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 realm of High-Frequency Trading (HFT) is characterized by rapid
decision-making processes that capitalize on fleeting market inefficiencies. As
the financial markets become increasingly competitive, there is a pressing need
for innovative strategies that can adapt and evolve with changing market
dynamics. Enter Reinforcement Learning (RL), a branch of machine learning where
agents learn by interacting with their environment, making it an intriguing
candidate for HFT applications. This paper dives deep into the integration of
RL in statistical arbitrage strategies tailored for HFT scenarios. By
leveraging the adaptive learning capabilities of RL, we explore its potential
to unearth patterns and devise trading strategies that traditional methods
might overlook. We delve into the intricate exploration-exploitation trade-offs
inherent in RL and how they manifest in the volatile world of HFT. Furthermore,
we confront the challenges of applying RL in non-stationary environments,
typical of financial markets, and investigate methodologies to mitigate
associated risks. Through extensive simulations and backtests, our research
reveals that RL not only enhances the adaptability of trading strategies but
also shows promise in improving profitability metrics and risk-adjusted
returns. This paper, therefore, positions RL as a pivotal tool for the next
generation of HFT-based statistical arbitrage, offering insights for both
researchers and practitioners in the field.

该研究论文探讨了将强化学习应用于统计套利策略中的高频交易场景，通过利用强化学习的自适应学习能力，发现其可以揭示传统方法可能忽略的模式并设计交易策略，同时解决在金融市场中这一非稳态环境中应用强化学习所面临的挑战，并研究缓解相关风险的方法。通过广泛的模拟和回测，研究结果表明，强化学习不仅提升了交易策略的适应性，而且显示了改善盈利指标和风险调整回报的潜力，从而将其定位为下一代基于高频交易的统计套利的关键工具，为该领域的研究人员和从业者提供了洞察。

应用深度 Q 学习于高频交易中的统计套利增强策略之全面探索

Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in  High-Frequency Trading: A Comprehensive Exploration

Alpha signals for statistical arbitrage strategies are often driven by latent
factors. This paper analyses how to optimally trade with latent factors that
cause prices to jump and diffuse. Moreover, we account for the effect of the
trader's actions on quoted prices and the prices they receive from trading.
Under fairly general assumptions, we demonstrate how the trader can learn the
posterior distribution over the latent states, and explicitly solve the latent
optimal trading problem. We provide a verification theorem, and a methodology
for calibrating the model by deriving a variation of the
expectation-maximization algorithm. To illustrate the efficacy of the optimal
strategy, we demonstrate its performance through simulations and compare it to
strategies which ignore learning in the latent factors. We also provide
calibration results for a particular model using Intel Corporation stock as an
example.

本文分析了如何通过学习潜在因素的后验分布来解决隐含最优交易问题，提出了一种 EM 算法的变体来校准模型，演示了此最优策略的性能和与忽略学习的策略的比较。