Hey Everybody,

We all know the Quantopian platform is incredibly powerful, but for those of us who have tried to coax it into backtesting machine learning algorithms on anything other than OHLCV data, we also know that the platform isn't as flexible with fundamental data, since Morningstar Fundamentals and other datasets can't be accessed through the data.history object. When working with fundamental factors, it's straightforward to use the research environment to tune a static set of parameters for a machine learning algorithm which you then implement in the Backtesting IDE, but it's not immediately clear how to implement a machine learning algorithm in the IDE that retrains and updates its parameters over time.

A few months back, I spent a good deal of time digging through the documentation, trawling the forum, hacking away at this, and I ultimately found a way to make it work. Despite likely giving a way a bit of a trading edge, I wanted to share the skeleton of my algo from back then to save a lot of you the headaches of trying to stand something like this up.

A lot of this will repeat from Thomas Wiecki's awesome post on this material, but, at just over 100 lines of code, I hope this notebook will be a little more digestible for folks than the very robust algorithm in that thread.

Note that the performance metrics in the below algorithm aren't too relevant-- if you pick up this algorithm for your own use, you'll likely have a more intentional and sensible portfolio allocation strategy. Plus, the choice of simple OLS for the machine learning algorithm, as well as the choice of these particular factors, are just placeholders-- you'll likely select both fundamental inputs and a machine learning algorithm which make sense.

Good luck!

EDIT: Bug fix-- thanks to Kelvin Ho for the catch.