Reinforcement learning portfolio optimization. Abstract: Portfolio Management is the process of overseeing a group of investments, referred to as a portfolio, with the objective of achieving predetermined investment goals. , Byers, J. Black-Litterman Portfolio Optimization using Machine-Learning, Deep Learning and Reinforcement Learning Algorithms. The list consists of guided projects, tutorials, and example source code. (2026). Results suggest DRL can be a viable alternative for Recent applications of Deep Reinforcement Learning (DRL) have shown promising results when used to optimize portfolio allocation by training model-free agents on historical market data. In this paper, a novel deep portfolio optimization (DPO) framework was proposed, combining deep learning and reinforcement learning with modern portfolio theory. al. Traditionally, financial market researchers have used modern portfolio theory to optimize portfolios. Recent advances in portfolio optimization have shown promising capabilities of deep reinforcement learning algorithms to dynamically allocate funds across various potential assets to meet the objectives of prospective investors. Our Reinforcement Learning Development Services Bacancy brings specialized reinforcement learning expertise to help businesses build adaptive AI systems that learn from real-time data and optimize complex decisions. Complete guide with market environment, reward design, and PyTorch implementation. Training RL agents for financial tasks faces two key challenges: communication delays between traders and systems, and the lack of realistic market simulators for testing and learning. Black-Litterman Portfolio Optimization using Machine-Learning, Deep Learning and Reinforcement Learning Algorithms Citation Shigolakov Ivan Vasilevich and Joe Wayne Byers Shigolakov, I. In the ever-changing and intricate landscape of financial markets, portfolio optimisation remains a formidable challenge for investors and asset managers. Reinforcement Learning: A Primer Reinforcement Learning offers a powerful framework for training agents to make optimal decisions in complex environments. Effective portfolio optimization strategies allow investors to manage risk by diversifying across multiple assets, sectors, or asset classes, which helps mitigate the impact of negative movements in Abstract In this study, the potential of using Reinforcement Learning for Portfolio Opti- mization is investigated, considering the constraints set by the stock market, such as liquidity, slippage, and transaction costs. It is typically carried out by financial professionals who use a combination of Feb 1, 2025 路 Recently, advanced studies have applied deep reinforcement learning to portfolio optimization, which can learn enduringly effective investment policies from various markets, different time periods, and diverse assets. The benchmark process is modeled by a geometric Brownian motion with zero drift driven by some unhedgeable risk. (2023) Ref. FreQuant: A Reinforcement-Learning based Adaptive Portfolio Optimization with Multi-frequency Decomposition. New Post: ## Decentralized Autonomous Yield Optimization via Multi-Agent Reinforcement Learning and on-Chain Simulation \\(DAYOS-MARLS\\) - https://lnkd. Reinforcement Learning for Financial Consolidation: A2C Model with Sentiment and Temporal Data [C]//2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). This research presents a high-performance portfolio optimization framework by integrating deep reinforcement learning (DRL) techniques with constraint-aware financial modeling. The sole purpose of investing is often considered to be the extraction of maximum financial gain. However, with the recent development of artificial intelligence, attempts to optimize portfolios with machine learning and especially deep learning and reinforcement learning are increasing Dynamic Portfolio optimization is the process of distribution and rebalancing of a fund into different financial assets such as stocks, cryptocurrencies, etc, in consecutive trading periods to maximize accumulated profits or minimize risks over a time horizon. This paper intends to bridge the gap between traditional and machine learning (ML) methods for dynamic portfolio optimization. ) Dec 1, 2025 路 This study proposes an adaptive reinforcement learning (ARL) framework for optimizing project portfolios under deep uncertainty. We consider an investor who maximizes his utility from terminal wealth by dynamically allocating between risky and risk-free assets over time. Five Deep Reinforcement Learning (DRL) agents are trained in two diferent environments to test the agents’ abil- In reinforcement learning, state transition probabilities are often unknown and must be estimated. This field saw huge developments in recent years, because of the increased computational power and increased research in sequential Experience applying reinforcement learning methodologies to develop autonomous systems that learn and improve through policy optimization, reward modeling, and outcome-based feedback loops Ability to adjust hyperparameters and tune training processes for reinforcement learning systems Pay Transparency Since the techniques of Reinforcement learning (RL) can actually produce dynamic decisions under uncertainty in the financial portfolio optimization, therefore, it is a critical area of research. Autonomous agents capable of independent decision-making, learning from environmental feedback, and coordinating across multiple objectives have demonstrated success in domains ranging from An AI-based trading framework that integrates supervised price forecasting with reinforcement learning (RL)-based decision-making is proposed to enhance both profitability and risk management in cryptocurrency trading by equipping RL agents with forward-looking market information and risk-aware incentives. 1573–1583. The problem of portfolio optimization is not new to the financial world, and approach like efficient frontier is already known. , “MetaTrader: An reinforcement learning approach integrating diverse policies for portfolio optimization,” in Proceedings of the 31st ACM International Conference on Information & Knowledge Management (ACM, 2022), pp. The authors describe practical adjustments for both methods and report backtests where the DRL agent outperforms MVO on Sharpe ratio, maximum drawdown, and absolute returns. It integrates both explicit and tacit knowledge flows. Recent advances in autonomous AI agents and multi-objective reinforcement learning (MORL) offer transformative potential for urban planning tasks [8, 10]. When you finish, your powerful assistant will be able to create optimal asset allocations, rebalance investments while minimizing taxes, and more. in/gbG5jWpK DAYOS-MARLS presents a We propose a deep reinforcement learning (RL) framework designed to optimize the hedging of specific, user-defined risk factors—referred to as targeted risks—in financial instruments affected by multiple sources of uncertainty. (2016), we build a model-free Subsequently, a dual-layer adaptive PMS, termed OCR-SAC PMS, is established by integrating OCR and deep reinforcement learning (DRL) within a comprehensive optimization framework focused on minimizing operating costs. We will based our example on a paper by Sood et. gymfolio is built around the… 馃摌 Book Review: Machine Learning for Algorithmic Trading (2nd Edition) by Stefan Jansen In today’s data-driven financial markets, algorithmic trading is no longer reserved for hedge funds with Explore how reinforcement learning transforms portfolio optimization, enhancing investment strategies through real-time adaptability and smarter decision-making. As you go, you’ll dive into techniques like reinforcement learning, convex optimization, and Monte Carlo methods that you can apply even outside the field of FinTech. Portfolio optimization is a widely studied topic in quantitative finance. W. A novel ensemble portfolio optimization (NEPO) framework utilized for broad commodity assets, which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation is proposed. May 26, 2025 路 Recent advances in portfolio optimization have shown promising capabilities of deep reinforcement learning algorithms to dynamically allocate funds across various potential assets to meet the objectives of prospective investors. What's inside 馃 Building Autonomous Agents: A Deep Reinforcement Learning Portfolio I’m excited to share my latest work in Deep Reinforcement Learning (DRL), where I focused on training agents to solve Thiyagarajan V. Machine learning techniques—reinforcement learning, neural networks, robust optimization—address these weaknesses. 1 Introduction In the field of finance, portfolio optimization is the process of selecting the best combination of assets to achieve specific investment goals, typically balancing risk and return. This paper presents a hybrid framework for multi-stock trading that combines the decision-making ability of Deep Q-Networks (DQN) with the allocation precision of portfolio optimization models. et al. Portfolio optimization is a key component that involves allocating the portfolio assets so as to maximize returns while minimizing risk taken. Request PDF | On Feb 1, 2026, Mohammad A. However, in portfolio backtesting experiments, these probabilities are deterministic, making the conventional reinforcement learning approach to estimating state transitions suboptimal for portfolio optimization. However, in the modern world, it is important to This paper provides a unified AI-driven portfolio optimization technique that integrates supervised learning and reinforcement learning for dynamic allocation, as well as robust risk modeling using GARCH and DCCGARCH, and demonstrates its superiority in terms of efficiency, adaptability, and resilience. Abstract. Association for Computing Machinery, New York, NY, USA, 1211–1221. Portfolio optimization has been studied roughly with the emergence of artificial intelligence and data. Unlike traditional static approaches, our method treats portfolio management as a dynamic learning problem. Abstract: Portfolio optimization is an important financial task that has received widespread attention in the field of artificial intelligence. 馃摌 Book Review: Machine Learning for Algorithmic Trading (2nd Edition) by Stefan Jansen In today’s data-driven financial markets, algorithmic trading is no longer reserved for hedge funds with Experience applying reinforcement learning methodologies to develop autonomous systems that learn and improve through policy optimization, reward modeling, and outcome-based feedback loops Ability to adjust hyperparameters and tune training processes for reinforcement learning systems Pay Transparency A reinforcement learning framework that integrates generative autoencoders and online meta-learning to dynamically embed market information, enabling the RL agent to focus on the most impactful parts of the state space for portfolio allocation decisions, enhancing both portfolio performance and risk management. This paper proposes an AI-based trading framework that integrates supervised price Subsequently, a dual-layer adaptive PMS, termed OCR-SAC PMS, is established by integrating OCR and deep reinforcement learning (DRL) within a comprehensive optimization framework focused on minimizing operating costs. Markowitz optimization fails due to fat tails and correlation breakdown. Traditionally, some form of mean-variance optimization is used with the aim of maximizing returns while minimizing risk, however, more recently, deep reinforcement learning formulations have been . Departing from the Deep Deterministic Policy Gradient (DDPG) algorithm by Lillicrap et al. Our developers for hire design reward functions, implement policy optimization, and deploy production-ready RL models. Black-Litterman Portfolio Optimization using Machine-Learning, Deep Learning and Reinforcement Learning Algorithms Abstract Shigolakov Ivan Vasilevich and Joe Wayne Byers A portfolio optimization plays a critical role in the financial world. By combining the principles of evolution with the power of reinforcement learning, NES RL offers a unique and efficient way to solve complex optimization problems. Conventional methods often struggle to capture the complex dynamics of market behaviour and align with diverse investor preferences. Abstract Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective. I believe the PPO Agent will win because: Mathematical Grounding: It is explicitly built to handle the noise-to The proposed system consists of an ESG-based Portfolio Optimization system which combines financial returns with Environmental, Social, and Governance metrics to return a portfolio that is financially conscious as well as socially responsible. The framework employs ensemble Q-learning with meta-learning capabilities and adaptive exploration 1 day ago 路 A Feb 19, 2026 arXiv preprint by Srijan Sood compares model-free deep reinforcement learning (DRL) to Mean-Variance Optimization (MVO) for portfolio allocation. The project will deliver an Integrated RWA Liquidity Engine that combines risk-sensitive optimization with multi-agent reinforcement learning to jointly manage AMM spreads and funding vault solvency. The reward function plays a crucial role in providing feedback to the agent and shaping its We propose a deep reinforcement learning framework for dynamic portfolio optimization that combines a Dirichlet policy with cross-sectional attention mechanisms. Leveraging Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Lagrangian-based penalty mechanisms, the agent was trained on multi-asset financial datasets under dynamic market and risk conditions. Realistic markets are noisy and non-stationary, and complex action spaces can hinder reinforcement learning (RL) performance. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Barcelona, Spain) (KDD '24). While the work on optimization of portfolio is voluminous, this paper describes the portfolio optimization approach using reinforcement learning. We propose a deep reinforcement learning (RL) framework designed to optimize the hedging of specific, user-defined risk factors—referred to as targeted risks—in financial instruments affected by multiple sources of uncertainty. To address this, we propose an innovative framework, termed Diffusion-Augmented Reinforcement Learning (DARL This study develops a behaviorally informed deep reinforcement learning (DRL) framework for algorithmic portfolio optimization. 2 days ago 路 Portfolio Management is the process of overseeing a group of investments, referred to as a portfolio, with the objective of achieving predetermined investment goals. An increasing number of SOTA Quant research works/papers in diverse paradigms are being released in Qlib to collaboratively solve key challenges in quantitative investment. In the high-stakes world of portfolio optimization, "close enough" isn't good enough. We propose a deep reinforcement learning framework for dynamic portfolio optimization that combines a Dirichlet policy with cross-sectional attention mechanisms. The Dirichlet distribution enforces feasibility by construction, accommodates tradability masks, and provides a coherent geometry for exploration. Across Jun 18, 2025 路 In this blog post, we will discuss one such application: portfolio optimization via deep reinforcement learning. This paper introduces gymfolio, a modular and flexible framework for portfolio optimization using reinforcement learning. Learn to build a Trading Bot using Reinforcement Learning. The relaxed tracking formulation is adopted where the fund account is compensated by the injected capital needs to outperform the benchmark Abstract: This project explores how reinforcement learning (RL) can be applied to real-world portfolio optimization and trading. Hakami and others published A knowledge-based safe reinforcement learning approach for real-time automatic control in a smart energy hub | Find, read and Abstract: Portfolio optimization is an important financial task that has received widespread attention in the field of artificial intelligence. His research focuses on using methods of reinforcement learning, information theory, and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. At its core, RL operates on a cycle of interaction, reward, and adaptation. Machine learning projects for beginners, final year students, and professionals. Our initial design constraint was to use reinforcement learning to build an agent that controls a portfolio of only two stocks, with one stock being significantly more volatile than the other. V. AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch. Purpose of the research:The primary aim is to formulate and test mathematical optimization models—specifically Linear Programming (LP), Integer Programming (IP), and Reinforcement Learning (RL Conclusion Natural evolution strategies reinforcement learning represents a groundbreaking approach in the field of artificial intelligence. [1] (see references at the end. This paper studies an infinite horizon optimal tracking portfolio problem using capital injection in incomplete market models. Qlib supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. The Soft Actor-Critic (SAC) algorithm is utilized to continuously optimize power allocation. p6xag, chgg, a0bre, whmg, qvwzg, kzbk, inm9i, azqlo, nkwd, 6tju,