In Statistical Arbitrage (StatArb), classical mean reversion trading
strategies typically hinge on asset-pricing or PCA based models to identify the
mean of a synthetic asset. Once such a (linear) model is identified, a separate
mean reversion strategy is then devised to generate a trading signal. With a
view of generalising such an approach and turning it truly data-driven, we
study the utility of Autoencoder architectures in StatArb. As a first approach,
we employ a standard Autoencoder trained on US stock returns to derive trading
strategies based on the Ornstein-Uhlenbeck (OU) process. To further enhance
this model, we take a policy-learning approach and embed the Autoencoder
network into a neural network representation of a space of portfolio trading
policies. This integration outputs portfolio allocations directly and is
end-to-end trainable by backpropagation of the risk-adjusted returns of the
neural policy. Our findings demonstrate that this innovative end-to-end policy
learning approach not only simplifies the strategy development process, but
also yields superior gross returns over its competitors illustrating the
potential of end-to-end training over classical two-stage approaches.

通过使用自动编码器架构和策略学习方法，研究证明了这种创新的端到端策略学习方法不仅简化了策略开发流程，而且在收益上优于传统的两阶段方法，展示了端到端训练的潜力。

统计套利自编码架构的端到端策略学习

End-to-End Policy Learning of a Statistical Arbitrage Autoencoder  Architecture

The estimation of an f-divergence between two probability distributions based
on samples is a fundamental problem in statistics and machine learning. Most
works study this problem under very weak assumptions, in which case it is
provably hard. We consider the case of stronger structural assumptions that are
commonly satisfied in modern machine learning, including representation
learning and generative modelling with autoencoder architectures. Under these
assumptions we propose and study an estimator that can be easily implemented,
works well in high dimensions, and enjoys faster rates of convergence. We
verify the behavior of our estimator empirically in both synthetic and
real-data experiments, and discuss its direct implications for total
correlation, entropy, and mutual information estimation.

本文研究了在结构假设条件下用样本估算概率分布之间 f-divergence 的问题，提出了一种易于实现、适用于高维数据且收敛速度更快的估算器，并在合成和真实数据实验中验证了其行为。