About Me
Quantitative Developer & Software Engineer (ML/AI)
I'm a quantitative developer and software engineer focused on building production-grade systems for financial markets and data-intensive products. My work covers time-series modeling, backtesting, and signal research—shipped as robust services and tools.
On the engineering side, I design and implement clean APIs, data pipelines, and services with strong testing, observability, and CI/CD. I've built order-book/OMS simulations, volatility forecasting, and ML-filtered strategies end-to-end—from data ingestion to deployment.
Stack highlights: Python (pandas, NumPy, scikit-learn, PyTorch), FastAPI/Node, Postgres/Redis, Docker, GitHub Actions, and React when a UI is needed. I care about correctness, performance, and maintainable code.
Technical Skills
Quant & ML
Time-Series: feature engineering, temporal CV, labeling
Models: scikit-learn, PyTorch, GARCH/ARCH, Random Forest
Metrics: Sharpe, max drawdown, CAGR, turnover
Software Engineering
APIs & Services: FastAPI, Node/Express, WebSockets
Architecture: modular design, OOP, clean interfaces
Quality: pytest, type hints, CI/CD (GitHub Actions), Docker
Data Platforms
Sources: yfinance, Bitquery, Google Sheets API
Storage: Postgres, Redis, SQLite
Pipelines: ETL, caching, batch & streaming
Applied AI
Agents: LangGraph workflows, structured extraction
Integrations: external APIs, Google services
Products: analytics dashboards, automation tools
