Research on reinforcement learning stock trading strategy based on cost-aware information selection

A Market-Aware Dynamic Trading Strategy Based on Unsupervised Clustering and Reinforcement Learning

The complexity and dynamics of the stock market make traditional absolute price prediction models based on supervised learning face great risks and limitations in practical business applications. This report proposes an innovative Hybrid Deep Reinforcement Learning trading framework to shift the forecast target from st...

Optimization of technical analysis-driven algorithmic trading using deep reinforcement learning

Reinforcement Learning for Adaptive Market-Making with Inventory-Aware Risk Constraints

It is essential for market-making strategies to optimally capture the spread, balance their inventory, and adjust to changing order-book dynamics. In this paper, we explore the use of Proximal Policy Optimization (PPO) to perform adaptive market making in a simulated limit-order-book setting using the MSFT data from th...

Uncertainty-Aware Forecast-Conditioned Reinforcement Learning for Multi-Asset Algorithmic Trading

Financial markets are challenging to navigate due to changing regimes, high volatility, and unpredictable investor behavior, often leading to model misspecification in classical stationary frameworks like Moving Average (MA) and Autoregressive (AR) models. To address this, we propose an uncertainty-aware framework that...

Reinforcement Learning-Driven Dynamic Trading Strategies for Financial Markets

Results from experiments show that RL-DynTrade outperforms both traditional approaches and RL models that use PPO or DQN alone in terms of stability, flexibility, and profitability.

Hybrid Deep Learning and Reinforcement Learning for Adaptive Cryptocurrency Trading Using Attention-LSTM and Soft Actor-Critic

This study proposes a hybrid artificial intelligence-based trading framework that integrates deep learning and reinforcement learning to develop an adaptive Bitcoin trading strategy. The proposed approach combines an Attention Long Short-Term Memory model to estimate the probability of future price movements with a Sof...