San Diego Metropolitan Area
Quantitative researcher with experience in market microstructure, alpha research, execution analysis, and machine-learning-based return forecasting. Built event-driven simulators, transaction-cost-aware backtests, and portfolio optimization tools across crypto and equity markets. Strong background in Python, C++, SQL, statistical modeling, and time-series analysis.
• Developed an agentic AI chatbot for Dhauz, enhancing clinical support for nurses and technicians. • Presented the project at the UCSD Rady School of Management TRACE Awards, achieving 3rd place. • Designed a multi-agent architecture integrating patient alert logic and contextual information retrieval. • Established evaluation workflows to assess chatbot outputs for relevance and accuracy in clinical scenarios.
• GitHub Repo: github.com/zacharylibret/Performance_Stock_Units_Valuation_BS_Lattice_Montcarlo • Modeled a 140% stock-price hurdle over 3 years. • Used market data, volatility blend, Treasury rates, and dividend yield. • Ran 200,000-path Monte Carlo to estimate hurdle probability. • Primary model: CRR lattice with barrier inside the tree. • Cross-checked with Black-Scholes and integrated Monte Carlo.
• Built an automated Python scraping pipeline for earnings call transcripts, reducing turnaround time by 10%. • Supported investment signal validation by integrating research outputs into internal workflows. • Assisted with data organization and reporting pipelines using modern analytics tools (Databricks/Snowflake).
• GitHub Repo: github.com/zacharylibret/Factor_VAE • Developed a GRU-based Variational Autoencoder (VAE) to model cross-sectional equity return patterns. • Built factor-driven portfolio workflows supporting $0.1M internal capital with 7% growth. • Designed research ETL pipelines in Python/SQL to clean, transform, and validate multi-source market datasets.