Discussion of article "Metamodels in machine learning and trading: Original timing of trading orders"
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New article Metamodels in machine learning and trading: Original timing of trading orders has been published:
Metamodels in machine learning: Auto creation of trading systems with little or no human intervention — The model decides when and how to trade on its own.
First, I need to make a small remark. Since the researcher deals with uncertainty while developing trading systems (including the ones applying machine learning), it is impossible to strictly formalize the object of search. It can be defined as some more or less stable dependencies in a multidimensional space that are difficult to interpret in human and even mathematical languages. It is difficult to conduct a detailed analysis of what we get from highly parameterized self-training systems. Such algorithms require a certain degree of trust from a trader based on the results of backtests, but they do not clarify the very essence and even the nature of the pattern found.
I want to write an algorithm that will be able to analyze and correct its own errors iteratively improving its results. To do this, I propose to take a bunch of two classifiers and train them sequentially as suggested in the following diagram. The detailed description of the idea is provided below.
Each of the classifiers is trained on its own dataset, which has its own size. The blue horizontal line represents the conditional history depth for the metamodel, and the orange ones stand for the base model. In other words, the depth of history for a metamodel is always greater than for the base one and is equal to the estimated (test) time interval, on which the combination of these models will be tested.
Author: Maxim Dmitrievsky