Understandable and explainable machine learning model I want to find formula for best financial portfolio. 

Inputs: Historical fundamental data for last 15 years. For 3000 companies for every quatal we have things like market cap, cash flow, liabilities, assets, revenue etc, every one of these inputs is a time series with data points for every quartal (i.e. 4 times in a year).

Goal: I want to find a formula that given the 3000 companies would allow me to rank (sort) it from best to worst. And then I can pick let's say top 10 of those as the best portfolio.

Example, how human can solve this task: study those historical financial data for couple of years, and came up with something. Like this very simple formula rank = revenue[last]/total-assets[last] + revenue[last]/market-cap[last] (looking for highly profitable and not too much overpriced companies).

Machine Learning way to solve it: feed those data points to Neural Net, run it and hope it will learn something after a while. 

The problem is that we can't analyse the the neural network result because it's a black box. And I want to see the final formula in more or less compact form and be able to understand what it does. 

What other technics other than Neural Nets can be used? The model has to be sophisticated enough to be able to work with the time series as inputs.
Neural networks are convenient for automatically building features from the data, but as far as I know this is not compatible with interpretability since we don't know what the features represent. So the only way I'm aware of to obtain predictions that can be explained and analyzed is to use traditional models, and this would certainly require a bit of feature engineering in this case. I would recommend decision trees which are completely transparent in their decision process and can be understood even by non-experts. With this kind of model the idea would be to provide the features which represent the evolution across time, e.g. min/max/average over the last N years, difference current-N, etc., possibly for several values of N.