  High-frequency quantitative investment is a crucial aspect of stock
investment. Notably, order flow data plays a critical role as it provides the
most detailed level of information among high-frequency trading data, including
comprehensive data from the order book and transaction records at the tick
level. The order flow data is extremely valuable for market analysis as it
equips traders with essential insights for making informed decisions. However,
extracting and effectively utilizing order flow data present challenges due to
the large volume of data involved and the limitations of traditional factor
mining techniques, which are primarily designed for coarser-level stock data.
To address these challenges, we propose a novel framework that aims to
effectively extract essential factors from order flow data for diverse
downstream tasks across different granularities and scenarios. Our method
consists of a Context Encoder and an Factor Extractor. The Context Encoder
learns an embedding for the current order flow data segment's context by
considering both the expected and actual market state. In addition, the Factor
Extractor uses unsupervised learning methods to select such important signals
that are most distinct from the majority within the given context. The
extracted factors are then utilized for downstream tasks. In empirical studies,
our proposed framework efficiently handles an entire year of stock order flow
data across diverse scenarios, offering a broader range of applications
compared to existing tick-level approaches that are limited to only a few days
of stock data. We demonstrate that our method extracts superior factors from
order flow data, enabling significant improvement for stock trend prediction
and order execution tasks at the second and minute level.
