Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets
This work addresses the challenge of automated state representation for learning agents in financial markets, though it appears incremental as it builds on existing clustering and correlation methods.
The paper tackles the problem of online state discovery in high-frequency financial markets by using real-time cluster configurations of streaming asynchronous features as state descriptors, enabling unsupervised enumeration of the state space and learning of action-selection policies without human-driven preprocessing.
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.