---
title: >-
  Max Diversification and Min CVaR Optimisation Models for a Portfolio with 5
  Assets
emoji: 📊
colorFrom: indigo
colorTo: indigo
sdk: docker
pinned: false
short_description: Dashboard for portfolio optimisation results
---

# Portfolio Optimisation Strategies
## Overview
Showcased machine learning in portfolio construction by training/testing multiple optimisation strategies (Max Diversification, Min CVaR, Equal Weighted) on a portfolio of ETFs, with results deployed in a Python Vizro dashboard.
Deployed on HuggingFace Spaces.

## Python Tools Used

### Data Fetching, Preparation and Plotting
* **yfinance** to obtain historical ETF prices
* **pandas** for data processing and preparation
* **plotly** for plotting the data

### Portfolio Optimisation
* **scikit-learn** for training and testing
* **skfolio** to access the Maximum Diversification and Minimum CVaR models

### Results Dashboard
* **vizro** to build the dashboard

## Description
### Portfolio Composition
Five ETFs:
- **EWA** iShares MSCI Australia ETF
- **EWH** iShares MSCI Hong Kong ETF
- **EWJ** iShares MSCI Japan ETF
- **ENZL** iShares MSCI New Zealand ETF
- **EWS** iShares MSCI Singapore ETF

### Time Period
30/09/2010 to 30/09/2024

### Optimisation Models Tested:
- Maximum Diversification
- Minimum CVaR
- Equal Weighted

### Metrics
- Annualised Mean
- Annualised Standard Deviation
- CVaR at 95%

## Results
- The number of stocks (ETFs) in the portfolio is very small, at only 5, so there was not much variability in the results.
- The Equal Weighted model had the highest annualised return and annualised standard deviation. 

## Next Steps
Test the optimisation models on a portfolio with a greater number of stocks, over different time periods, including other optimisation models in the analysis.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference