LIVE QUANT DASHBOARD
Real production systems I designed, built, and deployed — distributed compute, Monte Carlo risk engines, and machine learning APIs backed by live FastAPI services.
Specializing in quantitative risk modeling, Monte Carlo simulation, time-series forecasting, pricing optimization, PyTorch-based machine learning systems, and production FastAPI deployments for financial and data-intensive applications.
Live Services
Distributed Compute Scheduler
Job queue and scheduler with priorities, resources, and preemption.
{
"workers": 4,
"queue_depth": 2,
"jobs_running": 1,
"p50_ms": 32,
"p95_ms": 120
}Monte Carlo Risk Engine
Simulates price paths and volatility shocks using stochastic models.
Contract Intelligence Engine
Extracts structured legal signals from SaaS and MSP contracts using PyTorch NLP models.
{
"docs_indexed": 128,
"entities_per_doc": 34,
"latency_ms": 87
}Analytics Engineering (SQL Warehouses)
DEX Trading Metrics Warehouse (SQL)
Star schema + governed KPIs + cohort retention + visit→swap funnel. DuckDB-first, SQL-only.
- marts.kpi_daily_trading
- marts.kpi_weekly_retention
- Daily KPIs: volume, fees, unique traders, repeat rate
- Weekly retention: cohort = first swap week
- Funnel: visited → swapped → repeat swapped
SaaS Subscription Metrics Warehouse (SQL)
MRR, churn, conversion governance + cohort retention + signup→trial→paid funnel. DuckDB-first, SQL-only.
- marts.snapshot_daily_mrr
- marts.kpi_monthly_logo_churn
- marts.kpi_monthly_retention
- Daily MRR snapshot + paying customers
- Monthly logo churn
- Monthly cohort retention: cohort = first paid month