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Luggias/README.md

Hey, I'm Lukas 👋

🎓 B.Sc. Computer Science @ University of St. Gallen (HSG), Switzerland
💼 Strategic Ops & Research Intern @ CMT Capital Markets Trading (Frankfurt)
📊 Vice President @ Helvetian Investment Club (one of Europe’s largest student finance societies)
🚀 Building an Algorithmic Trading Bot (Python, IBKR API)
📑 Awarded by Dr. Hans Riegel Stiftung for my thesis on Real Estate & Climate Change

🌐 Socials:

GitHub Follow

💻 Tech Stack:

Java Python NumPy Pandas Polars Spark Matplotlib Scikit--Learn PyTorch TensorFlow SQL FastAPI Docker Raspberry Pi Git LaTeX

📌 Featured Projects:

  • Hotel Revenue Forecast – Random Forest, SARIMAX, Feature Engineering (Autoregressive Lags, Rolling Window Statistics, Fourier Series Terms (K=16)
  • Algorithmic Trading Bot – Python, IBKR API, backtesting & strategy automation

📊 GitHub Stats:

Top Languages

Note: Jupyter Notebooks are not pictured due to intensive machine learning projects which are very large in byte size. JavaScript should also be hidden (but cannot) as my only repository including JavaScript is a fork for hosting these stats.


GitHub Stats
GitHub Streak

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  1. algorithmic_trading algorithmic_trading Public

    Robust, extensible backbone for systematic trading: clean data → signals → deterministic backtests → paper/live execution

    Python

  2. pv_data_scraper pv_data_scraper Public

    This project automates the retrieval, consolidation, and analysis of hourly CSV data from a photovoltaic (PV) system’s web interface. It periodically downloads performance metrics, merges them into…

    Python

  3. latex_template latex_template Public template

    This repository provides a highly optimized LaTeX environment designed for academic and technical writing. It includes custom-defined shortcuts, intelligent tab-completion snippets, linting/formatt…

    TeX

  4. DSF_Group_Project DSF_Group_Project Public

    A revenue forecasting model for a Swiss restaurant using Random Forest, SARIMAX, and Prophet to optimize weekly staffing based on weather, school holidays, and historical sales trends.

    Jupyter Notebook