Dynamic Asset Allocation and Diversification Techniques

This project explores various methods for managing and optimizing asset portfolios using Python. The code and examples implement dynamic asset allocation, portfolio diversification strategies, and exit orders for risk management.

Structure

Python Files

• data_and_descriptives.py
  This script is responsible for:

  • Generating descriptive statistics for the selected assets.
  • Downloading and preprocessing asset data.
  • Visualizing asset performance and characteristics.

• dynamic_asset_allocation.py
  Contains implementations for dynamic asset allocation strategies, including:

  • CPPI (Constant Proportion Portfolio Insurance)
  • MDD (Maximum Drawdown)
  • RDD (Relative Drawdown)
  • EDD (Expected Drawdown)

• diversification.py
  Focuses on portfolio diversification through:

  • Equal-Weighted and Markowitz with weight bounds are supported. Other strategies, such as Hierarchical Risk Parity (HRP), can be incorporated with minimal modifications. HRP is included as an option in case you have your own implementation to import.
  • Incorporating stop-loss and take-profit mechanisms to manage risk and returns.

Jupyter Notebooks

• compare_data.ipynb
  Shows a basic example of the data_and_descriptives.py file functions in order to compare different assets.
• daa_diversification.ipynb Shows one of the backtesting strategies based on DAA and diversification strategies.
• create_report.ipynb Creates a report that outputs returns perfomance and risk metrics, along characteristics of the selected ETFs based on the etf_ib_data.xlsx data.

Data Files

• etf_ib_data.xlsx
  Contains descriptive data about ETFs used in the analysis.