QuantOs - Quantitative ML Platform
QuantOS — Quantitative ML Orchestration & Deployment Platform
Visit the Website - https://quant-os.duckdns.org/
The Challenge
Quant teams build dozens of models but have no standardized way to track, validate, and compare them. Experiments live in scattered notebooks, validation is manual, and bad models slip into production. How do you build a system that catches failures before they cost real money?
The Solution
I built a production-grade MLOps platform purpose-built for quantitative finance — with a model registry, experiment tracking, and a 5-tier automated validation framework that speaks the language of quant research: IC, Sharpe, turnover, factor exposure.
Key Achievements
5-tier automated validation covering coverage, IC, turnover, factor exposure, and transaction costs
Automated pass/fail reporting — no model reaches production without clearing every gate
Full-stack infrastructure deployed via Docker Compose with zero manual setup
Real-time monitoring dashboard tracking model performance across all validation tiers
How It Works
The platform enforces a strict validation pipeline before any model goes live:
Coverage Checks — Ensures the model has sufficient data coverage across target securities and sectors before proceeding
Information Coefficient Analysis — Measures the predictive power of alpha signals against forward returns, flagging weak or decaying signals
Turnover & Transaction Cost Simulation — Estimates real-world trading costs to verify that alpha survives after implementation friction
Factor Exposure Analysis — Decomposes model output against known risk factors to ensure alpha isn't just disguised beta
Automated Reporting — Generates pass/fail verdicts with full diagnostics, sent via email alerts to the research team
Technologies Used
Infrastructure: Docker Compose, PostgreSQL + TimescaleDB, Redis, MinIO, MLflow, Grafana
Backend: Python, FastAPI
Monitoring: Streamlit dashboard, Grafana metrics
CI/CD: GitHub Actions
Quant Metrics: IC, Sharpe ratio, turnover, factor exposure, transaction cost modeling
Results
Metric | Before QuantOS | With QuantOS |
|---|---|---|
Validation | Manual, ad-hoc | Automated 5-tier pipeline |
Model Comparison | Scattered notebooks | Centralized registry with versioning |
Time to Validate | Hours per model | Minutes with automated reporting |
Bad Model Detection | Post-deployment | Pre-deployment gate |
Impact
In quant finance, a single corrupted model can contaminate live regressions and cost real capital. QuantOS ensures every model is rigorously stress-tested against quant-specific criteria before it touches production — turning validation from an afterthought into an automated first-class process.
See It In Action
