Experimental
RL-Driven Portfolio Rebalancer
Reinforcement-learning agent that learns to rebalance a multi-asset portfolio under transaction-cost and risk constraints.
About this project
Existing rebalancing rules were static and ignored regime changes; the team wanted to explore whether RL could adapt continuously.
Solution
PPO agent trained on simulated and historical market data with a transaction-cost-aware reward; backtested against rule-based baselines.
Technology
- Python
- PyTorch
- Stable-Baselines3
- Pandas
- Optuna
Impact
Out-of-sample Sharpe +0.18 versus rule-based benchmark; experimental — not deployed to client capital.