory
Platform: ALL
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
