GAN-GNN-QAOA Quant Portfolio
Budget: $10 – $30 USD
Objectives & Outcomes
Primary goals:
• Demonstrate market microstructure understanding via synthetic LOB generation and execution stress.
• Produce cross‑sectional alpha using a graph‑structured learning approach (GraphSAGE/GATv2).
• Formulate mean–variance selection as QUBO and solve with QAOA; compare to classical optimizers.
• Ship an end‑to‑end backtest and a dashboard; package results in a paper‑style report.
Key outcomes: metrics tables, ablations (with/without GAN augmentation; GNN vs linear), and deployable code.
Primary goals:
• Demonstrate market microstructure understanding via synthetic LOB generation and execution stress.
• Produce cross‑sectional alpha using a graph‑structured learning approach (GraphSAGE/GATv2).
• Formulate mean–variance selection as QUBO and solve with QAOA; compare to classical optimizers.
• Ship an end‑to‑end backtest and a dashboard; package results in a paper‑style report.
Key outcomes: metrics tables, ablations (with/without GAN augmentation; GNN vs linear), and deployable code.