8:30 Cycle-in to work, check that overnight backtests have completed, inspect results. 9:00 Write up conclusions in short report, circulate 9:30 Check out all the generated signals from the previous day to validate that the models have performed as intended, read up on related news 10:30 Implement new scoring model for our backtest dataset, numerical programing in python 12:30 Lunch 13:00 Append new data to our train/test dataset, data-mangling in python 16:00 Discuss model integration with C# dev team. Bug-fix mismatch between back-test performance in python analytics vs C# implementation. 17:30 Brainstorm ideas with the analysts about modeling. 18:00 Set to run new model on the appended train/test set, cycle home.