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Dynamic Robustification of Trading Management Strategies for Unstable Immersion Environments
The paper considers the problem of constructing channel management
strategies for market chaos conditions. The nature of dynamic chaos vio-
lates the probabilistic-statistical paradigm's fundamental principle of exper-
iment repeatability. Under these conditions, the traditional statistical meth-
ods of evaluation are not effective, and the generated management deci-
sions are unstable. There is a need to create management strategies that
produce effective decisions for a wide variety of dynamic characteristics of
observation series generated by market chaos. In this article, we have con-
sidered two variants of such robustification using channel management
strategies as an ex-ample. The first approach is based on the assumption
that the optimal solution for the observation interval with the least favorable
dynamics for this management strategy will produce solutions that are
satisfactory at other observation sites as well. However, our numerical study
does not confirm this assumption. Explanation is that optimization of pa-
rameters for highly dynamic segments with abrupt changes in the observed
process produces degenerate decisions. The optimal control parameters
corresponding to them are suitable only for a very narrow range of possible
variations of the observed process. The second approach to the dynamic
robustification of management strategies is based on searching for optimal
parameters of the strategy on large observation intervals. It is assumed that
at such observation intervals, chaos will demonstrate the most variants of
local dynamics, and the found parameters will be adapted simultaneously to
the most diverse variations in dynamic characteristics of observation series.
In general, this approach gives an encouraging result, however, as ex-
pected, the decrease in performance in the non-matching data segment
turned out to be significant.