What Teaches Robots to Walk, Teaches Them to Trade too -- Regime Adaptive Execution using Informed Data and LLMs

arXiv:2406.15508v1 Announce Type: cross
Abstract: Machine learning techniques applied to the problem of financial market forecasting struggle with dynamic regime switching, or underlying correlation and covariance shifts in true (hidden) market variables. Drawing inspiration from the success of reinforcement learning in robotics, particularly in agile locomotion adaptation of quadruped robots to unseen terrains, we introduce an innovative approach that leverages world knowledge of pretrained LLMs (aka. 'privileged information' in robotics) and dynamically adapts them using intrinsic, natural market rewards using …

abstract arxiv correlation covariance cs.ai cs.lg cs.ro data dynamic financial financial market forecasting hidden inspiration llms machine machine learning machine learning techniques market problem q-fin.cp reinforcement reinforcement learning robots struggle success them trade true type variables