Ashok Jana

Financial markets don’t sit still. Volatility spikes, trends reverse, and what worked last month stops working today. This project builds an adaptive trading agent using deep reinforcement learning to handle these shifts. Instead of fixed rules or static predictions, the agent learns trading policies through simulated market interaction. I’ll use deep neural networks to approximate action-value functions for sequential decisions. Raw historical data gets processed into state representations capturing price movements, volume, volatility signals, and macro indicators. Several baseline strategies get compared against different RL configurations to test what actually improves risk adjusted returns. The core question: can agents learn to recognize and adapt to different market regimes? This project tests whether regime-aware representations help models generalize across bull markets, bear markets, and choppy sideways action.

TBD