# Graph-augmented reinforcement learning for stock trading

- Period: Sep 2022 – Sep 2024
- Domain: Graduate research, quantitative finance (Research and public sector)
- Role: Researcher, MSc thesis
- Capabilities: Applied ML and research
- Stack: Python, TD3, Node2Vec, Reinforcement learning
- URL: https://farhanbaluch.com/work/graph-rl-trading

A TD3 trading agent that uses Node2Vec embeddings of how stocks relate, outperformed RL baselines on Dow 30 data, and explains its own strategy.

**AI angle.** An RL agent is a sequential decision-maker, like an LLM agent. Designing rewards, comparing a policy against baselines and explaining what it learned is the same discipline I now bring to agent evaluation.

**What this shows:**
- I understand agents from the learning side: reward design, policy evaluation, baselines.
- I can run research end to end, from hypothesis to a defended thesis.

### Problem

RL trading agents usually treat each stock independently, even though stocks move together by sector and supply chain. Their strategies are also hard to explain, which makes them hard to trust.

### What I built

- A graph-augmented TD3 agent: Node2Vec embeddings of the relationships between stocks feed the agent's state.
- Sector clustering and technical indicators to improve risk-adjusted returns.
- Post-hoc visualizations that make the agent's strategy auditable.

### Outcome

- Outperformed RL baselines on Dow 30 data.
- MSc in Computer Science (AI specialization) with a 4.0 GPA and an Ontario Graduate Scholarship.