The project successfully developed a reinforcement learning (RL)-based prototype for financial index replication, demonstrating the ability to track stock and private equity indices using liquid instruments. Key achievements include:

Dual-Objective Reward Framework: Balanced index tracking with minimal tracking error.

Algorithm Selection & Validation: Tested and optimized deep RL models for performance.

Docker-Based Prototype: Ensured reproducibility and scalability for further research.

Application to Private Equity Indices: Explored RL’s feasibility for illiquid asset replication.

The results confirm RL’s potential for dynamic index replication, with future improvements focused on real-time data integration, parameter tuning, and broader market applications.

The project explored the use of Reinforcement Learning (RL) to replicate financial indices while minimizing tracking error. A prototype was developed, applying deep RL algorithms to track stock and private equity indices using liquid instruments such as futures on equities, fixed income, FX, and commodities.

Key achievements include a dual-objective reward framework, a Docker-based testing environment, and successful backtests demonstrating feasibility. The results highlight RL’s potential for index replication, paving the way for further refinement, parameter tuning, and real-world implementation.