Deep Reinforcement Learning for Stock Market Portfolio Optimization

Portfolio optimization seeks to decide an optimum combination of assets to maximize returns with the associated risk, which is difficult due to market volatility, transaction costs as well as the complex nature of risk limits. Conventional optimization techniques are not always able to handle the dynamic nature of the market and other nonlinear asset characteristics. The suggested methodology would resolve these issues by using a deep reinforcement learning model consisting of the Proximal Policy Optimization (PPO) algorithm applied in a trading simulation environment developed specifically. This agent becomes informed on how to dynamically invest on the portfolio based on the normalized stock price characteristics and engineered indicators and maximize cumulative returns and costs on the transaction. The model has proven to be effective based on a full stock data, the cumulative return is 8664.44, annualized is 4.25, Sharpe ratio is 9.33, and the maximum drawdown is controlled at −0.21 indicating its effectiveness and flexibility in financial portfolio management.