  Financial markets are a complex dynamical system. The complexity comes from
the interaction between a market and its participants, in other words, the
integrated outcome of activities of the entire participants determines the
markets trend, while the markets trend affects activities of participants.
These interwoven interactions make financial markets keep evolving. Inspired by
stochastic recurrent models that successfully capture variability observed in
natural sequential data such as speech and video, we propose CLVSA, a hybrid
model that consists of stochastic recurrent networks, the sequence-to-sequence
architecture, the self- and inter-attention mechanism, and convolutional LSTM
units to capture variationally underlying features in raw financial trading
data. Our model outperforms basic models, such as convolutional neural network,
vanilla LSTM network, and sequence-to-sequence model with attention, based on
backtesting results of six futures from January 2010 to December 2017. Our
experimental results show that, by introducing an approximate posterior, CLVSA
takes advantage of an extra regularizer based on the Kullback-Leibler
divergence to prevent itself from overfitting traps.
