Structure the dataset for financial machine learning
You are going to have to consider three different factors:

1 - What data are you going to have available when you run your predictions? Are you going to have to pre-process that data? Are you going to have the time to do it? You should be setting your focus on this and work backwards from what runtime predictions look like

2 - When it comes to time series, you have to think of it in terms of (1) lookback windows, or how many periods prior are you considering and (2) time shifts, or how many periods forward are you predicting? This can result in an amazing number of combinations for you to model. You should end up with data sets where you have n features for X time periods and your target variable (your labeled data) is the result Y time periods from X where you are creating a prediction.

3 - The cardinal sin of time series is that you should never model on data that was not available at the specific runtime date. It's a common beginner mistake where you mix up your time periods and end up taking in data that was not actually available when it was released; in other words, it will not be available to you (or at least not correct) when you go to make a prediction. This can be common in financial data where entities can come back and "re-state" numbers at a future date. You have to make sure you are modeling on data as it stood on the target date you are modeling. 

With these thoughts in mind, you should be on your way to prepping your data in a way that makes sense.