Python Portfolio Optimisation: Risk Based Strategies Explained

📊 How do you build a portfolio that truly balances risk and return using Python? In this recorded webinar, Professor Gerhard Kling breaks down risk-based portfolio optimisation with practical, hands-on examples using real financial data. Designed for ML engineers and finance enthusiasts, this session connects finance theory with clear Python implementation. 🎯 In this session, you will learn how to: • Understand the risk-return trade-off • Collect financial data using free APIs • Apply mean-variance optimisation • Build Minimum Risk and Maximum Diversification portfolios • Use tools like CVXPY and PyPortfolioOpt • Backtest and compare different portfolio strategies ⚙️ The webinar also explores the limitations of traditional mean-variance optimisation and introduces risk-based approaches used in modern portfolio construction. 💻 Prerequisite: Basic understanding of Python is helpful. No deep finance background required. If you are interested in quantitative finance, algorithmic investing, or applying machine learning in financial markets, this session provides practical insights you can apply immediately. 👍 If you found this valuable, like the video 💬 Share your questions in the comments 🔔 Subscribe for more content on Python, data science, and finance #python #portfoliomanagement #finance #quantfinance #riskmanagement #machinelearning #backtesting