There have been thousands of papers and many books written on the subject of using machine learning for time series forecasting and an unknown amount of money traded/invested with it. Most of them cast trading as a time series forecasting problem where performance is measured by mean square error. They usually then assume that would lead to profitable trading.

You don't get to define what "modern machine learning" is any more than I do.

The application of complex models to higher-dimensional domains have problems of their own. Both the successes and failures are worth writing and thinking about.

The post is an important reminder about simplicity and complexity in modeling. That's why I submitted it.