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.

提出了CLVSA模型，它是一个包含了随机循环网络、序列到序列的体系结构、自注意机制和卷积LSTM单元的混合模型，能够更好地捕捉金融交易数据的特征和趋势，同时利用Kullback-Leibler差距作为正则化，避免模型过拟合问题。通过六种期货的回测结果表明，该模型胜过基础模型，如卷积神经网络、vanilla LSTM网络和带有注意力机制的序列到序列模型。

CLVSA: 一种基于卷积LSTM和注意力机制的变分序列到序列模型，用于预测金融市场趋势