Data to 7 Oct 2026

Deep Reinforcement Learning for Trading

arXiv 1911.10107 ↗First posted 22 Nov 2019q-fin.CP cs.LG q-fin.TRcited by 73

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Abstract (arXiv)

We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatility scaling is incorporated to create reward functions which scale trade positions based on market volatility. We test our algorithms on the 50 most liquid futures contracts from 2011 to 2019, and investigate how performance varies across different asset classes including commodities, equity indices, fixed income and FX markets. We compare our algorithms against classical time series momentum strategies, and show that our method outperforms such baseline models, delivering positive profits despite heavy transaction costs. The experiments show that the proposed algorithms can follow large market trends without changing positions and can also scale down, or hold, through consolidation periods.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Nov 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 14; 2023: 13; 2024: 11; 2025: 13; 2026: 7.

73 papers.

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2021Recent Advances in Reinforcement Learning in Financecited by 102
2021FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Financecited by 57
2025StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?cited by 43
2024A review on data-driven constitutive laws for solidscited by 35
2021Reinforcement Learning for Quantitative Tradingcited by 24
2025FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Tradingcited by 23
2021Deep Reinforcement Learning for Active High Frequency Tradingcited by 21
2022Offline Reinforcement Learning with Differential Privacycited by 19
2021Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Reviewcited by 17
2023Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approachcited by 17
2021Trading with the Momentum Transformer: An Intelligent and Interpretable Architecturecited by 16
2022GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesiscited by 16

38 more

2023Dynamic Datasets and Market Environments for Financial Reinforcement Learningcited by 15
2025Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategycited by 15
2021DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunitiescited by 14
2022A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networkscited by 12
2024The Evolution of Reinforcement Learning in Quantitative Finance: A Surveycited by 11
2021Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Unitscited by 10
2021Deep Learning for Market by Order Datacited by 8
2021Realised Volatility Forecasting: Machine Learning via Financial Word Embeddingcited by 7
2021Quantum Machine Learning for Financecited by 7
2024MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Tradingcited by 7
2024In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before An Ongoing Trajectory Terminatescited by 7
2023The AI Revolution: Opportunities and Challenges for the Finance Sectorcited by 6
2022Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfittingcited by 5
2023PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Marketscited by 5
2025TRADES: Generating Realistic Market Simulations with Diffusion Modelscited by 5
2022The Recurrent Reinforcement Learning Crypto Agentcited by 4
2024Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimizationcited by 4
2025TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Datacited by 4
2026Can Blindfolded LLMs Still Trade? An Anonymization-First Framework for Portfolio Optimizationcited by 4
2021Reinforcement Learning with Expert Trajectory For Quantitative Tradingcited by 3
2021Reinforcement Learning for Systematic FX Tradingcited by 3
2023Neuro-symbolic Meta Reinforcement Learning for Tradingcited by 3
2024CNN-DRL with Shuffled Features in Financecited by 3
2024Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agentcited by 3
2025FlowHFT: Imitation Learning via Flow Matching Policy for Optimal High-Frequency Trading under Diverse Market Conditionscited by 3
2021A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rulescited by 2
2021High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learningcited by 2
2022ARLO: A Framework for Automated Reinforcement Learningcited by 2
2022Markov Decision Processes under Model Uncertaintycited by 2
2022Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Networkcited by 2
2024FinLLM-B: When Large Language Models Meet Financial Breakout Tradingcited by 2
2025Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluationcited by 2
2026Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimizationcited by 2
2022Financial Vision Based Reinforcement Learning Trading Strategycited by 1
2022Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learningcited by 1
2023Improving International Climate Policy via Mutually Conditional Binding Commitmentscited by 1
2023Conservative Predictions on Noisy Financial Datacited by 1
2024Reinforcement Learning Pair Trading: A Dynamic Scaling approachcited by 1

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