pat42w / EF_Portfolio_Optimization

This project aims to test Portfolio Optimization methods on stock data in python.

EF_Portfolio_Optimization

Introduction

This project began as an attempt to test portfolio generation methods.

We soon realised that to consolidate stock price data was no small task, so our initial focus became building out libraries for data acquisition.

When researching algorithms for portfolio generation, we found Efficient Frontier methods interesting, and to use these have leveraged a great package called Py Portfolio Opt.

We step through the theory behind these methods & using them in Python in EF_method_example.ipynb.

Our homebrew functions are at DatabaseMainFnc.py; if you see room for improvements please let us know through Issues and Pull Requests.

Currently we have compiled ticker lists for NASDAQ & NYSE exchanges, but more can be easily added. The code currently expects ticker lists in TSV format. If you compile these do share them & we will add them to the repo.

Notebooks

We attempt to split out our proccess into 3 clear parts, each with its' own notebook, and make these as modular and self-explanatory as possible.

1. Gathering Stock Price & Forex Rates

Database_maintainance.ipynb

2. Establishing Historic Performance

Historic_portfolio_nb.ipynb

3. Analysing Historic Performance

EF_analysis.ipynb

Notes

Using Semantic Versioning for version control.

Authors

Patrick Walsh BSc.
Email: [e-mail]

Eoghan O'Hara BSc.