Stock market prediction using deep reinforcement learning

The stock market exhibits complex, volatile behavior influenced by a multitude of factors ranging from economic indicators to investor sentiments. Accurate prediction remains a significant challenge. In this project, we propose an advanced framework leveraging Deep Reinforcement Learning (DRL), specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, to model intelligent trading strategies. The system incorporates real-time financial data, sentiment analysis of market news using VADER, and a continuous retraining pipeline to adapt to changing market conditions. Our experimental results demonstrate notable improvement in prediction accuracy and investment returns compared to traditional machine learning approaches.