Reinforcement Learning
RL Portfolio Optimizer
Deep reinforcement learning for stock portfolio allocation. PPO agent with custom Gymnasium environment.
About the Project
An end-to-end reinforcement learning system for portfolio allocation across Indian stocks. A PPO agent learns daily portfolio weights in a custom Gymnasium environment that penalizes transaction costs and balances return against risk. Includes an interactive Streamlit dashboard for backtesting with comprehensive metrics.
Key Features
- Proximal Policy Optimization (PPO) agent for portfolio weight allocation
- Custom Gymnasium environment with transaction cost penalties
- Balances return vs risk with multiple metrics: CAGR, Volatility, Sharpe, Max Drawdown
- Interactive Streamlit backtesting dashboard
- Trained on Indian NSE stock data via yfinance
Impact
Demonstrates real ML engineering -- custom RL environments, reward shaping, and production-ready backtesting infrastructure.
Tech Stack
PythonStable-Baselines3PPOGymnasiumyfinanceStreamlit
Metrics
Custom RL environment
PPO agent
Interactive backtesting
CAGR, Sharpe, Max Drawdown
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