Research

Lumos Alpha’s Algorithmic Trading Research Team is dedicated to advancing state-of-the-art machine learning and AI algorithms and applying these algorithms to equity trading, portfolio construction, and risk management. Our researchers also participate in algorithm competitions at leading international conferences and on Kaggle, consistently earning top-tier placements.

Beyond finance, we investigate signal processing techniques across the biomedical engineering, communications, speech processing, and computer vision domains. We believe algorithms from these areas—especially for weak-signal extraction, denoising, and data mining—will largely benefit algorithmic trading and risk control.

Selected Publications by the Algorithmic Research Team

[2026] A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective,

O. Zhang, Z. Zhang

IEEE Conference on Artificial Intelligence, Spain, May 8 -10, 2026

[2015] Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction,

Z. Zhang

IEEE Transactions on Biomedical Engineering 62 (8), 1902 - 1910

[2015] TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise,

Z. Zhang, Z. Pi, B. Liu

IEEE Transactions on Biomedical Engineering 62 (2), 522-531

[2014] Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals,

Z. Zhang, T.P. Jung, S. Makeig, Z. Pi, B.D. Rao

IEEE Transactions on Neural Systems and Rehabilitation Engineering 22 (6), 1186 - 1197

[2014] Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning,

J. Wan, Z. Zhang, B.D. Rao, S. Fang, J. Yan, A. Saykin, L. Shen

IEEE Transactions on Medical Imaging 33 (7), 1475-1487

[2013] Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation,

Z. Zhang, B.D. Rao

IEEE Transactions on Signal Processing 61 (8), 2009-2015

[2013] Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning,

Z. Zhang, T.P. Jung, S. Makeig, B.D. Rao

IEEE Transactions on Biomedical Engineering 60 (2), 300-309

[2013] Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware,

Z. Zhang, T.P. Jung, S. Makeig, B.D. Rao

IEEE Transactions on Biomedical Engineering 60 (1), 221-224

[2012] Sparse Bayesian Multi-Task Learning for Predicting Cognitive Outcomes from Neuroimaging Measures in Alzheimer's Disease,

J. Wan, Z. Zhang, J. Yan, T. Li, B.D. Rao, S. Fang, S. Kim, S. Risacher, A. Saykin, L. Shen

CVPR 2012

[2012] Evolving Signal Processing for Brain-Computer Interfaces,

S. Makeig, C. Kothe, T. Mullen, N. Bigdely-Shamlo, Z. Zhang, K. Kreutz-Delgado

Proceedings of the IEEE 100 (Special Centennial Issue), 1567-1584

[2012] Recovery of Block Sparse Signals Using the Framework of Block Sparse Bayesian Learning,

Z. Zhang, B.D. Rao

ICASSP 2012

[2011] Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning,

Z. Zhang, B.D. Rao

IEEE Journal of Selected Topics in Signal Processing 5 (5), 912 - 926

[2010] Sparse signal recovery in the presence of correlated multiple measurement vectors,

Z. Zhang, B.D. Rao

ICASSP 2010

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