Summary
In this chapter, we took a much closer look at how backtesting works, what challenges there are, and how to manage them. We demonstrated how to use the two popular backtesting libraries, backtrader and Zipline.
Most importantly, however, we walked through the end-to-end process of designing and testing an ML model, showed you how to implement trading logic that acts on the signals provided by the model's predictions, and saw how to conduct and evaluate backtests. Now, we are ready to continue exploring a much broader and more sophisticated array of ML models than the linear regressions we started with.
The next chapter will cover how to incorporate the time dimension into our models.
