Deep reinforcement learning for financial trading using price trailing. Feb...

Deep reinforcement learning for financial trading using price trailing. Feb 23, 2026 · Deep Reinforcement Learning framework for multi-asset portfolio allocation using Proximal Policy Optimization (PPO), custom trading environments, and risk-adjusted evaluation. This led to the development of various methods for analyzing and forecasting the behaviour of financial assets, ranging from traditional quantitative . The first model (Trail Environment) is our proposed method which is explained analytically the paper above. - mfzhang/20260223-Re This paper summarizes the research achievements of transaction systems, adaptive algorithms, and transaction strategies based on the progress of reinforcement learning models, which are commonly used in the financial field. Two models were developed in this project. Price Trailing for Financial Trading using Deep Reinforcement Learning Avraam Tsantekidis, Nikolaos Passalis, Anastasia-Sotiria Toufa, Konstantinos Saitas-Zarkias, Stergios Chairistanidis, and Anastasios Tefas This work uses a deep Q-network to design long-short trading strategies for futures contracts and analyzes how training based on a combination of artificial data and actual price series can be successfully deployed in real markets. In this work, we propose a Deep Reinforcement Learning-based approach that ensures consistent rewards are provided to the trading agent, mitigating the noisy nature of Profit-and-Loss rewards that are usually used. When we introduce "Positional Context," we expand the state space from external market data to include internal inventory data. Deep Reinforcement Learning (DRL) excels here because it does not just predict price; it learns a Policy, which is a mapping from the current market state to the best possible action. Financial trading has been widely analyzed for decades with market participants and academics always looking for advanced methods to improve trading performance A a AA AAA Aachen aah Aaliyah Aaliyah's aardvark aardvark's aardvarks Aaron AA's AB ab ABA aback abacus abacuses abacus's abaft abalone abalone's abalones abandon abandoned abandoning abandonment abandonment's abandons abase abased abasement abasement's abases abash abashed abashedly abashes abashing abashment abashment's abasing abate abated abatement abatement's abates abating abattoir This paper presents an AI stock market trading strategy advisor system that combines Long Short-Term Memory (LSTM) networks (for price prediction) with FinBERT-based Natural Language Processing (NLP) using financial sentiment and Deep Q-Network (DQN) reinforcement learning as deep neural networks for intelligent decision making. 6 days ago · Reinforcement Learning Relevant source files This page documents the Reinforcement Learning (RL) section of the repository, covering the full learning roadmap from foundational theory through deep RL algorithms, advanced topics, tooling, and finance-specific applications. In this work, we propose a Deep Reinforcement Learning-based approach that ensures consistent rewards are provided to the trading agent, mitigating the noisy nature of Profit-and-Loss In this article, we propose a deep reinforcement learning-based approach, which ensures that consistent rewards are provided to the trading agent, mitigating the noisy nature of profit-and-loss rewards that are usually used. Feb 20, 2026 · A new reinforcement learning framework, powered by Deep Q-Networks, is proving effective at optimizing market making strategies in dynamic environments with end-of-day closing auctions. In this article, we propose a deep reinforcement learning-based approach, which ensures that consistent rewards are provided to the trading agent, mitigating the noisy nature of profit-and-loss rewards that are usually used. Developing accurate financial analysis tools can be useful both for speculative trading, as well as for analyzing the behavior of markets and promptly responding to unstable conditions ensuring the smooth operation of the financial markets. Training Developing accurate financial analysis tools can be useful both for speculative trading, as well as for analyzing the behavior of markets and promptly responding to unstable conditions ensuring the smooth operation of the financial markets. Jun 9, 2020 · This paper aims to provide insight into various DL methods for financial trading, under both the supervised and reinforcement learning schemes, and discusses and demonstrates their usefulness through corresponding research studies. In contrast, in this paper we propose a novel price trailing method that goes beyond traditional price forecasting by reformulating trading as a control problem, effectively overcoming the aforementioned limitations. This led to the development of various methods for analyzing and forecasting the behaviour of financial assets, ranging from traditional quantitative Oct 28, 2025 · A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions Article Full-text available Nov 2021 Li Chen Cheng Yu-Hsiang Huang Ming-hua Hsieh Mu-En Wu View 5 days ago · In parallel, reinforcement learning has been widely used in finance for portfolio and trading decisions, including deep Q-learning variants and actor–critic methods, often with a focus on sample efficiency, market dynamics, and reward shaping . nes day mzk ccu kzt nza ffv fat ryh vvt kso tsw qhr yjo izf