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amandahtay/README.md

Hi, I'm Amanda 👋

I build quantitative models and data systems for financial markets.

Most of my work is hands-on: data pipelines, transformation workflows, and analytics infrastructure designed for scalability, reproducibility, and performance.

I focus on clean architecture, efficient processing, and production-ready systems that support quantitative research and financial decision-making.

Open to: early design partners in accounting/finance, angel investors interested in EU fintech infrastructure, and engineering collaborators

What I work on

  • 📦 Quantitative Data Pipelines: Market data ingestion → feature engineering → model-ready datasets (options Greeks, volatility surfaces, microstructure features)
  • 📈 Financial ML: Supervised learning for trading signals, meta-labeling frameworks, time-series cross-validation, walk-forward analysis
  • ☁️ Production Systems: Dockerized backtesting environments, automated model retraining, CI/CD for quant research
  • ⚡ High-Performance Computing: Vectorized pandas operations, parallel processing for large-scale simulations, computational optimization

About Me

  • 💼 Experience across quantitative research, capital markets, and trading system environments
  • 🎓 MSc Financial Engineering
  • 📐 Mathematics studies
  • 🌍 Based in Germany

I enjoy turning financial theory into clean, testable, and production-ready systems.

Currently Building

  • Options Trading ML System: Implementing Lopez de Prado's triple-barrier labeling with meta-learning for position sizing
  • Time-Series Volatility Modeling: GARCH models for implied volatility forecasting

Technical Stack

  • Languages: Python, SQL, Bash
  • ML/Data: XGBoost, scikit-learn, PyTorch, pandas, numpy, scipy
  • Quant Finance: Black-Scholes pricing, Greeks calculation, options analytics, risk modeling
  • Data Engineering: Apache Airflow, SQL databases, data validation pipelines
  • DevOps: Docker, Git, AWS/Azure, CI/CD
  • Research: Jupyter, matplotlib, seaborn, SHAP, statistical analysis
  • LLMs: Mistral, LiteLLM, prompt engineering, RAG pipelines
  • Document AI: Surya OCR, Docling, pdfplumber, document extraction
  • Agent Frameworks: OpenClaw / OpenHands skills development

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