Deep Reinforcement Learning for Dynamic Portfolio Optimization Under Regime Shifts
Traditional portfolio optimization techniques, while powerful, often falter in the face of dynamic market conditions. These strategies, typically relying on historical data and static assumptions about asset returns, struggle to adapt to the ever-changing realities of financial markets. This is where reinforcement learning (RL) offers a compelling alternative.
RL, inspired by how humans learn through trial and error, enables machines to make sequential decisions in uncertain environments to maximize a cumulative reward. In the context of portfolio optimization, this translates to training an RL agent that learns to rebalance a portfolio based on the observed market conditions and predicted future states.
This project delves into the application of Deep Reinforcement Learning (DRL) for dynamic portfolio optimization, specifically addressing the challenge of regime shifts in financial markets. We’ll design a Deep Q-Network (DQN) agent that adapts its investment strategy to different market regimes — bull, bear and sideways — aiming to maximize risk-adjusted returns.
Table of Contents
- Project Setup and Data Acquisition: Setting up the environment and fetching historical stock data using yfinance.
