It’s hard to find a field where machine learning didn’t make its impact already. But despite all the buzzwords and marketing, machine learning in finance is still an open case. The main reason is that machine learning practitioners treat financial data as it is regular routinely practice in computer vision or NLP, not taking into account stochastic underlying nature of the data and related decisions. This deep dive presentation shows on real examples the top-mistakes that practitioners do with financial time series, how these mistakes can be fixed and how drastically change the related results.