DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
In electronic trading markets, limit order books (LOBs) provide information
about pending buy/sell orders at various price levels for a given security.
Recently, there has been a growing interest in using LOB data for resolving
downstream machine learning tasks (e.g., forecasting). However, dealing with
out-of-distribution (OOD) LOB data is challenging since distributional shifts
are unlabeled in current publicly available LOB datasets. Therefore, it is
critical to build a synthetic LOB dataset with labeled OOD samples serving as a
testbed for developing models that generalize well to unseen scenarios. In this
work, we utilize a multi-agent market simulator to build a synthetic LOB
dataset, named DSLOB, with and without market stress scenarios, which allows
for the design of controlled distributional shift benchmarking. Using the
proposed synthetic dataset, we provide a holistic analysis on the forecasting
performance of three different state-of-the-art forecasting methods. Our
results reflect the need for increased researcher efforts to develop algorithms
with robustness to distributional shifts in high-frequency time series data.