What are the best tested ways to preprocess data with very different frequencies such as fundamental and market data into same ML model for quant trading?
There are many ways to go about this, for example:
You can build one global model using combined dataset (less frequent data will essentially carry the precious observed value until the next observation). Problem that you would face with this approach is the one of the data sources may be less predictive so you might not get a balanced model.
Build two separate models, and then combine them, either as a weighted average, or as a grid. Subjectivity and/or incorporation of less predictive attributes would be the major drawbacks.
Use the fundamental data to segment or short list the securities and then make trading based on the more frequent market data. Drawback would be the loss of interaction effects for example.
Can think of many others. The best approach would be pretty much driven by the organisation setup and the strategies so an approach that might work for one is not necessarily going to work for everyone, some one got to lose!