19 research outputs found
A posteriori multi-stage optimal trading under transaction costs and a diversification constraint
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.Comment: 25 pages, 4 figures, 6 table
A posteriori Trading-inspired Model-free Time Series Segmentation
Within the context of multivariate time series segmentation this paper
proposes a method inspired by a posteriori optimal trading. After a
normalization step time series are treated channel-wise as surrogate stock
prices that can be traded optimally a posteriori in a virtual portfolio holding
either stock or cash. Linear transaction costs are interpreted as
hyperparameters for noise filtering. Resulting trading signals as well as
resulting trading signals obtained on the reversed time series are used for
unsupervised labeling, before a consensus over channels is reached that
determines segmentation time instants. The method is model-free such that no
model prescriptions for segments are made. Benefits of proposed approach
include simplicity, computational efficiency and adaptability to a wide range
of different shapes of time series. Performance is demonstrated on synthetic
and real-world data, including a large-scale dataset comprising a multivariate
time series of dimension 1000 and length 2709. Proposed method is compared to a
popular model-based bottom-up approach fitting piecewise affine models and to a
recent model-based top-down approach fitting Gaussian models, and found to be
consistently faster while producing more intuitive results.Comment: 9 pages, double column, 13 figures, 2 table