Markowitz Portfolio Optimization

Introduction

This project is a Python implementation of the Markowitz Portfolio Optimization algorithm. The algorithm is used to find the optimal allocation of assets in a portfolio to maximise the expected inflation-adjusted return. The algorithm is based on the work of Harry Markowitz, who won the Nobel Prize in Economics in 1990 for his work on portfolio theory.

Results

Key Findings

• Inflation-Resilient Asset Classes Identified: The analysis identified several asset classes that strongly correlate with inflation. For that, we explored treasury bonds, crude oil prices, the SPX index, real estate, gold futures, and bitcoin.

• Optimised Portfolio Performance: The optimised portfolio, constructed using the Markowitz framework and adjusted for inflation, outperforms a CPI-adjusted, equally weighted portfolio.

• Diversification and Constraints: The final portfolio includes a diversified mix of assets, each with specified weights, adhering to constraints like the sum of asset weights equaling 1 and individual asset weight upper and lower bounds.

• Performance Metrics: The optimized portfolio shows an expected annual return of 9.82%, a risk (annual standard deviation) of 19.96%, and a Sharpe ratio of 0.4922.

Conclusion: Effective Inflation-Resilient Strategy

Portfolios optimised for inflation resilience can significantly outperform traditional portfolios.

Optimised Portfolio Composition

Asset Class	Weight
GS10: 10-Year Treasury Constant Maturity Rate	5%
GS30: 30-Year Treasury Constant Maturity Rate	5%
DCOILWTICO: Crude Oil Prices: West Texas Intermediate (WTI)	5%
XLE: Energy Stocks (Energy Select Sector SPDR Fund)	15%
GC=F: Gold Futures	30%
FXE: Foreign Currencies (CurrencyShares Euro Trust)	5%
DBC: Commodities (Invesco DB Commodity Index Tracking Fund)	5%
BTC-USD: Cryptocurrencies (Bitcoin)	30%

Usage Instructions

To run the program, you must first install the required dependencies. This can be done by running the following commands in the terminal:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Once the dependencies are installed, you can run the program by running the following command in the terminal:

python3 code/src/processing/downloader.py [-h] [-d DATA_FOLDER]

The program will then download the data from Yahoo Finance and save it to the specified folder. The default folder is data/.

The code/src/main.ipynb notebook explores the optimisation algorithm. The notebook can be run by running the following command in the terminal:

jupyter notebook code/src/main.ipynb

This can also be explored in Docker. To do so, run the following commands in the terminal:

[sudo] docker build --build-arg CACHEBUST=$(date +%s) -t markowitz .
[sudo] docker run -p 8888:8888 markowitz

Development

Installation

To install the dependencies, run the following commands in the terminal:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -r requirements-dev.txt
pre-commit install