Release 0.1.0

CHANGELOG.md

@@ -0,0 +1,11 @@
+# Changes to the InvestOps Python package
+
+## Version 0.1.0 (2021-10-23)
+
+First beta version with the following features:
+
+- Portfolio diversification (`diversify.py`)
+- Portfolio weight normalization (`normalize.py`)
+- Random generation of portfolio weights and correlation matrix (`random.py`)
+- Check and fix correlation matrix (`check.py`)
+- Linear mapping (`maps.py`)

README.md

@@ -1,3 +1,66 @@
 # InvestOps
 
-Stay tuned, coming soon to a [cinema](https://www.youtube.com/watch?v=MFqxdvggAxM) near you!
+[Original repository on GitHub](https://github.com/Hvass-Labs/InvestOps)
+
+Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org)
+
+
+## Introduction
+
+This is a Python package with simple and effective tools for investing,
+including the following:
+
+- Portfolio diversification using the so-called "Hvass Diversification"
+  algorithm, which is extremely fast to compute and very robust to estimation
+  errors in the correlation matrix.
+  ([Tutorial](https://github.com/Hvass-Labs/InvestOps-Tutorials/blob/master/01_Portfolio_Diversification.ipynb))
+  ([Paper](https://ssrn.com/abstract=3942552))
+
+The InvestOps Python package is a distilled version of some of the research
+from the [FinanceOps](https://github.com/Hvass-Labs/FinanceOps) project,
+whose papers can be found on [SSRN](http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=1993051)
+and [GitHub](https://github.com/Hvass-Labs/Finance-Papers).
+
+
+## Tutorials
+
+[Tutorials](https://github.com/Hvass-Labs/InvestOps-Tutorials) are made as
+Python Notebooks which can be modified and run entirely on the internet
+or on your own computer.
+
+
+## Installation
+
+It is best to use a virtual environment when installing Python packages,
+so you can easily delete the environment again if something goes wrong.
+You write the following in a Linux terminal:
+
+    virtualenv investops-env
+
+Or you can use [Anaconda](https://www.anaconda.com/download) instead of a virtualenv:
+
+    conda create --name investops-env python=3
+
+Then you switch to the virtual environment:
+
+    source activate investops-env
+
+And then you can install the InvestOps package inside that virtual environment:
+
+    pip install investops   
+
+You can now import the InvestOps package in your Python program as follows:
+
+    import investops as iv
+
+    # Print the InvestOps version.
+    print(iv.__version__)
+
+
+## License (MIT)
+
+This is published under the [MIT License](https://github.com/Hvass-Labs/InvestOps/blob/main/LICENSE)
+which allows very broad use for both academic and commercial purposes.
+
+You are very welcome to modify and use this source-code in your own project.
+Please keep a link to the [original repository](https://github.com/Hvass-Labs/InvestOps).

investops/__init__.py

@@ -1,5 +1,2 @@
 # The version is also defined in setup.py and must be updated in both places.
-__version__ = "0.0.1"
-
-# Expose the following as top-level imports.
-
+__version__ = "0.1.0"

setup.py

@@ -2,7 +2,7 @@
 from setuptools import setup
 
 # Also defined in investops/__init__.py and must be updated in both places.
-MY_VERSION = '0.0.1'
+MY_VERSION = '0.1.0'
 
 setup(
     name='investops',
@@ -17,10 +17,11 @@
     author_email='[email protected]',
     url='https://github.com/Hvass-Labs/InvestOps',
     license='MIT',
-    keywords=['investing', 'portfolio optimization'],
+    keywords=['investing', 'portfolio optimization', 'hvass diversification'],
     install_requires=[
         'numpy',
         'pandas',
+        'numba',
     ],
 )