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Quant · Graph ML · 2026

Forward Risk Manager

A walk-forward downside-risk pipeline that turns market data into graphs, trains Forward-Forward and backprop GNN variants, and reports model quality, speed, and trading economics side by side.

Economic scorecard from the Forward Risk Manager benchmark report.
Economic scorecard from the published benchmark artifacts.

Problem

A risk model can look excellent on accuracy and still lose money. I wanted a single place where model quality, throughput, and the actual trading and risk numbers sit next to each other instead of in separate notebooks.

Approach

I built the whole path around one reusable codebase: graph construction, two flavors of GNN training (Forward-Forward and standard backprop), walk-forward benchmarks, scenario calibration, stress tests, parameter sweeps, and the report files at the end.

Outcome

The most useful part is where the numbers disagree. A model can score well statistically and still do badly on the economics, and the generated reports make that gap easy to see.