FineFT: Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading

• URL: http://arxiv.org/abs/2512.23773v1
• Date: Mon, 29 Dec 2025 11:56:33 GMT
• Title: FineFT: Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading
• Authors: Molei Qin, Xinyu Cai, Yewen Li, Haochong Xia, Chuqiao Zong, Shuo Sun, Xinrun Wang, Bo An,
• Abstract summary: The Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading (FineFT) is a novel ensemble framework with stable training and proper risk management.<n>We show FineFT outperforms 12 SOTA baselines in 6 financial metrics, reducing risk by more than 40% while achieving superior profitability compared to the runner-up.
• Score: 39.845446417892525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Futures are contracts obligating the exchange of an asset at a predetermined date and price, notable for their high leverage and liquidity and, therefore, thrive in the Crypto market. RL has been widely applied in various quantitative tasks. However, most methods focus on the spot and could not be directly applied to the futures market with high leverage because of 2 challenges. First, high leverage amplifies reward fluctuations, making training stochastic and difficult to converge. Second, prior works lacked self-awareness of capability boundaries, exposing them to the risk of significant loss when encountering new market state (e.g.,a black swan event like COVID-19). To tackle these challenges, we propose the Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading (FineFT), a novel three-stage ensemble RL framework with stable training and proper risk management. In stage I, ensemble Q learners are selectively updated by ensemble TD errors to improve convergence. In stage II, we filter the Q-learners based on their profitabilities and train VAEs on market states to identify the capability boundaries of the learners. In stage III, we choose from the filtered ensemble and a conservative policy, guided by trained VAEs, to maintain profitability and mitigate risk with new market states. Through extensive experiments on crypto futures in a high-frequency trading environment with high fidelity and 5x leverage, we demonstrate that FineFT outperforms 12 SOTA baselines in 6 financial metrics, reducing risk by more than 40% while achieving superior profitability compared to the runner-up. Visualization of the selective update mechanism shows that different agents specialize in distinct market dynamics, and ablation studies certify routing with VAEs reduces maximum drawdown effectively, and selective update improves convergence and performance.

Related papers

• Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets [57.179679246370114]
  In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices.<n>During deployment, these agents trade in live markets where their own transactions can shift asset prices, a phenomenon known as market impact.<n>Traditional robust RL approaches address this model misspecification by optimizing the worst-case performance over a set of uncertainties.<n>We develop a novel class of elliptic uncertainty sets, enabling efficient and tractable robust policy evaluation.
  arXiv  Detail & Related papers  (2025-10-22T18:22:25Z)
• Learn to Rank Risky Investors: A Case Study of Predicting Retail Traders' Behaviour and Profitability [3.731289189298451]
  We propose a profit-aware risk ranker (PA-RiskRanker) that reframes the problem of identifying risky traders as a ranking task.<n>Our approach features a Profit-Aware binary cross entropy (PA-BCE) loss function and a transformer-based ranker enhanced with a self-cross-trader attention pipeline.<n>Our research critically examines the limitations of existing deep learning-based LETOR algorithms in trading risk management.
  arXiv  Detail & Related papers  (2025-09-20T10:41:13Z)
• Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents [69.58565132975504]
  Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks.<n>We present the Agent Trading Arena, a virtual zero-sum stock market in which LLM-based agents engage in competitive multi-agent trading.
  arXiv  Detail & Related papers  (2025-02-25T08:41:01Z)
• Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework [0.0]
  This study presents a Reinforcement Learning-based portfolio management model tailored for high-risk environments. We implement the model using the Soft Actor-Critic (SAC) agent with a Convolutional Neural Network with Multi-Head Attention. Tested over two 16-month periods of varying market volatility, the model significantly outperformed benchmarks.
  arXiv  Detail & Related papers  (2024-08-09T23:36:58Z)
• MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading [20.3106468936159]
  Reinforcement learning (RL) has become another appealing approach for high-frequency trading (HFT) We propose a novel Memory Augmented Context-aware Reinforcement learning method On HFT, empha.k.a. MacroHFT. We show that MacroHFT can achieve state-of-the-art performance on minute-level trading tasks.
  arXiv  Detail & Related papers  (2024-06-20T17:48:24Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach [29.706515133374193]
  We show that existing deep learning methods are sensitive to random seeds and network routers. We propose a novel two-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up trading strategy design workflow of successful trading firms. AlphaMix significantly outperforms many state-of-the-art baselines in terms of four financial criteria.
  arXiv  Detail & Related papers  (2022-06-07T08:58:00Z)
• DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities [33.28409845878758]
  We propose DeepScalper, a deep reinforcement learning framework for intraday trading. We show that DeepScalper significantly outperforms many state-of-the-art baselines in terms of four financial criteria.
  arXiv  Detail & Related papers  (2021-12-15T15:24:02Z)
• Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning [72.28520951105207]
  Overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning. We propose a novel regularization-based update scheme that penalizes large joint action-values deviating from a baseline. We show that our method provides a consistent performance improvement on a set of challenging StarCraft II micromanagement tasks.
  arXiv  Detail & Related papers  (2021-03-22T14:18:39Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.