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Multi-dimensional trading problem in multi-participant settings
Zeman, Jan
The dimensionality of optimization problem arising within multi-market trading task grows exponentially with a growing number of markets. To prevent the dimensionality problem, multi-market trading is represented as a multi-participant decision making problem with finite common capital. Each local DM task is a single-market trading enriched by an ability to share a part of local capital with other local DM tasks (participants). The paper provides formulation of the problem and basic algorithmic steps. The approach is illustrated on the real market data.
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Výběr délky regresoru
Křivánek, O. ; Zeman, Jan
This research report is closely connected to the long time running research of the usage of the theory of Bayesian learning, stochastic dynamic programming and its approximations in futures dealing problem. This report describes tuning of one selectable parameter, which occurs in the new-designed algortihm called iterations-spread-in-time strategy. Experiment is done on real economic data on 35 selected futures markets. The main criterion of succes is the so-called net profit and also comparison with the previous experiments.
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Testování zapomínacího faktoru
Votava, A. ; Zeman, Jan
Presented work deals with forgetting estimation in the frame of dynamic decision making. The main goal is to find the optimal forgetting system for the algorithm for estimation of forgetting factor in time in the optimal way. Further goal is to compare the algorithm with the constant forgetting for various settings.
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Nový přístup k odhadování Bellmanových funkcí
Zeman, Jan
The paper concerns an approximate dynamic programming. It deals with a class of tasks, where the optimal strategy on a shorter horizon is close to the global optimal strategy. This property leads to a new, specific, design of the Bellman function estimation. The paper introduces the proposed approach and provides an illustrative example performed on the futures trading data.
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Vylešpení modelu dynamického rozhodování pomocí metody "Iteration spread in time"
Divišová, L. ; Zeman, Jan
In the present work we study the problem of ¯nding the best de- cision based on our previous experience with the system. To solve this task, we use the dynamic programming and its approximations. In the work we summarize the theory needed for usage of the dynamic programming and we deal with its application on futures dealing trying to ¯nd best strategy, id est a sequence of decisions, maximizing our gain or minimizing the loss function. We introduce notion "Bellman function", explain why the approximation of this function is needed, demonstrate one of already tested approximation methods together with its results and we try to propose a method that would lead to the best approximation in suitable time and with available computation aids.
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Návrh strategie pro obchodování s futures kontrakty
Zeman, Jan
The paper considers by design of trading strategy for futures markets. The problem is defined as dynamic decision making task and it is solved using iteration spread in time and Monte Carlo method. The paper includes results on experiments with real data.
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