Extracting High Frequency Alpha Signals through Deep Visual Representation Learning on Order Flow Imbalance and Multi Level Liquidity Patterns
Abstract
The increasing granularity of financial market data has shifted the frontier of high-frequency trading from traditional linear econometric models toward complex, non-linear deep learning architectures. This paper explores the extraction of high-frequency alpha signals through a novel system-level integration of deep visual representation learning, specifically targeting the latent dynamics within order flow imbalance and multi-level liquidity patterns. By transforming the discrete, high-dimensional state of the limit order book into continuous spatial-temporal representations, we provide a robust framework for identifying transient inefficiencies that escape conventional scalar-based analysis. Our research emphasizes the structural trade-offs between model interpretability and predictive latency, proposing an infrastructure that leverages convolutional and attention-based mechanisms to process multi-level depth data as visual hierarchies. Furthermore, we address the socio-technical implications of such systems, including market robustness, the ethics of information asymmetry, and the regulatory challenges posed by autonomous liquidity provision. Through a comprehensive discussion of system deployment and sustainability, we demonstrate how visual representation learning mitigates the signal-to-noise ratio issues inherent in tick-by-tick data. The study concludes that the future of high-frequency alpha extraction lies in the convergence of computer vision methodologies with market microstructure theory, necessitating a policy-driven approach to ensure systemic fairness in an increasingly automated global financial infrastructure.
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