[MNT] fix all URLs and main branch references after move to new org

.github/ISSUE_TEMPLATE/workflows/main.yml

@@ -3,7 +3,7 @@ on:
     pull_request:
     push:
         branches:
-            - master
+            - main
 
 jobs:
     pytest:

.github/workflows/codecov.yml

@@ -3,7 +3,7 @@ on:
   pull_request:
   push:
     branches:
-      - master
+      - main
 
 jobs:
   codecov:

.github/workflows/main.yml

@@ -5,9 +5,9 @@ name: pytest
 
 on:
   push:
-    branches: ["master"]
+    branches: ["main"]
   pull_request:
-    branches: ["master"]
+    branches: ["main"]
 
 jobs:
   pytest:

CONTRIBUTING.md

@@ -4,7 +4,7 @@ Please refer to the roadmap for a list of areas that I think PyPortfolioOpt coul
 from. In addition, the following is always welcome::
 
 - Improve performance of existing code (but not at the cost of readability) – are there any nice numpy tricks I've missed?
-- Add new optimization objective functions. For example, if you think that the best performance metric has not been included, write it into a function (or suggest it in [Issues](https://github.com/robertmartin8/PyPortfolioOpt/issues) and I will have a go).
+- Add new optimization objective functions. For example, if you think that the best performance metric has not been included, write it into a function (or suggest it in [Issues](https://github.com/pyportfolio/pyportfolioopt/issues) and I will have a go).
 - Help me write more tests! If you are someone learning about quant finance and/or unit testing in python, what better way to practice than to write some tests on an open-source project! Feel free to check for edge cases, or test performance on a dataset with more stocks.
 
 ## Guidelines
@@ -31,13 +31,13 @@ I would appreciate if changes are accompanied by relevant documentation – it d
 
 ## Questions
 
-If you have any questions related to the project, it is probably easiest to [raise an issue](https://github.com/robertmartin8/PyPortfolioOpt/issues), and I will tag it as a question.
+If you have any questions related to the project, it is probably easiest to [raise an issue](https://github.com/pyportfolio/pyportfolioopt/issues), and I will tag it as a question.
 
 If you have questions unrelated to the project, drop me an email – contact details can be found on my [website](https://reasonabledeviations.com/about/)
 
 ## Bugs/issues
 
-If you find any bugs or the portfolio optimization is not working as expected, feel free to [raise an issue](https://github.com/robertmartin8/PyPortfolioOpt/issues). I would ask that you provide the following information in the issue:
+If you find any bugs or the portfolio optimization is not working as expected, feel free to [raise an issue](https://github.com/pyportfolio/pyportfolioopt/issues). I would ask that you provide the following information in the issue:
 
 - Descriptive title so that other users can see the existing issues
 - Operating system, python version, and python distribution (optional).

README.md

@@ -1,5 +1,5 @@
 <p align="center">
-    <img width=60% src="https://github.com/robertmartin8/PyPortfolioOpt/blob/master/media/logo_v1.png?raw=true">
+    <img width=60% src="https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/media/logo_v1.png?raw=true">
 </p>
 
 <!-- buttons -->
@@ -16,16 +16,16 @@
     <a href="https://opensource.org/licenses/MIT">
         <img src="https://img.shields.io/badge/license-MIT-brightgreen.svg"
             alt="MIT license"></a> &nbsp;
-    <a href="https://github.com/robertmartin8/PyPortfolioOpt/actions">
-        <img src="https://github.com/robertmartin8/PyPortfolioOpt/actions/workflows/main.yml/badge.svg?branch=master"
+    <a href="https://github.com/pyportfolio/pyportfolioopt/actions">
+        <img src="https://github.com/pyportfolio/pyportfolioopt/actions/workflows/main.yml/badge.svg?branch=main"
             alt="build"></a> &nbsp;
-    <a href="https://app.codecov.io/gh/robertmartin8/PyPortfolioOpt">
-        <img src="https://codecov.io/github/robertmartin8/PyPortfolioOpt/coverage.svg?branch=main"
+    <a href="https://app.codecov.io/gh/pyportfolio/pyportfolioopt">
+        <img src="https://codecov.io/github/pyportfolio/pyportfolioopt/coverage.svg?branch=main"
             alt="codecov"></a> &nbsp;
     <a href="https://pepy.tech/project/pyportfolioopt">
         <img src="https://pepy.tech/badge/pyportfolioopt"
             alt="downloads"></a> &nbsp;
-    <a href="https://mybinder.org/v2/gh/robertmartin8/pyportfolioopt/master/?filepath=cookbook">
+    <a href="https://mybinder.org/v2/gh/pyportfolio/pyportfolioopt/main/?filepath=cookbook">
       <img src="https://mybinder.org/badge_logo.svg"
             alt="binder"></a> &nbsp;
 </p>
@@ -45,10 +45,10 @@ in a risk-efficient way.
 
 PyPortfolioOpt is now being maintained by [Tuan Tran](https://github.com/88d52bdba0366127fffca9dfa93895).
 
-Head over to the **[documentation on ReadTheDocs](https://pyportfolioopt.readthedocs.io/en/latest/)** to get an in-depth look at the project, or check out the [cookbook](https://github.com/robertmartin8/PyPortfolioOpt/tree/master/cookbook) to see some examples showing the full process from downloading data to building a portfolio.
+Head over to the **[documentation on ReadTheDocs](https://pyportfolioopt.readthedocs.io/en/latest/)** to get an in-depth look at the project, or check out the [cookbook](https://github.com/pyportfolio/pyportfolioopt/tree/main/cookbook) to see some examples showing the full process from downloading data to building a portfolio.
 
 <center>
-<img src="https://github.com/robertmartin8/PyPortfolioOpt/blob/master/media/conceptual_flowchart_v2.png?raw=true" style="width:70%;"/>
+<img src="https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/media/conceptual_flowchart_v2.png?raw=true" style="width:70%;"/>
 </center>
 
 ## Table of contents
@@ -74,7 +74,7 @@ Head over to the **[documentation on ReadTheDocs](https://pyportfolioopt.readthe
 
 ## Getting started
 
-If you would like to play with PyPortfolioOpt interactively in your browser, you may launch Binder [here](https://mybinder.org/v2/gh/robertmartin8/pyportfolioopt/master). It takes a
+If you would like to play with PyPortfolioOpt interactively in your browser, you may launch Binder [here](https://mybinder.org/v2/gh/pyportfolio/pyportfolioopt/main). It takes a
 while to set up, but it lets you try out the cookbook recipes without having to deal with all of the requirements.
 
 _Note: macOS users will need to install [Command Line Tools](https://osxdaily.com/2014/02/12/install-command-line-tools-mac-os-x/)._
@@ -126,13 +126,13 @@ For more information, please read [this guide](https://docker-curriculum.com/#in
 If you would like to make major changes to integrate this with your proprietary system, it probably makes sense to clone this repository and to just use the source code.
 
 ```bash
-git clone https://github.com/robertmartin8/PyPortfolioOpt
+git clone https://github.com/pyportfolio/pyportfolioopt
 ```
 
 Alternatively, you could try:
 
 ```bash
-pip install -e git+https://github.com/robertmartin8/PyPortfolioOpt.git
+pip install -e git+https://github.com/pyportfolio/pyportfolioopt.git
 ```
 
 ## A quick example
@@ -211,14 +211,14 @@ Discrete allocation: {'GOOG': 1, 'AAPL': 4, 'FB': 12, 'BABA': 4, 'BBY': 2,
 Funds remaining: $11.89
 ```
 
-_Disclaimer: nothing about this project constitues investment advice, and the author bears no responsibiltiy for your subsequent investment decisions. Please refer to the [license](https://github.com/robertmartin8/PyPortfolioOpt/blob/master/LICENSE.txt) for more information._
+_Disclaimer: nothing about this project constitues investment advice, and the author bears no responsibiltiy for your subsequent investment decisions. Please refer to the [license](https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/LICENSE.txt) for more information._
 
 ## An overview of classical portfolio optimization methods
 
 Harry Markowitz's 1952 paper is the undeniable classic, which turned portfolio optimization from an art into a science. The key insight is that by combining assets with different expected returns and volatilities, one can decide on a mathematically optimal allocation which minimises the risk for a target return – the set of all such optimal portfolios is referred to as the **efficient frontier**.
 
 <center>
-<img src="https://github.com/robertmartin8/PyPortfolioOpt/blob/master/media/efficient_frontier_white.png?raw=true" style="width:60%;"/>
+<img src="https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/media/efficient_frontier_white.png?raw=true" style="width:60%;"/>
 </center>
 
 Although much development has been made in the subject, more than half a century later, Markowitz's core ideas are still fundamentally important and see daily use in many portfolio management firms.
@@ -236,7 +236,7 @@ components while still making use of the framework that PyPortfolioOpt provides.
 
 ## Features
 
-In this section, we detail some of PyPortfolioOpt's available functionality. More examples are offered in the Jupyter notebooks [here](https://github.com/robertmartin8/PyPortfolioOpt/tree/master/cookbook). Another good resource is the [tests](https://github.com/robertmartin8/PyPortfolioOpt/tree/master/tests).
+In this section, we detail some of PyPortfolioOpt's available functionality. More examples are offered in the Jupyter notebooks [here](https://github.com/pyportfolio/pyportfolioopt/tree/main/cookbook). Another good resource is the [tests](https://github.com/pyportfolio/pyportfolioopt/tree/main/tests).
 
 A far more comprehensive version of this can be found on [ReadTheDocs](https://pyportfolioopt.readthedocs.io/en/latest/), as well as possible extensions for more advanced users.
 
@@ -272,7 +272,7 @@ The covariance matrix encodes not just the volatility of an asset, but also how
   - implemented in `sklearn.covariance`
 
 <p align="center">
-    <img width=60% src="https://github.com/robertmartin8/PyPortfolioOpt/blob/master/media/corrplot_white.png?raw=true">
+    <img width=60% src="https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/media/corrplot_white.png?raw=true">
 </p>
 
 (This plot was generated using `plotting.plot_covariance`)
@@ -420,7 +420,7 @@ BibTex::
 
 ## Contributing
 
-Contributions are _most welcome_. Have a look at the [Contribution Guide](https://github.com/robertmartin8/PyPortfolioOpt/blob/master/CONTRIBUTING.md) for more.
+Contributions are _most welcome_. Have a look at the [Contribution Guide](https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/CONTRIBUTING.md) for more.
 
 I'd like to thank all of the people who have contributed to PyPortfolioOpt since its release in 2018.
 Special shout-outs to:

cookbook/1-RiskReturnModels.ipynb

@@ -11,13 +11,13 @@
         "\n",
         "In this section, we compare how well the different risk models predict an out-of-sample covariance matrix, and how well the different returns models predict out-of-sample returns.\n",
         "\n",
-        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n",
         "    \n",
-        "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/robertmartin8/PyPortfolioOpt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n",
+        "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/1-RiskReturnModels.ipynb)\n",
         "    \n",
-        "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n",
+        "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n",
         "    \n",
-        "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n",
+        "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n",
         "\n",
         "## Risk models"
       ]
@@ -37,7 +37,7 @@
         "!pip install pandas numpy matplotlib PyPortfolioOpt\n",
         "import os\n",
         "if not os.path.isdir('data'):\n",
-        "    os.system('git clone https://github.com/robertmartin8/PyPortfolioOpt.git')\n",
+        "    os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n",
         "    os.chdir('PyPortfolioOpt/cookbook')"
       ]
     },

cookbook/2-Mean-Variance-Optimisation.ipynb

@@ -33,13 +33,13 @@
         "\n",
         "To download data, we will use `yfinance`, an excellent library that provides free price data from Yahoo Finance, no API key needed.\n",
         "\n",
-        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)\n",
         "    \n",
-        "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/robertmartin8/PyPortfolioOpt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)\n",
+        "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimisation.ipynb)\n",
         "    \n",
-        "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)\n",
+        "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)\n",
         "    \n",
-        "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)"
+        "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)"
       ]
     },
     {
@@ -57,7 +57,7 @@
         "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n",
         "import os\n",
         "if not os.path.isdir('data'):\n",
-        "    os.system('git clone https://github.com/robertmartin8/PyPortfolioOpt.git')\n",
+        "    os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n",
         "    os.chdir('PyPortfolioOpt/cookbook')"
       ]
     },

cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb

@@ -20,13 +20,13 @@
         "\n",
         "As discussed in the previous notebook, assets are an exogenous input (i.e you must come up with a list of tickers). We will use `yfinance` to download data for thesee tickers\n",
         "\n",
-        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n",
         "    \n",
-        "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/robertmartin8/PyPortfolioOpt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n",
+        "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n",
         "    \n",
-        "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n",
+        "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n",
         "    \n",
-        "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)"
+        "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)"
       ]
     },
     {
@@ -87,7 +87,7 @@
         "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n",
         "import os\n",
         "if not os.path.isdir('data'):\n",
-        "    os.system('git clone https://github.com/robertmartin8/PyPortfolioOpt.git')\n",
+        "    os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n",
         "    os.chdir('PyPortfolioOpt/cookbook')"
       ]
     },

cookbook/4-Black-Litterman-Allocation.ipynb

@@ -24,13 +24,13 @@
         "In addition to price data, constructing a market prior requires market-caps.\n",
         "\n",
         "\n",
-        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)\n",
         "    \n",
-        "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/robertmartin8/PyPortfolioOpt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)\n",
+        "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/4-Black-Litterman-Allocation.ipynb)\n",
         "    \n",
-        "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)\n",
+        "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)\n",
         "    \n",
-        "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/robertmartin8/PyPortfolioOpt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)"
+        "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)"
       ]
     },
     {
@@ -48,7 +48,7 @@
         "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n",
         "import os\n",
         "if not os.path.isdir('data'):\n",
-        "    os.system('git clone https://github.com/robertmartin8/PyPortfolioOpt.git')\n",
+        "    os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n",
         "    os.chdir('PyPortfolioOpt/cookbook')"
       ]
     },