Asset Allocation and Portfolio Construction 2: Advanced Methods using Python

This course provides practical Python skills for investment professionals, focusing on implementing advanced portfolio construction techniques, tail risk management, factor models, causal analysis, and network-based approaches.

Participants will learn to build working solutions for realistic portfolio optimization, downside risk CVaR frameworks, macroeconomic factor models, machine learning-enhanced forecasts, Black-Litterman views, and graph theory applications. The focus is on production-ready implementation, not theoretical mathematics.

This course is not about writing production-ready Python code, but participants will learn how to read Python code.

• Portfolio managers
• Asset allocators
• Multi-asset strategists
• And quantitative analysts seeking to implement advanced portfolio construction techniques using Python.

• Navigate the Python ecosystem including packages, notebooks, and integration with existing investment management systems
• Translate common Excel operations into equivalent Python implementations
• Apply complex portfolio constraints
• Apply modern regression techniques for variable selection and multicollinearity
• Engineer features for asset return prediction including technical indicators, cross-sectional features, macro features, and network features
• Build portfolios based on network centrality measures and hierarchical clustering (waterfall portfolios)
• Detect structural changes in correlation networks over time for dynamic portfolio adjustment
• Conduct robustness checks through Monte Carlo parameter sampling, bootstrap resampling, and sensitivity analysis

• Excel skills, willingness to acquire Python skills
• Understanding of portfolio theory and asset allocation concepts, ideally MAAPC
• No programming experience required

Python Fundamentals for Investment Professionals

• What is Python and why use it for portfolio construction
• When to use Python versus Excel
• The Python workflow and integration with existing systems
• Python versus Python in Excel
• Industry adoption in asset management
• The Python biotope: packages
• Notebooks for interactive analysis

Python Basics

• Variables and data types
• Lists, dictionaries, and data structures
• Conditional logic and loops
• Functions and modules
• Excel-Python translation reference for the most important concepts

NumPy for Portfolio Calculations

• Array operations for return calculations
• Vectorized computations for portfolio analytics
• Matrix operations for covariance calculations
• Mathematical functions for optimization

Pandas for Time Series Analysis

• DataFrame structure and manipulation
• Time series data handling (resampling, rolling windows)
• Multi-index DataFrames for multi-asset portfolios
• Typical Excel operations with Pandas

Python-Excel Integration

• Reading Excel files into Python
• Writing Python results to Excel
• Formatting Excel output for client reports
• Maintaining Excel compatibility

Data Sources & Market Data Pipelines

• Market data from exchanges
• Economic indicators
• Internal data from CSV files
• Building automated market data pipelines

CVaR Framework for Tail Risk Management

CVaR Framework Foundations

• Understanding Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR)
• Mathematical formulation of CVaR optimization
• Python implementation: setting up goal function and constraints
• Mean-CVaR efficient frontier construction
• Scenario-based optimization using historical returns
• Comparing CVaR portfolios to mean-variance portfolios

Advanced Applications

• Complex constraints: sector limits, tracking error budgets, turnover constraints, factor exposure constraints
• Multi-period CVaR optimization with rebalancing costs
• Copula-based CVaR for modeling tail dependence
• Stress testing and scenario analysis frameworks

Economic Factor Models & Analysis of Factors

Building Macroeconomic Factor Models with Modern Regression Techniques

• Downloading economic data from FRED
• Building single-factor and multi-factor regression models
• Modern linear regression techniques for variable selection: Ridge regression for multicollinearity, Lasso regression and other techniques for automatic feature selection, Elastic Nets
• Introduction to machine learning techniques for regression: Regressograms (histogram-based regression), k-Nearest Neighbors (k-NN) regression, Decision Trees for non-linear relationships, Random Forests for robust feature selection and importance ranking
• Analyzing the stability of factor models

Impulse-Response Analysis & Causality Testing

• Understanding Vector Autoregression (VAR) models
• Estimating VAR models in Python
• Interpreting shock transmission across asset classes
• Causality testing between economic variables and asset returns
• Visualizing causal relationships and transmission mechanisms

Regime Detection & PCA Integration

Regime Identification

• Hidden Markov Models (HMM) for regime detection
• Markov regime-switching models
• Identifying bull/bear/sideways regimes
• Volatility regime detection
• Economic cycle identification using macroeconomic indicators
• Visual regime analysis and dating
• Non-traditional approaches to regime identification

ML-Enhanced Forecasting & Black Litterman Implementation

Review of Black-Litterman

• Active investment management with the Black-Litterman framework
• Choosing and calculating the prior
• Specifying views
• Combining prior and views to generate posterior return forecasts
• Calculating Black-Litterman portfolios with full constraints

Using Machine Learning to Derive Quantitative Signals

• Feature engineering for asset return prediction: technical, macro, network features
• Random Forest, Gradient Boosting (XGBoost, LightGBM), Ridge/Lasso regression
• Model evaluation metrics: RMSE, directional accuracy, information coefficient
• Feature importance analysis and ensemble methods

Turning Signals into Views and Integration

• Combining multiple view sources into coherent view portfolios
• View confidence matrix calibration: Mapping signal strength and ML prediction confidence to omega parameters

Sensitivity Analysis & Multi-Period Application

• Sensitivity analysis: impact of tau parameter on prior vs. view weighting
• Omega parameter effects: view confidence and portfolio concentration
• Prior dominance analysis: when equilibrium overwhelms views
• Multi-period application with dynamic view updating
• Rolling Black-Litterman portfolios with time-varying ML-enhanced views

Graph Theory & Network-Based Portfolio Construction

Correlation Networks & Minimum Spanning Trees

• Building correlation matrices from asset returns
• Converting correlations to distance metrics
• Constructing Minimum Spanning Trees (MST) using NetworkX
• Visualizing asset relationship networks
• Identifying clusters and central assets
• Network topology analysis (degree centrality, betweenness)
• Detecting changes in network structure over time

Network-Based Portfolio Construction

• Portfolio construction using network centrality measures
• Diversification based on network topology
• Identifying systemic risk through network analysis
• Dynamic portfolio adjustment based on changing networks
• Applying hierarchical clustering for asset grouping: waterfall portfolios

Backtesting, Parameter Calibration & Robustness Testing

Common Pitfalls in Backtesting

• Look-ahead bias: using future information in historical tests
• Survivorship bias: excluding delisted or failed assets
• Data snooping and p-hacking: over-optimization on historical data
• Transaction costs and liquidity constraints ignored
• Unrealistic rebalancing assumptions
• Regime changes and non-stationarity
• Cherry-picking favorable time periods
• Ignoring implementation constraints

Backtesting Best Practices

• Walk-forward analysis with out-of-sample testing
• Separating in-sample (training) and out-of-sample (testing) periods
• Rolling window vs. expanding window approaches
• Realistic transaction cost modeling (bid-ask spreads, market impact)
• Proper handling of data availability and point-in-time data
• Multiple performance metrics: Sharpe ratio, max drawdown, Calmar ratio, Sortino ratio, Benchmark-relative performance analysis
• Stress testing and Monte Carlo simulation
• Monte Carlo simulation for confidence intervals

Parameter Calibration

• Window size selection for rolling estimates: short-term responsiveness vs. long-term stability trade-offs
• Lookback periods for covariance matrix estimation (e.g., 36 months, 60 months, 120 months
• Tau parameter in exponentially-weighted (EW) statistics: decay factor calibration, half-life considerations, optimal tau for different asset classes
• Black-Litterman tau and omega parameter tuning
• Rebalancing frequency optimization: daily, weekly, monthly, quarterly trade-offs
• CVaR confidence level selection (90%, 95%, 99%)
• Black-Litterman tau and omega parameter tuning
• Cross-validation for optimal parameter selection

Robustness Checks

• Random parameter perturbations: systematically varying key parameters
• Monte Carlo parameter sampling: drawing parameters from reasonable distributions
• Resulting confidence ranges for portfolio weights and performance metrics
• Sensitivity analysis: identifying which parameters have largest impact on results
• Bootstrap resampling methods of historical data for distribution of outcomes

From Ad Hoc Calculations to Production Deployment

• Notebooks versus Python scripts
• Scheduling automated portfolio updates
• Error handling and logging
• Email notifications for regime changes
• Version control with Git/GitHub
• Documentation and handoff procedures

Closing Remarks & Q&A