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In the previous article, we have thoroughly examined training via meta learner and cross-validation, as well as saving models in the ONNX format. I have also noted that machine learning models are not capable of finding patterns out of the box in disparate and contradictory data. In this case, it is very important what exactly is sent to the input and output of a neural network or any other machine learning algorithm.

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This article describes an attempt to understand some causal inference techniques in relation to algorithmic trading.

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