This paper presents a method for the evaluation of a posteriori (historical) multi-variate multi-stage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, we analyze the stage-wise optimal allocation over a time horizon with potential investments in multiple currencies and various assets , such as, for example, assets emulating stock indices. Variants are discussed, such as unconstrained trading frequency, a fixed number of total admissable trades, and the waiting of a specific time-period after every executed trade until the next trade. Mathematical aspects are the modeling of transition dynamics as a Markov Decision Process (MDP), efficient graph generation and consequent graph search . The developed strategies are evaluated on recent real-world data dating back one year. Special focus is on the evaluation of quantitative results .
This paper presents a simple method for a posteriori (historical) multi-variate multi-stage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, we analyze the stage-wise optimal allocation over a time horizon with potential investments in multiple currencies and various assets . Three variants are discussed, including unconstrained trading frequency, a fixed number of total admissable trades, and the waiting of a specific time-period after every executed trade until the next trade. The developed methods are based on efficient graph generation and consequent graph search , and are evaluated quantitatively on real-world data . The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning .
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Mathematical aspects are the modeling of transition dynamics as a Markov Decision Process (MDP),
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dating back one year. Special focus is on the evaluation of quantitative results
. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning