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include README.rst
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##############
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PyPortfolioOpt
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##############
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************
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Introduction
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************
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PyPortfolioOpt is a simple library that contains widely used portfolio optimisation techniques, with
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a number of novel/experimental features.
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*********************
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Currently Implemented
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*********************
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Implemented
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Efficient frontier
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*******
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Testing
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*******
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Test use a returns dataset using daily returns for 20 tickers. These tickers have been informally selected
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to meet a number of criteria
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- reasonably liquid
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- different performances and volatilities
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- different amounts of data to test robustness
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****************
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Design decisions
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****************
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- Should be easy to swap out components to test
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- Some robustness to missing data
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# Makefile for Sphinx documentation
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#
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# You can set these variables from the command line.
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SPHINXOPTS =
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SPHINXBUILD = sphinx-build
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PAPER =
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BUILDDIR = _build
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# User-friendly check for sphinx-build
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ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
|
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$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
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endif
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# Internal variables.
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PAPEROPT_a4 = -D latex_paper_size=a4
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PAPEROPT_letter = -D latex_paper_size=letter
|
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ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
|
||||
# the i18n builder cannot share the environment and doctrees with the others
|
||||
I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
|
||||
|
||||
.PHONY: help
|
||||
help:
|
||||
@echo "Please use \`make <target>' where <target> is one of"
|
||||
@echo " html to make standalone HTML files"
|
||||
@echo " dirhtml to make HTML files named index.html in directories"
|
||||
@echo " singlehtml to make a single large HTML file"
|
||||
@echo " pickle to make pickle files"
|
||||
@echo " json to make JSON files"
|
||||
@echo " htmlhelp to make HTML files and a HTML help project"
|
||||
@echo " qthelp to make HTML files and a qthelp project"
|
||||
@echo " applehelp to make an Apple Help Book"
|
||||
@echo " devhelp to make HTML files and a Devhelp project"
|
||||
@echo " epub to make an epub"
|
||||
@echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
|
||||
@echo " latexpdf to make LaTeX files and run them through pdflatex"
|
||||
@echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
|
||||
@echo " text to make text files"
|
||||
@echo " man to make manual pages"
|
||||
@echo " texinfo to make Texinfo files"
|
||||
@echo " info to make Texinfo files and run them through makeinfo"
|
||||
@echo " gettext to make PO message catalogs"
|
||||
@echo " changes to make an overview of all changed/added/deprecated items"
|
||||
@echo " xml to make Docutils-native XML files"
|
||||
@echo " pseudoxml to make pseudoxml-XML files for display purposes"
|
||||
@echo " linkcheck to check all external links for integrity"
|
||||
@echo " doctest to run all doctests embedded in the documentation (if enabled)"
|
||||
@echo " coverage to run coverage check of the documentation (if enabled)"
|
||||
|
||||
.PHONY: clean
|
||||
clean:
|
||||
rm -rf $(BUILDDIR)/*
|
||||
|
||||
.PHONY: html
|
||||
html:
|
||||
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
|
||||
@echo
|
||||
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
|
||||
|
||||
.PHONY: dirhtml
|
||||
dirhtml:
|
||||
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
|
||||
@echo
|
||||
@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
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||||
|
||||
.PHONY: singlehtml
|
||||
singlehtml:
|
||||
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
|
||||
@echo
|
||||
@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
|
||||
|
||||
.PHONY: pickle
|
||||
pickle:
|
||||
$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
|
||||
@echo
|
||||
@echo "Build finished; now you can process the pickle files."
|
||||
|
||||
.PHONY: json
|
||||
json:
|
||||
$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
|
||||
@echo
|
||||
@echo "Build finished; now you can process the JSON files."
|
||||
|
||||
.PHONY: htmlhelp
|
||||
htmlhelp:
|
||||
$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
|
||||
@echo
|
||||
@echo "Build finished; now you can run HTML Help Workshop with the" \
|
||||
".hhp project file in $(BUILDDIR)/htmlhelp."
|
||||
|
||||
.PHONY: qthelp
|
||||
qthelp:
|
||||
$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
|
||||
@echo
|
||||
@echo "Build finished; now you can run "qcollectiongenerator" with the" \
|
||||
".qhcp project file in $(BUILDDIR)/qthelp, like this:"
|
||||
@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/package_template.qhcp"
|
||||
@echo "To view the help file:"
|
||||
@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/package_template.qhc"
|
||||
|
||||
.PHONY: applehelp
|
||||
applehelp:
|
||||
$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
|
||||
@echo
|
||||
@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
|
||||
@echo "N.B. You won't be able to view it unless you put it in" \
|
||||
"~/Library/Documentation/Help or install it in your application" \
|
||||
"bundle."
|
||||
|
||||
.PHONY: devhelp
|
||||
devhelp:
|
||||
$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
|
||||
@echo
|
||||
@echo "Build finished."
|
||||
@echo "To view the help file:"
|
||||
@echo "# mkdir -p $$HOME/.local/share/devhelp/package_template"
|
||||
@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/package_template"
|
||||
@echo "# devhelp"
|
||||
|
||||
.PHONY: epub
|
||||
epub:
|
||||
$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
|
||||
@echo
|
||||
@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
|
||||
|
||||
.PHONY: latex
|
||||
latex:
|
||||
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
|
||||
@echo
|
||||
@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
|
||||
@echo "Run \`make' in that directory to run these through (pdf)latex" \
|
||||
"(use \`make latexpdf' here to do that automatically)."
|
||||
|
||||
.PHONY: latexpdf
|
||||
latexpdf:
|
||||
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
|
||||
@echo "Running LaTeX files through pdflatex..."
|
||||
$(MAKE) -C $(BUILDDIR)/latex all-pdf
|
||||
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
|
||||
|
||||
.PHONY: latexpdfja
|
||||
latexpdfja:
|
||||
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
|
||||
@echo "Running LaTeX files through platex and dvipdfmx..."
|
||||
$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
|
||||
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
|
||||
|
||||
.PHONY: text
|
||||
text:
|
||||
$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
|
||||
@echo
|
||||
@echo "Build finished. The text files are in $(BUILDDIR)/text."
|
||||
|
||||
.PHONY: man
|
||||
man:
|
||||
$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
|
||||
@echo
|
||||
@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
|
||||
|
||||
.PHONY: texinfo
|
||||
texinfo:
|
||||
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
|
||||
@echo
|
||||
@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
|
||||
@echo "Run \`make' in that directory to run these through makeinfo" \
|
||||
"(use \`make info' here to do that automatically)."
|
||||
|
||||
.PHONY: info
|
||||
info:
|
||||
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
|
||||
@echo "Running Texinfo files through makeinfo..."
|
||||
make -C $(BUILDDIR)/texinfo info
|
||||
@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
|
||||
|
||||
.PHONY: gettext
|
||||
gettext:
|
||||
$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
|
||||
@echo
|
||||
@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
|
||||
|
||||
.PHONY: changes
|
||||
changes:
|
||||
$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
|
||||
@echo
|
||||
@echo "The overview file is in $(BUILDDIR)/changes."
|
||||
|
||||
.PHONY: linkcheck
|
||||
linkcheck:
|
||||
$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
|
||||
@echo
|
||||
@echo "Link check complete; look for any errors in the above output " \
|
||||
"or in $(BUILDDIR)/linkcheck/output.txt."
|
||||
|
||||
.PHONY: doctest
|
||||
doctest:
|
||||
$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
|
||||
@echo "Testing of doctests in the sources finished, look at the " \
|
||||
"results in $(BUILDDIR)/doctest/output.txt."
|
||||
|
||||
.PHONY: coverage
|
||||
coverage:
|
||||
$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
|
||||
@echo "Testing of coverage in the sources finished, look at the " \
|
||||
"results in $(BUILDDIR)/coverage/python.txt."
|
||||
|
||||
.PHONY: xml
|
||||
xml:
|
||||
$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
|
||||
@echo
|
||||
@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
|
||||
|
||||
.PHONY: pseudoxml
|
||||
pseudoxml:
|
||||
$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
|
||||
@echo
|
||||
@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
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@ -0,0 +1,6 @@
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PyPortfolioOpt
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====================
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.. module::PyPortfolioOpt
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.. autofunction:: add
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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#
|
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# package_template documentation build configuration file, created by
|
||||
# sphinx-quickstart on Fri May 13 14:31:17 2016.
|
||||
#
|
||||
# This file is execfile()d with the current directory set to its
|
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# containing dir.
|
||||
#
|
||||
# Note that not all possible configuration values are present in this
|
||||
# autogenerated file.
|
||||
#
|
||||
# All configuration values have a default; values that are commented out
|
||||
# serve to show the default.
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||||
|
||||
import sys
|
||||
import os
|
||||
|
||||
# If extensions (or modules to document with autodoc) are in another directory,
|
||||
# add these directories to sys.path here. If the directory is relative to the
|
||||
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
||||
# sys.path.insert(0, os.path.abspath('.'))
|
||||
|
||||
# -- General configuration ------------------------------------------------
|
||||
|
||||
# If your documentation needs a minimal Sphinx version, state it here.
|
||||
# needs_sphinx = '1.0'
|
||||
|
||||
# Add any Sphinx extension module names here, as strings. They can be
|
||||
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
||||
# ones.
|
||||
extensions = ["sphinx.ext.autodoc"]
|
||||
|
||||
# Add any paths that contain templates here, relative to this directory.
|
||||
templates_path = ["_templates"]
|
||||
|
||||
# The suffix(es) of source filenames.
|
||||
# You can specify multiple suffix as a list of string:
|
||||
# source_suffix = ['.rst', '.md']
|
||||
source_suffix = ".rst"
|
||||
|
||||
# The encoding of source files.
|
||||
# source_encoding = 'utf-8-sig'
|
||||
|
||||
# The master toctree document.
|
||||
master_doc = "index"
|
||||
|
||||
# General information about the project.
|
||||
project = "PyPortfolioOpt"
|
||||
copyright = "2018, Robert Andrew Martin"
|
||||
author = "Robert Andrew Martin"
|
||||
|
||||
# The version info for the project you're documenting, acts as replacement for
|
||||
# |version| and |release|, also used in various other places throughout the
|
||||
# built documents.
|
||||
#
|
||||
# The short X.Y version.
|
||||
version = "0.1"
|
||||
# The full version, including alpha/beta/rc tags.
|
||||
release = "0.1"
|
||||
|
||||
# The language for content autogenerated by Sphinx. Refer to documentation
|
||||
# for a list of supported languages.
|
||||
#
|
||||
# This is also used if you do content translation via gettext catalogs.
|
||||
# Usually you set "language" from the command line for these cases.
|
||||
language = None
|
||||
|
||||
# There are two options for replacing |today|: either, you set today to some
|
||||
# non-false value, then it is used:
|
||||
# today = ''
|
||||
# Else, today_fmt is used as the format for a strftime call.
|
||||
# today_fmt = '%B %d, %Y'
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
exclude_patterns = ["_build"]
|
||||
|
||||
# The reST default role (used for this markup: `text`) to use for all
|
||||
# documents.
|
||||
# default_role = None
|
||||
|
||||
# If true, '()' will be appended to :func: etc. cross-reference text.
|
||||
# add_function_parentheses = True
|
||||
|
||||
# If true, the current module name will be prepended to all description
|
||||
# unit titles (such as .. function::).
|
||||
# add_module_names = True
|
||||
|
||||
# If true, sectionauthor and moduleauthor directives will be shown in the
|
||||
# output. They are ignored by default.
|
||||
# show_authors = False
|
||||
|
||||
# The name of the Pygments (syntax highlighting) style to use.
|
||||
pygments_style = "sphinx"
|
||||
|
||||
# A list of ignored prefixes for module index sorting.
|
||||
# modindex_common_prefix = []
|
||||
|
||||
# If true, keep warnings as "system message" paragraphs in the built documents.
|
||||
# keep_warnings = False
|
||||
|
||||
# If true, `todo` and `todoList` produce output, else they produce nothing.
|
||||
todo_include_todos = False
|
||||
|
||||
|
||||
# -- Options for HTML output ----------------------------------------------
|
||||
|
||||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
html_theme = "alabaster"
|
||||
|
||||
# Theme options are theme-specific and customize the look and feel of a theme
|
||||
# further. For a list of options available for each theme, see the
|
||||
# documentation.
|
||||
# html_theme_options = {}
|
||||
|
||||
# Add any paths that contain custom themes here, relative to this directory.
|
||||
# html_theme_path = []
|
||||
|
||||
# The name for this set of Sphinx documents. If None, it defaults to
|
||||
# "<project> v<release> documentation".
|
||||
# html_title = None
|
||||
|
||||
# A shorter title for the navigation bar. Default is the same as html_title.
|
||||
# html_short_title = None
|
||||
|
||||
# The name of an image file (relative to this directory) to place at the top
|
||||
# of the sidebar.
|
||||
# html_logo = None
|
||||
|
||||
# The name of an image file (within the static path) to use as favicon of the
|
||||
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
|
||||
# pixels large.
|
||||
# html_favicon = None
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ["_static"]
|
||||
|
||||
# Add any extra paths that contain custom files (such as robots.txt or
|
||||
# .htaccess) here, relative to this directory. These files are copied
|
||||
# directly to the root of the documentation.
|
||||
# html_extra_path = []
|
||||
|
||||
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
|
||||
# using the given strftime format.
|
||||
# html_last_updated_fmt = '%b %d, %Y'
|
||||
|
||||
# If true, SmartyPants will be used to convert quotes and dashes to
|
||||
# typographically correct entities.
|
||||
# html_use_smartypants = True
|
||||
|
||||
# Custom sidebar templates, maps document names to template names.
|
||||
# html_sidebars = {}
|
||||
|
||||
# Additional templates that should be rendered to pages, maps page names to
|
||||
# template names.
|
||||
# html_additional_pages = {}
|
||||
|
||||
# If false, no module index is generated.
|
||||
# html_domain_indices = True
|
||||
|
||||
# If false, no index is generated.
|
||||
# html_use_index = True
|
||||
|
||||
# If true, the index is split into individual pages for each letter.
|
||||
# html_split_index = False
|
||||
|
||||
# If true, links to the reST sources are added to the pages.
|
||||
# html_show_sourcelink = True
|
||||
|
||||
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
|
||||
# html_show_sphinx = True
|
||||
|
||||
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
|
||||
# html_show_copyright = True
|
||||
|
||||
# If true, an OpenSearch description file will be output, and all pages will
|
||||
# contain a <link> tag referring to it. The value of this option must be the
|
||||
# base URL from which the finished HTML is served.
|
||||
# html_use_opensearch = ''
|
||||
|
||||
# This is the file name suffix for HTML files (e.g. ".xhtml").
|
||||
# html_file_suffix = None
|
||||
|
||||
# Language to be used for generating the HTML full-text search index.
|
||||
# Sphinx supports the following languages:
|
||||
# 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja'
|
||||
# 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr'
|
||||
# html_search_language = 'en'
|
||||
|
||||
# A dictionary with options for the search language support, empty by default.
|
||||
# Now only 'ja' uses this config value
|
||||
# html_search_options = {'type': 'default'}
|
||||
|
||||
# The name of a javascript file (relative to the configuration directory) that
|
||||
# implements a search results scorer. If empty, the default will be used.
|
||||
# html_search_scorer = 'scorer.js'
|
||||
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = "package_templatedoc"
|
||||
|
||||
# -- Options for LaTeX output ---------------------------------------------
|
||||
|
||||
latex_elements = {
|
||||
# The paper size ('letterpaper' or 'a4paper').
|
||||
# 'papersize': 'letterpaper',
|
||||
# The font size ('10pt', '11pt' or '12pt').
|
||||
# 'pointsize': '10pt',
|
||||
# Additional stuff for the LaTeX preamble.
|
||||
# 'preamble': '',
|
||||
# Latex figure (float) alignment
|
||||
# 'figure_align': 'htbp',
|
||||
}
|
||||
|
||||
# Grouping the document tree into LaTeX files. List of tuples
|
||||
# (source start file, target name, title,
|
||||
# author, documentclass [howto, manual, or own class]).
|
||||
latex_documents = [
|
||||
(
|
||||
master_doc,
|
||||
"package_template.tex",
|
||||
"package\\_template Documentation",
|
||||
"Computational Modelling Group",
|
||||
"manual",
|
||||
)
|
||||
]
|
||||
|
||||
# The name of an image file (relative to this directory) to place at the top of
|
||||
# the title page.
|
||||
# latex_logo = None
|
||||
|
||||
# For "manual" documents, if this is true, then toplevel headings are parts,
|
||||
# not chapters.
|
||||
# latex_use_parts = False
|
||||
|
||||
# If true, show page references after internal links.
|
||||
# latex_show_pagerefs = False
|
||||
|
||||
# If true, show URL addresses after external links.
|
||||
# latex_show_urls = False
|
||||
|
||||
# Documents to append as an appendix to all manuals.
|
||||
# latex_appendices = []
|
||||
|
||||
# If false, no module index is generated.
|
||||
# latex_domain_indices = True
|
||||
|
||||
|
||||
# -- Options for manual page output ---------------------------------------
|
||||
|
||||
# One entry per manual page. List of tuples
|
||||
# (source start file, name, description, authors, manual section).
|
||||
man_pages = [
|
||||
(master_doc, "package_template", "package_template Documentation", [author], 1)
|
||||
]
|
||||
|
||||
# If true, show URL addresses after external links.
|
||||
# man_show_urls = False
|
||||
|
||||
|
||||
# -- Options for Texinfo output -------------------------------------------
|
||||
|
||||
# Grouping the document tree into Texinfo files. List of tuples
|
||||
# (source start file, target name, title, author,
|
||||
# dir menu entry, description, category)
|
||||
texinfo_documents = [
|
||||
(
|
||||
master_doc,
|
||||
"package_template",
|
||||
"package_template Documentation",
|
||||
author,
|
||||
"package_template",
|
||||
"One line description of project.",
|
||||
"Miscellaneous",
|
||||
)
|
||||
]
|
||||
|
||||
# Documents to append as an appendix to all manuals.
|
||||
# texinfo_appendices = []
|
||||
|
||||
# If false, no module index is generated.
|
||||
# texinfo_domain_indices = True
|
||||
|
||||
# How to display URL addresses: 'footnote', 'no', or 'inline'.
|
||||
# texinfo_show_urls = 'footnote'
|
||||
|
||||
# If true, do not generate a @detailmenu in the "Top" node's menu.
|
||||
# texinfo_no_detailmenu = False
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
Package template |version|
|
||||
==========================
|
||||
|
||||
This is sample documentation for the Python package template. To start using
|
||||
Sphinx docs, create an empty ``docs`` folder and run::
|
||||
|
||||
sphinx-quickstart
|
||||
|
||||
Contents:
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
arith
|
||||
Binary file not shown.
|
|
@ -0,0 +1 @@
|
|||
{"last_check":"2018-05-27T02:16:45Z","pypi_version":"10.0.1"}
|
||||
|
|
@ -0,0 +1 @@
|
|||
|
||||
|
|
@ -0,0 +1,74 @@
|
|||
import numpy as np
|
||||
|
||||
# TODO discrete portfolio allocation input and return types
|
||||
|
||||
|
||||
def discrete_portfolio_allocation(
|
||||
weights, min_allocation, portfolio_size, latest_prices
|
||||
):
|
||||
"""
|
||||
Generates a discrete allocation based on continuous weights, using a greedy algorithm,
|
||||
then stores in an instance variable as a list of tuples
|
||||
:return: 1. a dict containing the ticker and the number of shares that should be purchased,
|
||||
2. leftover funds
|
||||
"""
|
||||
# Drop any companies with negligible weights
|
||||
nonzero_cont_allocation = [i for i in weights if i[1] > min_allocation]
|
||||
remaining_tickers = [i[0] for i in nonzero_cont_allocation]
|
||||
print(
|
||||
f"{len(weights) - len(nonzero_cont_allocation)} out of {len(weights)} tickers were removed"
|
||||
)
|
||||
print(f"Remaining tickers: {remaining_tickers}\n")
|
||||
nonzero_cont_allocation.sort(key=lambda x: x[1])
|
||||
nonzero_cont_allocation = nonzero_cont_allocation[::-1]
|
||||
available_funds = portfolio_size
|
||||
shares_purchased = []
|
||||
share_prices = []
|
||||
|
||||
for pair in nonzero_cont_allocation:
|
||||
ticker, weight = pair
|
||||
share_price = latest_prices[ticker]
|
||||
|
||||
n_shares = int(weight * portfolio_size / share_price)
|
||||
cost_basis = n_shares * share_price
|
||||
if cost_basis > available_funds:
|
||||
n_shares = int(available_funds // share_price)
|
||||
if n_shares == 0:
|
||||
print("Insufficient funds")
|
||||
available_funds -= cost_basis
|
||||
shares_purchased.append(n_shares)
|
||||
share_prices.append(share_price)
|
||||
|
||||
# Second round
|
||||
while available_funds > 0:
|
||||
actual_weights = np.array(share_prices) * np.array(shares_purchased)
|
||||
actual_weights /= actual_weights.sum()
|
||||
ideal_weights = np.array([i[1] for i in nonzero_cont_allocation])
|
||||
deficit = ideal_weights - actual_weights
|
||||
|
||||
idx = np.argmax(deficit)
|
||||
ticker, weight = nonzero_cont_allocation[idx]
|
||||
share_price = latest_prices[ticker]
|
||||
|
||||
counter = 0
|
||||
while share_price > available_funds:
|
||||
# Find the second highest deficit and carry on
|
||||
deficit[idx] = 0
|
||||
idx = np.argmax(deficit)
|
||||
if deficit[idx] < 0 or counter == 10:
|
||||
break
|
||||
|
||||
ticker, weight = nonzero_cont_allocation[idx]
|
||||
share_price = latest_prices[ticker]
|
||||
counter += 1
|
||||
|
||||
if deficit[idx] < 0 or counter == 10:
|
||||
break
|
||||
shares_purchased[idx] += 1
|
||||
available_funds -= share_price
|
||||
|
||||
print(f"Funds remaining: {available_funds:.2f}")
|
||||
|
||||
# The instance variable is a list of tuples, while the returned value is a dict.
|
||||
num_shares = list(zip([i[0] for i in nonzero_cont_allocation], shares_purchased))
|
||||
return dict(num_shares), available_funds
|
||||
|
|
@ -0,0 +1,173 @@
|
|||
import numpy as np
|
||||
import scipy.optimize as sco
|
||||
from . import objective_functions
|
||||
import warnings
|
||||
|
||||
# TODO investigate market neutral for efficient risk
|
||||
|
||||
|
||||
class EfficientFrontier:
|
||||
|
||||
def __init__(self, expected_returns, cov_matrix, weight_bounds=(0, 1)):
|
||||
"""
|
||||
:param expected_returns: expected returns for each asset
|
||||
:type expected_returns: pd.Series, list, np vector.
|
||||
:param cov_matrix: covariance of returns for each asset
|
||||
:type cov_matrix: pd.DataFrame or np.array
|
||||
:param weight_bounds: minimum and maximum weight of an asset, defaults to (0, 1)
|
||||
:param weight_bounds: tuple, optional
|
||||
"""
|
||||
# Inputs
|
||||
self.expected_returns = expected_returns
|
||||
self.cov_matrix = cov_matrix
|
||||
self.n_assets = len(expected_returns)
|
||||
self.tickers = list(expected_returns.index)
|
||||
# Optimisation parameters
|
||||
self.initial_guess = np.array([1 / self.n_assets] * self.n_assets)
|
||||
self.constraints = [{"type": "eq", "fun": lambda x: np.sum(x) - 1}]
|
||||
self.bounds = self._make_valid_bounds(weight_bounds)
|
||||
# Optional
|
||||
self.risk_free_rate = 0.02
|
||||
# Outputs
|
||||
self.weights = None
|
||||
|
||||
def _make_valid_bounds(self, test_bounds):
|
||||
if test_bounds[0] is not None:
|
||||
if test_bounds[0] * self.n_assets > 1:
|
||||
raise ValueError("Lower bound is too high")
|
||||
|
||||
return (test_bounds,) * self.n_assets
|
||||
|
||||
def max_sharpe(self, alpha=0, risk_free_rate=0.02):
|
||||
"""
|
||||
The 'tangent' portfolio that maximises the Sharpe Ratio. The Sharpe ratio is defined as
|
||||
.. math::
|
||||
\frac{\mu - R_f}{\sigma}
|
||||
:param risk_free_rate: risk free rate of borrowing/lending, defaults to 0.02
|
||||
:type risk_free_rate: float, optional
|
||||
:return: portfolio weights
|
||||
:rtype: dictionary: keys are tickers (string), values are weights (float)
|
||||
"""
|
||||
self.risk_free_rate = risk_free_rate
|
||||
args = (self.expected_returns, self.cov_matrix, alpha, risk_free_rate)
|
||||
constraints = self.constraints
|
||||
|
||||
result = sco.minimize(
|
||||
objective_functions.negative_sharpe,
|
||||
x0=self.initial_guess,
|
||||
args=args,
|
||||
method="SLSQP",
|
||||
bounds=self.bounds,
|
||||
constraints=constraints,
|
||||
)
|
||||
self.weights = result["x"]
|
||||
return dict(zip(self.tickers, self.weights))
|
||||
|
||||
def min_volatility(self, alpha=0):
|
||||
args = (self.cov_matrix, alpha)
|
||||
|
||||
constraints = self.constraints
|
||||
|
||||
result = sco.minimize(
|
||||
objective_functions.volatility,
|
||||
x0=self.initial_guess,
|
||||
args=args,
|
||||
method="SLSQP",
|
||||
bounds=self.bounds,
|
||||
constraints=constraints,
|
||||
)
|
||||
self.weights = result["x"]
|
||||
return dict(zip(self.tickers, self.weights))
|
||||
|
||||
def efficient_risk(self, target_risk, alpha=0, risk_free_rate=0.02):
|
||||
"""
|
||||
Calculates the Sharpe-maximising portfolio for a given target risk
|
||||
:param self.expected_returns: array of mean returns for a number of stocks
|
||||
:param self.cov_matrix: covariance of these stocks.
|
||||
:param target_risk: the target return
|
||||
:param risk_free_rate: defaults to zero
|
||||
:return: the weights of the portfolio that minimise risk for this target return
|
||||
"""
|
||||
self.n_assets = len(self.expected_returns)
|
||||
args = (self.expected_returns, self.cov_matrix, alpha, risk_free_rate)
|
||||
|
||||
constraints = self.constraints + [
|
||||
{
|
||||
"type": "ineq",
|
||||
"fun": lambda w: target_risk
|
||||
- objective_functions.volatility(w, self.cov_matrix),
|
||||
}
|
||||
]
|
||||
|
||||
result = sco.minimize(
|
||||
objective_functions.negative_sharpe,
|
||||
x0=self.initial_guess,
|
||||
args=args,
|
||||
method="SLSQP",
|
||||
bounds=self.bounds,
|
||||
constraints=constraints,
|
||||
)
|
||||
self.weights = result["x"]
|
||||
return dict(zip(self.tickers, self.weights))
|
||||
|
||||
def efficient_return(self, target_return, alpha=0, market_neutral=False):
|
||||
"""
|
||||
Calculates the "Markowitz" portfolio, minimising risk for a target return
|
||||
:param self.expected_returns: array of mean returns for a number of stocks
|
||||
:param self.cov_matrix: covariance of these stocks.
|
||||
:param target_risk: the target return
|
||||
:param risk_free_rate: defaults to zero
|
||||
:return: the weights of the portfolio that minimise risk for this target return
|
||||
"""
|
||||
|
||||
self.n_assets = len(self.expected_returns)
|
||||
args = (self.cov_matrix, alpha)
|
||||
target_constraint = {
|
||||
"type": "eq",
|
||||
"fun": lambda w: w.dot(self.expected_returns) - target_return,
|
||||
}
|
||||
if market_neutral:
|
||||
if self.bounds[0][0] is not None and self.bounds[0][0] >= 0:
|
||||
warnings.warn(
|
||||
"Market neutrality requires shorting - bounds have been amended",
|
||||
RuntimeWarning,
|
||||
)
|
||||
self.bounds = self._make_valid_bounds((-1, 1))
|
||||
|
||||
constraints = [
|
||||
{"type": "eq", "fun": lambda x: np.sum(x)},
|
||||
target_constraint,
|
||||
]
|
||||
else:
|
||||
constraints = self.constraints + [target_constraint]
|
||||
|
||||
result = sco.minimize(
|
||||
objective_functions.volatility,
|
||||
x0=self.initial_guess,
|
||||
args=args,
|
||||
method="SLSQP",
|
||||
bounds=self.bounds,
|
||||
constraints=constraints,
|
||||
)
|
||||
self.weights = result["x"]
|
||||
return dict(zip(self.tickers, self.weights))
|
||||
|
||||
def portfolio_performance(self, verbose=False):
|
||||
"""
|
||||
Calculates the performance given the calculated weights of the portfolio
|
||||
:return: [description]
|
||||
:rtype: [type]
|
||||
"""
|
||||
if self.weights is None:
|
||||
raise ValueError("Weights not calculated yet")
|
||||
sigma = objective_functions.volatility(self.weights, self.cov_matrix)
|
||||
mu = self.weights.dot(self.expected_returns)
|
||||
|
||||
sharpe = -objective_functions.negative_sharpe(
|
||||
self.weights, self.expected_returns, self.cov_matrix, self.risk_free_rate
|
||||
)
|
||||
if verbose:
|
||||
print("Expected return:", mu)
|
||||
print("Volatility:", sigma)
|
||||
print("Sharpe:", sharpe)
|
||||
return mu, sigma, sharpe
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
"""
|
||||
This module implements possible models for the expected return.
|
||||
It is assumed that daily returns are provided, though in reality the below methods are agnostic
|
||||
to the time period (just changed the frequency parameter to annualise).
|
||||
"""
|
||||
|
||||
|
||||
def mean_historical_return(daily_returns, frequency=252):
|
||||
"""
|
||||
Annualises mean daily historical return.
|
||||
:param daily_returns: Daily returns, each row is a date and each column is a ticker
|
||||
:type daily_returns: pd.DataFrame
|
||||
:param frequency: number of days (more generally, number of your desired time period)
|
||||
in a trading year, defaults to 252 days.
|
||||
:param frequency: int, optional
|
||||
:return: annualised mean daily return
|
||||
:rtype: pd.Series
|
||||
"""
|
||||
return daily_returns.mean() * frequency
|
||||
|
||||
|
||||
def ema_historical_return(daily_returns, frequency=252, span=500):
|
||||
"""
|
||||
Annualised exponentially-weighted mean of daily historical return, giving
|
||||
higher weight to more recent data.
|
||||
:param daily_returns: Daily returns, each row is a date and each column is a ticker
|
||||
:type daily_returns: pd.DataFrame
|
||||
:param frequency: number of days (more generally, number of your desired time period)
|
||||
in a trading year, defaults to 252 days.
|
||||
:param frequency: int, optional
|
||||
:param span: the time period for the EMA, defaults to 500-day EMA.
|
||||
:type span: int, optional
|
||||
:return: annualised exponentially-weighted mean daily return
|
||||
:rtype: pd.Series
|
||||
"""
|
||||
return daily_returns.ewm(span=span).mean().iloc[-1] * frequency
|
||||
|
|
@ -0,0 +1,49 @@
|
|||
"""
|
||||
This model implements possible objective functions for efficient optimisation
|
||||
|
||||
:return: [description]
|
||||
:rtype: [type]
|
||||
"""
|
||||
import numpy as np
|
||||
|
||||
|
||||
def negative_mean_return(weights, expected_returns):
|
||||
"""
|
||||
Negative mean return of a portfolio
|
||||
:param weights: normalised weights
|
||||
:type weights: np.array
|
||||
:param expected_returns: mean returns of the assets
|
||||
:type expected_returns: pd.Series
|
||||
:return: negative mean return
|
||||
:rtype: float
|
||||
"""
|
||||
return -weights.dot(expected_returns)
|
||||
|
||||
|
||||
def negative_sharpe(
|
||||
weights, expected_returns, cov_matrix, alpha=0, risk_free_rate=0.02
|
||||
):
|
||||
"""
|
||||
Negative Sharpe Ratio of a given portfolio
|
||||
|
||||
:param weights: normalised weights
|
||||
:param expected_returns: mean returns for a number of stocks
|
||||
:param cov_matrix: covariance of these stocks.
|
||||
:param risk_free_rate: defaults to zero
|
||||
:return: the negative Sharpe ratio
|
||||
"""
|
||||
mu = weights.dot(expected_returns)
|
||||
sigma = np.sqrt(np.dot(weights, np.dot(cov_matrix, weights.T)))
|
||||
L2_reg = alpha * (weights ** 2).sum()
|
||||
return -(mu - risk_free_rate) / sigma + L2_reg
|
||||
|
||||
|
||||
def volatility(weights, cov_matrix, alpha=0):
|
||||
"""
|
||||
Volatility of a given portfolio
|
||||
:param weights: normalised weights
|
||||
:param cov_matrix: covariance of these stocks.
|
||||
:return:
|
||||
"""
|
||||
L2_reg = alpha * (weights ** 2).sum()
|
||||
return np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights))) + L2_reg
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
"""
|
||||
This module implements possible models for risk of a portfolio
|
||||
"""
|
||||
import pandas as pd
|
||||
import warnings
|
||||
|
||||
|
||||
def sample_cov(daily_returns):
|
||||
"""
|
||||
Calculates the sample covariance matrix of daily returns, then annualises.
|
||||
:param daily_returns: Daily returns, each row is a date and each column is a ticker
|
||||
:type daily_returns: pd.DataFrame or array-like
|
||||
:returns: annualised sample covariance matrix of daily returns
|
||||
:rtype: pd.DataFrame
|
||||
"""
|
||||
if not isinstance(daily_returns, pd.DataFrame):
|
||||
warnings.warn("daily_returns is not a dataframe", RuntimeWarning)
|
||||
daily_returns = pd.DataFrame(daily_returns)
|
||||
return daily_returns.cov() * 252
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
[pytest]
|
||||
addopts = -v --ignore=lib
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
home = /Users/Robert/anaconda/bin
|
||||
include-system-site-packages = false
|
||||
version = 3.6.5
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
python:
|
||||
version: 3
|
||||
pip_install: true
|
||||
|
||||
# For more fields that can be specified here, see:
|
||||
# http://docs.readthedocs.io/en/latest/yaml-config.html
|
||||
|
|
@ -0,0 +1,92 @@
|
|||
from pypfopt.efficient_frontier import EfficientFrontier
|
||||
from pypfopt.risk_models import sample_cov
|
||||
from pypfopt.expected_returns import mean_historical_return
|
||||
from pypfopt.tests.utilities_for_tests import setup_efficient_frontier
|
||||
import pandas as pd
|
||||
|
||||
|
||||
df = pd.read_csv("pypfopt/tests/stock_returns.csv", parse_dates=True, index_col="date")
|
||||
e_ret = mean_historical_return(df)
|
||||
cov = sample_cov(df)
|
||||
|
||||
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.max_sharpe()
|
||||
ef.portfolio_performance(verbose=True)
|
||||
"""
|
||||
Volatility: 0.21671629525656422
|
||||
Expected return: 0.33035542211545876
|
||||
Sharpe: 1.4320816150351678
|
||||
"""
|
||||
|
||||
ef = EfficientFrontier(e_ret, cov, weight_bounds=(0, 0.15))
|
||||
w = ef.max_sharpe()
|
||||
ef.portfolio_performance(verbose=True)
|
||||
"""
|
||||
Volatility: 0.21671629525656422
|
||||
Expected return: 0.33035542211545876
|
||||
Sharpe: 1.4320816150351678
|
||||
"""
|
||||
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.min_volatility()
|
||||
ef.portfolio_performance(verbose=True)
|
||||
"""
|
||||
Expected return: 0.1793245141665063
|
||||
Volatility: 0.15915107045094778
|
||||
Sharpe: 0.9981835740658117
|
||||
"""
|
||||
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.efficient_risk(0.19)
|
||||
ef.portfolio_performance(verbose=True)
|
||||
"""
|
||||
Expected return: 0.28577470210889416
|
||||
Volatility: 0.1900001239293301
|
||||
Sharpe: 1.3964928761303517
|
||||
"""
|
||||
|
||||
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.efficient_return(0.25)
|
||||
ef.portfolio_performance(verbose=True)
|
||||
"""
|
||||
Expected return: 0.2500000000006342
|
||||
Volatility: 0.17388540121530308
|
||||
Sharpe: 1.3205072040538786
|
||||
"""
|
||||
|
||||
ef = EfficientFrontier(e_ret, cov)
|
||||
sharpes = []
|
||||
for i in range(10):
|
||||
ef.max_sharpe(risk_free_rate=i / 100)
|
||||
sharpe = ef.portfolio_performance(verbose=True)[2]
|
||||
sharpes.append(sharpe)
|
||||
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.max_sharpe(alpha=1)
|
||||
sum(ef.weights > 0.02)
|
||||
ef.portfolio_performance(verbose=True)
|
||||
|
||||
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.min_volatility(alpha=1)
|
||||
sum(ef.weights > 0.02)
|
||||
ef.portfolio_performance(verbose=True)
|
||||
"""
|
||||
Expected return: 0.2211888419683154
|
||||
Volatility: 0.18050174016287326
|
||||
Sharpe: 1.1133499289183508
|
||||
"""
|
||||
|
||||
|
||||
# test shorts
|
||||
e_ret[::2] *= -1
|
||||
ef = EfficientFrontier(e_ret, cov, weight_bounds=(None, None))
|
||||
ef.max_sharpe()
|
||||
|
||||
|
||||
# market neutral
|
||||
ef = setup_efficient_frontier()
|
||||
ef.bounds = ((-1, 1),) * 20
|
||||
ef.max_sharpe(market_neutral=True)
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
from distutils.core import setup
|
||||
|
||||
with open("README.rst") as f:
|
||||
readme = f.read()
|
||||
|
||||
setup(
|
||||
name="PyPortfolioOpt",
|
||||
version="0.1",
|
||||
description="PyPortfolioOpt: Efficient Frontier, Black Litterman, Monte Carlo optimisation methods",
|
||||
long_description=readme,
|
||||
author="Robert Andrew Martin",
|
||||
author_email="martin.robertandrew @ gmail.com",
|
||||
packages=["pypfopt", "pypfopt.tests"],
|
||||
classifiers=[
|
||||
"Development Status :: 1 - Planning",
|
||||
"Environment :: Console",
|
||||
"Intended Audience :: Financial and Insurance Industry",
|
||||
"Intended Audience :: Science/Research",
|
||||
"License :: OSI Approved :: MIT License",
|
||||
"Natural Language :: English",
|
||||
"Operating System :: OS Independent",
|
||||
"Programming Language :: Python :: 3.6",
|
||||
"Programming Language :: Python :: 3 :: Only",
|
||||
"Topic :: Office/Business :: Financial",
|
||||
"Topic :: Office/Business :: Financial :: Investment",
|
||||
"Topic :: Scientific/Engineering :: Mathematics",
|
||||
],
|
||||
)
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,417 @@
|
|||
import pandas as pd
|
||||
from pypfopt.efficient_frontier import EfficientFrontier
|
||||
from tests.utilities_for_tests import get_data, setup_efficient_frontier
|
||||
import pytest
|
||||
import numpy as np
|
||||
import warnings
|
||||
|
||||
|
||||
def test_data_source():
|
||||
df = get_data()
|
||||
assert isinstance(df, pd.DataFrame)
|
||||
assert df.shape[1] == 20
|
||||
assert len(df) == 7125
|
||||
assert df.index.is_all_dates
|
||||
|
||||
|
||||
def test_portfolio_performance():
|
||||
ef = setup_efficient_frontier()
|
||||
with pytest.raises(ValueError):
|
||||
ef.portfolio_performance()
|
||||
ef.max_sharpe()
|
||||
assert ef.portfolio_performance()
|
||||
|
||||
|
||||
def test_max_sharpe_long_only():
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.max_sharpe()
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(),
|
||||
(0.3303554237026972, 0.21671629636481254, 1.4288438866031374),
|
||||
)
|
||||
|
||||
|
||||
def test_max_sharpe_short():
|
||||
ef = EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
|
||||
)
|
||||
w = ef.max_sharpe()
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(),
|
||||
(0.40723757138191374, 0.24823079451957306, 1.5524922427959371),
|
||||
)
|
||||
sharpe = ef.portfolio_performance()[2]
|
||||
|
||||
ef_long_only = setup_efficient_frontier()
|
||||
ef_long_only.max_sharpe()
|
||||
long_only_sharpe = ef_long_only.portfolio_performance()[2]
|
||||
|
||||
assert sharpe > long_only_sharpe
|
||||
|
||||
|
||||
def test_max_sharpe_L2_reg():
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.max_sharpe(alpha=1)
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(),
|
||||
(0.3062919882686126, 0.20291367026287507, 1.4087639167552641),
|
||||
)
|
||||
|
||||
|
||||
def test_max_sharpe_L2_reg_many_values():
|
||||
ef = setup_efficient_frontier()
|
||||
ef.max_sharpe()
|
||||
# Count the number of weights more 1%
|
||||
initial_number = sum(ef.weights > 0.01)
|
||||
for a in np.arange(0.5, 5, 0.5):
|
||||
ef.max_sharpe(alpha=a)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
new_number = sum(ef.weights > 0.01)
|
||||
# Higher alpha should reduce the number of small weights
|
||||
assert new_number >= initial_number
|
||||
initial_number = new_number
|
||||
|
||||
|
||||
def test_max_sharpe_L2_reg_limit_case():
|
||||
ef = setup_efficient_frontier()
|
||||
ef.max_sharpe(alpha=1e10)
|
||||
equal_weights = np.array([1 / ef.n_assets] * ef.n_assets)
|
||||
np.testing.assert_array_almost_equal(ef.weights, equal_weights)
|
||||
|
||||
|
||||
def test_max_sharpe_L2_reg_reduces_sharpe():
|
||||
# L2 reg should reduce the number of small weights at the cost of Sharpe
|
||||
ef_no_reg = setup_efficient_frontier()
|
||||
ef_no_reg.max_sharpe()
|
||||
sharpe_no_reg = ef_no_reg.portfolio_performance()[2]
|
||||
ef = setup_efficient_frontier()
|
||||
ef.max_sharpe(alpha=1)
|
||||
sharpe = ef.portfolio_performance()[2]
|
||||
|
||||
assert sharpe < sharpe_no_reg
|
||||
|
||||
|
||||
def test_max_sharpe_L2_reg_with_shorts():
|
||||
ef_no_reg = setup_efficient_frontier()
|
||||
ef_no_reg.max_sharpe()
|
||||
initial_number = sum(ef_no_reg.weights > 0.01)
|
||||
|
||||
ef = EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
|
||||
)
|
||||
w = ef.max_sharpe(alpha=1)
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(),
|
||||
(0.3236047844566581, 0.20241509723550233, 1.4969817524033966),
|
||||
)
|
||||
new_number = sum(ef.weights > 0.01)
|
||||
assert new_number >= initial_number
|
||||
|
||||
|
||||
def test_max_sharpe_risk_free_rate():
|
||||
ef = setup_efficient_frontier()
|
||||
ef.max_sharpe()
|
||||
_, _, initial_sharpe = ef.portfolio_performance()
|
||||
ef.max_sharpe(risk_free_rate=0.10)
|
||||
_, _, new_sharpe = ef.portfolio_performance()
|
||||
assert new_sharpe <= initial_sharpe
|
||||
|
||||
ef.max_sharpe(risk_free_rate=0)
|
||||
_, _, new_sharpe = ef.portfolio_performance()
|
||||
assert new_sharpe >= initial_sharpe
|
||||
|
||||
|
||||
def test_min_volatility():
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.min_volatility()
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(),
|
||||
(0.1793245141665063, 0.15915107045094778, 0.9981835740658117),
|
||||
)
|
||||
|
||||
|
||||
def test_min_volatility_short():
|
||||
ef = EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
|
||||
)
|
||||
w = ef.min_volatility()
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(),
|
||||
(0.17225673749865328, 0.15559209747801794, 0.9752992044136976),
|
||||
)
|
||||
|
||||
# Shorting should reduce volatility
|
||||
volatility = ef.portfolio_performance()[1]
|
||||
ef_long_only = setup_efficient_frontier()
|
||||
ef_long_only.min_volatility()
|
||||
long_only_volatility = ef_long_only.portfolio_performance()[1]
|
||||
assert volatility < long_only_volatility
|
||||
|
||||
|
||||
def test_min_volatility_L2_reg():
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.min_volatility(alpha=1)
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(),
|
||||
(0.2211888419683154, 0.18050174016287326, 1.1133499289183508),
|
||||
)
|
||||
|
||||
|
||||
def test_min_volatility_L2_reg_many_values():
|
||||
ef = setup_efficient_frontier()
|
||||
ef.min_volatility()
|
||||
# Count the number of weights more 1%
|
||||
initial_number = sum(ef.weights > 0.01)
|
||||
for a in np.arange(0.5, 5, 0.5):
|
||||
ef.min_volatility(alpha=a)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
new_number = sum(ef.weights > 0.01)
|
||||
# Higher alpha should reduce the number of small weights
|
||||
assert new_number >= initial_number
|
||||
initial_number = new_number
|
||||
|
||||
|
||||
def test_efficient_risk():
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.efficient_risk(0.19)
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(), (0.285775, 0.19, 1.396493), atol=1e-6
|
||||
)
|
||||
|
||||
|
||||
def test_efficient_risk_short():
|
||||
ef = EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
|
||||
)
|
||||
w = ef.efficient_risk(0.19)
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(),
|
||||
(0.30468522897560224, 0.19, 1.4947624032507056),
|
||||
atol=1e6,
|
||||
)
|
||||
sharpe = ef.portfolio_performance()[2]
|
||||
|
||||
ef_long_only = setup_efficient_frontier()
|
||||
ef_long_only.efficient_return(0.25)
|
||||
long_only_sharpe = ef_long_only.portfolio_performance()[2]
|
||||
|
||||
assert sharpe > long_only_sharpe
|
||||
|
||||
|
||||
def test_efficient_risk_many_values():
|
||||
ef = setup_efficient_frontier()
|
||||
for target_risk in np.arange(0.16, 0.21, 0.01):
|
||||
ef.efficient_risk(target_risk)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
volatility = ef.portfolio_performance()[1]
|
||||
assert abs(target_risk - volatility) < 0.05
|
||||
|
||||
|
||||
def test_efficient_risk_L2_reg():
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.efficient_risk(0.19, alpha=1)
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(),
|
||||
(0.2843888327412046, 0.19, 1.3895318474675356),
|
||||
atol=1e-6,
|
||||
)
|
||||
|
||||
|
||||
def test_efficient_risk_L2_reg_many_values():
|
||||
ef = setup_efficient_frontier()
|
||||
ef.efficient_risk(0.19)
|
||||
# Count the number of weights more 1%
|
||||
initial_number = sum(ef.weights > 0.01)
|
||||
for a in np.arange(0.5, 5, 0.5):
|
||||
ef.efficient_risk(0.19, alpha=a)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
new_number = sum(ef.weights > 0.01)
|
||||
# Higher alpha should reduce the number of small weights
|
||||
assert new_number >= initial_number
|
||||
initial_number = new_number
|
||||
|
||||
|
||||
def test_efficient_return():
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.efficient_return(0.25)
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(), (0.25, 0.173885, 1.320507), atol=1e-6
|
||||
)
|
||||
|
||||
|
||||
def test_efficient_return_many_values():
|
||||
ef = setup_efficient_frontier()
|
||||
for target_return in np.arange(0.19, 0.30, 0.01):
|
||||
ef.efficient_return(target_return)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
mean_return = ef.portfolio_performance()[0]
|
||||
assert abs(target_return - mean_return) < 0.05
|
||||
|
||||
|
||||
def test_efficient_return_short():
|
||||
ef = EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(None, None)
|
||||
)
|
||||
w = ef.efficient_return(0.25)
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(), (0.25, 0.16826260520748268, 1.3641098601259731)
|
||||
)
|
||||
sharpe = ef.portfolio_performance()[2]
|
||||
|
||||
ef_long_only = setup_efficient_frontier()
|
||||
ef_long_only.efficient_return(0.25)
|
||||
long_only_sharpe = ef_long_only.portfolio_performance()[2]
|
||||
|
||||
assert sharpe > long_only_sharpe
|
||||
|
||||
|
||||
def test_efficient_return_L2_reg():
|
||||
ef = setup_efficient_frontier()
|
||||
w = ef.efficient_return(0.25, alpha=1)
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
|
||||
np.testing.assert_allclose(
|
||||
ef.portfolio_performance(), (0.25, 0.18813935436629708, 1.221273523695721)
|
||||
)
|
||||
|
||||
|
||||
def test_efficient_return_L2_reg_many_values():
|
||||
ef = setup_efficient_frontier()
|
||||
ef.efficient_return(0.25)
|
||||
# Count the number of weights more 1%
|
||||
initial_number = sum(ef.weights > 0.01)
|
||||
for a in np.arange(0.5, 5, 0.5):
|
||||
ef.efficient_return(0.25, alpha=a)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
new_number = sum(ef.weights > 0.01)
|
||||
# Higher alpha should reduce the number of small weights
|
||||
assert new_number >= initial_number
|
||||
initial_number = new_number
|
||||
|
||||
|
||||
def test_efficient_return_market_neutral():
|
||||
ef = EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(-1, 1)
|
||||
)
|
||||
w = ef.efficient_return(0.25, market_neutral=True)
|
||||
assert isinstance(w, dict)
|
||||
assert list(w.keys()) == ef.tickers
|
||||
assert list(w.keys()) == list(ef.expected_returns.index)
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 0)
|
||||
assert (ef.weights < 1).all() and (ef.weights > -1).all()
|
||||
np.testing.assert_almost_equal(
|
||||
ef.portfolio_performance(),
|
||||
(0.24999999999755498, 0.20567338787141307, 1.1087493060316183),
|
||||
)
|
||||
sharpe = ef.portfolio_performance()[2]
|
||||
|
||||
ef_long_only = setup_efficient_frontier()
|
||||
ef_long_only.efficient_return(0.25)
|
||||
long_only_sharpe = ef_long_only.portfolio_performance()[2]
|
||||
assert long_only_sharpe > sharpe
|
||||
|
||||
|
||||
def test_efficient_return_market_neutral_warning():
|
||||
ef = setup_efficient_frontier()
|
||||
with warnings.catch_warnings(record=True) as w:
|
||||
ef.efficient_return(0.25, market_neutral=True)
|
||||
assert len(w) == 1
|
||||
assert issubclass(w[0].category, RuntimeWarning)
|
||||
assert (
|
||||
str(w[0].message)
|
||||
== "Market neutrality requires shorting - bounds have been amended"
|
||||
)
|
||||
|
||||
|
||||
def test_custom_upper_bound():
|
||||
ef = EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(0, 0.10)
|
||||
)
|
||||
ef.max_sharpe()
|
||||
ef.portfolio_performance()
|
||||
assert ef.weights.max() <= 0.1
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
|
||||
|
||||
def test_custom_lower_bound():
|
||||
ef = EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(0.02, 1)
|
||||
)
|
||||
ef.max_sharpe()
|
||||
assert ef.weights.min() >= 0.02
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
|
||||
|
||||
def test_custom_bounds():
|
||||
ef = EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(0.03, 0.13)
|
||||
)
|
||||
ef.max_sharpe()
|
||||
assert ef.weights.min() >= 0.03
|
||||
assert ef.weights.max() <= 0.13
|
||||
np.testing.assert_almost_equal(ef.weights.sum(), 1)
|
||||
|
||||
|
||||
def test_custom_bounds_error():
|
||||
with pytest.raises(ValueError):
|
||||
EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(0.06, 1)
|
||||
)
|
||||
assert EfficientFrontier(
|
||||
*setup_efficient_frontier(data_only=True), weight_bounds=(0, 1)
|
||||
)
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
from pypfopt import objective_functions
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from tests.utilities_for_tests import get_data
|
||||
from pypfopt.expected_returns import mean_historical_return
|
||||
from pypfopt.risk_models import sample_cov
|
||||
|
||||
|
||||
def test_negative_mean_return_dummy():
|
||||
w = np.array([0.3, 0.1, 0.2, 0.25, 0.15])
|
||||
e_rets = pd.Series([0.19, 0.08, 0.09, 0.23, 0.17])
|
||||
|
||||
negative_mu = objective_functions.negative_mean_return(w, e_rets)
|
||||
assert isinstance(negative_mu, float)
|
||||
assert negative_mu < 0
|
||||
assert negative_mu == -w.dot(e_rets)
|
||||
assert negative_mu == -(w * e_rets).sum()
|
||||
|
||||
|
||||
def test_negative_mean_return_real():
|
||||
df = get_data()
|
||||
e_rets = mean_historical_return(df)
|
||||
w = np.array([1 / len(e_rets)] * len(e_rets))
|
||||
negative_mu = objective_functions.negative_mean_return(w, e_rets)
|
||||
assert isinstance(negative_mu, float)
|
||||
assert negative_mu < 0
|
||||
assert negative_mu == -w.dot(e_rets)
|
||||
assert negative_mu == -(w * e_rets).sum()
|
||||
np.testing.assert_almost_equal(-e_rets.sum() / len(e_rets), negative_mu)
|
||||
|
||||
|
||||
def test_negative_sharpe():
|
||||
df = get_data()
|
||||
e_rets = mean_historical_return(df)
|
||||
S = sample_cov(df)
|
||||
w = np.array([1 / len(e_rets)] * len(e_rets))
|
||||
|
||||
sharpe = objective_functions.negative_sharpe(w, e_rets, S)
|
||||
assert isinstance(sharpe, float)
|
||||
assert sharpe < 0
|
||||
|
||||
sigma = np.sqrt(np.dot(w, np.dot(S, w.T)))
|
||||
negative_mu = objective_functions.negative_mean_return(w, e_rets)
|
||||
np.testing.assert_almost_equal(sharpe * sigma - 0.02, negative_mu)
|
||||
|
||||
# Risk free rate increasing should lead to negative Sharpe increasing.
|
||||
assert sharpe < objective_functions.negative_sharpe(
|
||||
w, e_rets, S, risk_free_rate=0.1
|
||||
)
|
||||
|
||||
|
||||
def test_volatility_dummy():
|
||||
w = np.array([0.4, 0.4, 0.2])
|
||||
data = np.diag([0.5, 0.8, 0.9])
|
||||
test_vol = objective_functions.volatility(w, data)
|
||||
np.testing.assert_almost_equal(test_vol, 0.244 ** 0.5)
|
||||
|
||||
|
||||
def test_volatility():
|
||||
df = get_data()
|
||||
S = sample_cov(df)
|
||||
w = np.array([1 / df.shape[1]] * df.shape[1])
|
||||
vol = objective_functions.volatility(w, S)
|
||||
np.testing.assert_almost_equal(vol, 0.21209018103844543)
|
||||
|
|
@ -0,0 +1,52 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
from pypfopt import risk_models
|
||||
from tests.utilities_for_tests import get_data
|
||||
import warnings
|
||||
|
||||
|
||||
def test_sample_cov_dummy():
|
||||
data = pd.DataFrame(
|
||||
[
|
||||
[4.0, 2.0, 0.6],
|
||||
[4.2, 2.1, 0.59],
|
||||
[3.9, 2.0, 0.58],
|
||||
[4.3, 2.1, 0.62],
|
||||
[4.1, 2.2, 0.63],
|
||||
]
|
||||
)
|
||||
test_answer = pd.DataFrame(
|
||||
[
|
||||
[0.02500, 0.00750, 0.00175],
|
||||
[0.00750, 0.00700, 0.00135],
|
||||
[0.00175, 0.00135, 0.00043],
|
||||
]
|
||||
)
|
||||
S = risk_models.sample_cov(data) / 252
|
||||
pd.testing.assert_frame_equal(S, test_answer)
|
||||
|
||||
|
||||
def test_sample_cov_real():
|
||||
df = get_data()
|
||||
S = risk_models.sample_cov(df)
|
||||
assert S.shape == (20, 20)
|
||||
assert S.index.equals(df.columns)
|
||||
assert S.index.equals(S.columns)
|
||||
assert S.notnull().all().all()
|
||||
|
||||
|
||||
def test_sample_cov_type_warning():
|
||||
df = get_data()
|
||||
cov_from_df = risk_models.sample_cov(df)
|
||||
|
||||
returns_as_array = np.array(df)
|
||||
with warnings.catch_warnings(record=True) as w:
|
||||
cov_from_array = risk_models.sample_cov(returns_as_array)
|
||||
|
||||
assert len(w) == 1
|
||||
assert issubclass(w[0].category, RuntimeWarning)
|
||||
assert str(w[0].message) == "daily_returns is not a dataframe"
|
||||
|
||||
np.testing.assert_array_almost_equal(
|
||||
cov_from_df.values, cov_from_array.values, decimal=6
|
||||
)
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
import pandas as pd
|
||||
from pypfopt import expected_returns
|
||||
from pypfopt import risk_models
|
||||
from pypfopt.efficient_frontier import EfficientFrontier
|
||||
|
||||
|
||||
def get_data():
|
||||
return pd.read_csv("tests/stock_returns.csv", parse_dates=True, index_col="date")
|
||||
|
||||
|
||||
def setup_efficient_frontier(data_only=False):
|
||||
df = get_data()
|
||||
mean_return = expected_returns.mean_historical_return(df)
|
||||
sample_cov_matrix = risk_models.sample_cov(df)
|
||||
if data_only:
|
||||
return mean_return, sample_cov_matrix
|
||||
return EfficientFrontier(mean_return, sample_cov_matrix)
|
||||
Loading…
Reference in New Issue