Dynamic Robustification of Trading Management Strategies for Unstable Im-mersion Environments

The paper considers the problem of constructing channel management strategies for market chaos conditions. The nature of dynamic chaos violates the probabilistic-statistical paradigm's fundamental principle of experiment repeatability. Under these conditions, the traditional statistical methods of evaluation are not effective, and the generated management decisions 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 considered 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 parameters 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 strat-egies 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 expected, the decrease in performance in the non-matching data segment turned out to be significant.

Will be published in January 2023 (Vol. 19, No. 1). 
Link to work (TBA)
2022 articles Grant CEBA