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arXiv · 2605.25527

DeepSeekMath Meets Order Book: Group-Aware Policy Optimization for High-Frequency Directional Trading

Sayak Charabarty·Souradip Pal
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Abstract

This paper studies reinforcement learning for high-frequency trading on limit order books by pairing an Order-Flow-based state model with policy-gradient methods. Instead of value-based RL techniques like tabular Q-learning, our approach deploys policy-based methods like vanilla PPO and DeepSeekMath-inspired variants like GRPO and GSPO, that use group-normalized updates and downside-aware shaping. On backtests with financial assets AMZN, AAPL, and GOOG under a simplified backtesting setup based on spread-scaled rewards, these new policies improve net average PnL, profitability, and drawdown over the Q-Learning baseline. Our results show that (1) Order-Flow signals are an adequate state for policy RL and (2) group-aware PPO surrogates are preferable over value-based baselines.

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Sayak Charabarty, Souradip Pal. 2026-05-25. DeepSeekMath Meets Order Book: Group-Aware Policy Optimization for High-Frequency Directional Trading. https://doi.org/10.5220/0015139200004091

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