Python Backtest Mastery for Risk Parity Portfolios

Optimize Investment Strategies with Python: Comprehensive Risk Parity Backtesting Techniques & Insights

Created by: Paul Carter
Engineer & Creator

What Will I Learn?

• Implement Modern Risk Parity analysis to select a portfolio of stocks and weights.
• Write a reusable backtesting class that iteratively implements your parameters
• Analytical outputs such as Sharpe Ratio, CAGR, Drawdown, Benchmark Charts , etc.
• Select optimal stock universes from S&P500 data
• Integrate SQL databases for streamlined data retrieval
• Generate key financial metrics for performance review
• Craft a Python backtesting class for strategy analysis
• Utilize Python for dynamic asset allocation
• Backtest how your strategy would have done through time

Requirements

• Some exposure to python is preferred, although code will be given if student prefer to only execute the code

Target audience

• Anyone curious about testing Modern Portfolio theories to understand if they are worth implementing