Nicolas D.

Quant Scientist with over 5 years of experience in financial modeling, algorithmic trading, and machine learning. I specialize in building and deploying data-driven systems for forecasting, strategy development, and risk management — with deep expertise in time-series modeling and Python-based tooling. Currently, I work at a Global Macro Hedge Fund where I design and implement systematic strategies across asset classes, build portfolio optimization tools, and develop quantitative risk frameworks. Outside of work, I actively research and trade in equities and digital assets, with hands-on experience developing DeFi strategies (arbitrage, trend-following, yield optimization) since 2021. What I Can Help You With? - Development and backtesting of trading strategies (equities, crypto, futures) - Predictive modeling & machine learning (scikit-learn, TensorFlow) - Time-series analysis and signal engineering - Custom data pipelines: APIs, scraping, structured storage - Interactive dashboards and analytics (Dash, Plotly) Tech Stack - Languages & Libraries: Python, NumPy, Pandas, Scikit-learn, XGBoost, TensorFlow, Statsmodels, Git - Backtesting & Strategy Simulation: Backtrader, VectorBT, QuantConnect (LEAN) - Risk & Portfolio Analysis: Pyfolio, Riskfolio-Lib, Empyrical, cvxpy, Quantlib - Forecasting: Scikit-learn, XGBoost, TensorFlow, Darts, TSFresh - Data & Automation: REST APIs, Web Scraping (Selenium, BeautifulSoup), SQL, ArcticDB - Visualization & Dashboards: Plotly, Dash, Seaborn - API Integration: Interactive Brokers API (ib_insync), CCXT (CEXs), Web3.py, The Graph (on-chain data), 0x API - Infrastructure & Workflow (Optional): Docker, Airflow, AWS (EC2, S3), Git - Options: understanding of theory, including Greeks, implied volatility, and surface construction