英文标题：
《Using real-time cluster configurations of streaming asynchronous
  features as online state descriptors in financial markets》
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作者：
Dieter Hendricks
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最新提交年份：
2017
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英文摘要：
  We present a scheme for online, unsupervised state discovery and detection from streaming, multi-featured, asynchronous data in high-frequency financial markets. Online feature correlations are computed using an unbiased, lossless Fourier estimator. A high-speed maximum likelihood clustering algorithm is then used to find the feature cluster configuration which best explains the structure in the correlation matrix. We conjecture that this feature configuration is a candidate descriptor for the temporal state of the system. Using a simple cluster configuration similarity metric, we are able to enumerate the state space based on prevailing feature configurations. The proposed state representation removes the need for human-driven data pre-processing for state attribute specification, allowing a learning agent to find structure in streaming data, discern changes in the system, enumerate its perceived state space and learn suitable action-selection policies.
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中文摘要：
我们提出了一种在高频金融市场中在线、无监督地从流式、多功能、异步数据中发现和检测状态的方案。在线特征相关性使用无偏无损傅里叶估计器计算。然后使用高速最大似然聚类算法找到最能解释相关矩阵结构的特征聚类配置。我们推测，该特征配置是系统时间状态的候选描述符。通过使用一个简单的集群配置相似性度量，我们能够根据主要的特征配置枚举状态空间。所提出的状态表示消除了对状态属性规范的人为数据预处理的需要，允许学习代理在流数据中找到结构，识别系统中的变化，枚举其感知的状态空间，并学习合适的动作选择策略。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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