8
The ML4T Workflow â From Model to Strategy Backtesting
Now, it's time to integrate the various building blocks of the machine learning for trading (ML4T) workflow that we have so far discussed separately. The goal of this chapter is to present an end-to-end perspective of the process of designing, simulating, and evaluating a trading strategy driven by an ML algorithm. To this end, we will demonstrate in more detail how to backtest an ML-driven strategy in a historical market context using the Python libraries backtrader and Zipline.
The ultimate objective of the ML4T workflow is to gather evidence from historical data. This helps us decide whether to deploy a candidate strategy in a live market and put financial resources at risk. This ...
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