Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets

Understand

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.