e.g. Machine learning architecture, choose objective function (more art than science, need to under the problem to design suitable ObjF), TrainData (data-driven aspect, important for the performance of the algorithms afterwards, motivation for data generator) .
Architecture, ObjF combined as Network;
Quality of training data shapes the DNN (and its performance)!
What kind of training data we should use, what is the training data that will prepare our algorithms ideally for the scenarios that they will be facing in real?
Test data is real life data that algorithm will face in the market.
What are the challenges that we use the historical data as test data?
Historical data is one realisation of the past, just represents one out of many many realisations.
Unless make some extreme assumptions: e.g., market is stationary which is not the case most of times; We observe the evolution in the past does not mean that it works like this in the future.