Portfolio Allocation Optimizer

Disclaimer

This tool should be viewed as a statistical model of the stock market rather than investment advice. Use the Portfolio Allocation Optimizer completely at your own risk. None of the creators or hosts of this software assume any responsibility for financial loss or other damages resulting from its use.

What is it?

This portfolio optimizer is an Angular 2.0 web application that takes a list of stocks in your portfolio (or a hypothetical portfolio) and returns the optimal allocations for each stock. A backend Python SimpleHTTP server runs the data analysis and serves it up to the frontend Angular application.

What does "optimal" mean?

We choose to optimize the portfolio's Sharpe ratio. Sharpe ratio is a metric for risk-adjusted returns, so it penalizes portfolios that contain stocks with high volatility. Other metrics could be used and may be added in the future.

What pricing data are you using?

We use Yahoo Finance data from Quandl. Admittedly, there are better data sources, but Quandl is a great compromise for its ease of use, price (free), and accuracy.

Getting Started

Setup the Backend Data Service

1. Clone this repo using git clone https://github.com/mwytock0812/portfolio_optimizer.git.
2. Build the Anaconda environment.

• Install Anaconda. If you would like a lighter installation, follow the instructions to install Miniconda instead.
• cd into the application root, ./portfolio_optimizer/
• Use conda env create -f ./backend/environment.yml to install the Python dependencies for the backend. This will create a conda environment called stocks.
• Activate the stocks environment using source activate stocks on Linux/OS X or activate stocks on Windows. You can deactivate the conda environment using source deactivate on Linux/OS X or deactivate on Windows.

1. With the stocks environment activated, run python app.py from ./portfolio_optimizer/backend.
2. Navigate to http://localhost:8000 to verify that you see the "Success!" message.

• By default, the server runs on port 8000, you can specify the port by including a command line flag: python app.py --port=5678.

Setup The Frontend Application

1. Assuming you've already cloned the repo, navigate to its root, ./portfolio_optiizer.
2. Install Node.js and npm.
3. Install the Angular 2 command line interface: npm install -g angular-cli.
4. Run npm install to install node dependencies.
5. Run ng server to begin serving the application on http://localhost:4200.

Testing

Testing the Backend Data Service

1. cd into ./portfolio_optimizer/backend, activate the conda environment, and finally run python app.py . See Setup The Backend Data Service for more details on these steps.
2. Open a new shell session, cd into ./portfolio_optimizer/backend, and activate the conda environment.
3. Run python -m tornado.testing test.optimizer_test to test the optimizer module.
4. Run python -m tornado.testing test.app_test to test the backend app itself.