In 10 lines of Python code, I can do a full portfolio optimization.
This is wild. Let me show how:
1. Load Python libraries
These are the python packages and functions we'll use.
2. Create a Maximum Sharpe Ratio Portfolio
We create a Maximum Sharpe Ratio model and then fit it on the training set.
portfolio_params are parameters passed to the Portfolio returned by the predict method.
3. Create a Benchmark
This is an inverse volatility portfolio that I'll compare my Max Sharpe portfolio against.
4. Out-Of-Sample Performance
Use Predict Method to get out-of-sample Portfolio Performance
5. Portfolio Composition
The visualizations from skfolio are incredible.
6. Cumulative Returns
In 1 line of code, we can get cumulative returns for our portfolio and benchmark.
7. Performance Tear Sheet
In 1 line of code, I get a comprehensive performance tear sheet to share with clients.
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