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arXiv Statistics ML

Deep reinforcement learning for optimal trading with partial information

The paper explores optimal trading under partial information, modeling the trading signal as an Ornstein‑Uhlenbeck process with regime switching. It proposes three reinforcement‑learning approaches that integrate recurrent neural networks to filter latent market regimes, comparing a one‑step method and two two‑step methods that use posterior regime probabilities or signal forecasts. Simulations and an equity pairs‑trading case study show that using posterior probability estimates of the latent regime yields the best cumulative rewards and more interpretable strategies.

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arXiv AI
Oct 5

When a Correct Reward Is Not Enough: Diagnosing and Guiding PPO in an Analytically Solved Broker-Trader Game

The paper investigates whether a proximal policy optimisation (PPO) agent can learn to act as a broker in a continuous‑time broker‑trader game that has an analytically solved optimal strategy. By discretising the continuous‑time reward and validating the implementation, the authors show that a PPO agent with a feed‑forward neural network can approximate the reference action when there is no uninformed order flow, but struggles under stochastic uninformed flow, with critics failing to rank actions reliably. The study further demonstrates that a causal certainty‑equivalent controller based on observable history outperforms PPO under partial information, and that freezing the analytical policy and fine‑tuning PPO after a change in execution cost yields a measurable improvement. whyItMatters:"The analytical solution serves both as a diagnostic benchmark for RL performance and as a practical starting policy that can be adapted to changing market conditions, illustrating how theoretical finance models can guide and improve reinforcement learning in complex financial control tasks."

By Siu Tung Wong (Institute of Finance and Technology, University College London), Carlo Campajola (Institute of Finance and Technology, University College London, UZH Blockchain Center)
agentsreinforcement-learningbenchmarks
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arXiv Machine Learning
Jul 20

CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach

arXiv:2607. 16028v1 Announce Type: new Abstract: This paper presents our system for Task 3 of the CLEF 2026 FinMMEval Lab, which requires daily long, flat, or short trading decisions for Bitcoin (BTC) and Tesla (TSLA) using news and historical market data.

By Andrei Neagu, Eeham Khan, Leila Kosseim
llmsagentsreinforcement-learning
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arXiv AI
Oct 16

Plan Before You Trade: Inference-Time Optimization for RL Trading Agents

FinPILOT is a plugin inference‑time policy optimization framework that enhances pretrained reinforcement‑learning trading agents by leveraging a multi‑step price forecaster to generate imagined market trajectories. At each decision point, it optimizes the pretrained policy using the forecaster’s predicted price path before executing a trade, without altering the original training pipeline. Across five pretrained policies and multiple market settings, FinPILOT consistently improves total return and Sharpe ratio, with gains increasing as forecast accuracy improves.

By Eun Go, Rohan Deb, Arindam Banerjee
agentsreinforcement-learning
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arXiv AI
Jul 9

Can Reinforcement Learning Efficiently Discover Price Manipulation?

arXiv:2607. 06121v1 Announce Type: cross Abstract: In this paper, we investigate whether a model-free RL agent can identify and exploit price manipulation opportunities more effectively than a traditional model-based approach that assumes correct specification of the data-generating process but relies on noisy parameter estimates.

By Ioanna-Yvonni Tsaknaki, Andrea Macr\`i, Fabrizio Lillo
agentsreinforcement-learningroboticsbenchmarks
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arXiv AI
Sep 25

Agent Memory with Episodic Retrieval for Financial Decision-Making

META (Memory Enhanced Trading Agent) is a new agent-based trading framework that augments large language models with episodic memory. It combines specialized indicator agents—such as Trend, MACD, Stochastic, RSI, SMA, AVWAP, and Heikin‑Ashi—with a Decision Agent that fuses their reports, while a Memory module retrieves and updates past trading episodes encoded as market state embeddings with outcomes and reflections. By recalling relevant experiences and adaptively reweighting signals under similar market regimes, META improves directional accuracy and robustness in short‑horizon evaluations, offering regime‑aware, interpretable, and low‑latency decision‑making for financial trading.

By Nuoyue Xu, Jiang Liu, Wenxuan Huang, Xiang Zhang, Juntai Cao, Jiaqi Wei
llmsragagents
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