Emre E.

Senior Automation Engineer & Quantitative Programmer (PhD) | Python Expert Institutional-Grade Python Systems & Automation I am a PhD-level engineer with years of experience developing high-stakes technology for multiple multi-billion dollar hedge funds and a National Laboratory. I specialize in building robust, production-ready Python pipelines and automation tools, applying institutional-level rigor to every programming project. Broad Engineering Expertise Applied to Programming I leverage a deep background in Electrical Engineering, Computer Science, and Operations Research to build software that is significantly more efficient and resilient than standard scripts. My programming and automation projects are informed by: Queueing Theory & Flow Optimization: Designing data pipelines and processing engines built to handle high-volume throughput without bottlenecks. Systems & Industrial Engineering: Applying professional experience in process improvement and project management to ensure code is clean, maintainable, and highly efficient. Quantitative Research: Extensive experience developing and running live systematic trading models for futures, equities, options, and high-fidelity backtesting engines. National Lab-Grade Reliability: Drawing on my experience at a National Laboratory to build self-healing automation that connects disparate APIs, databases, and proprietary platforms. Core Project Examples My work focuses on transforming complex, manual, or fragmented workflows into centralized Python applications. Recent projects include: Institutional Backtesting Engines: Developed high-fidelity Python frameworks for futures and equities, ensuring zero look-ahead bias and realistic slippage modeling (e.g., FuturesBacktestingEngine). Automated Data Pipelines: Engineered end-to-end systems for data ingestion, cleaning, and statistical feature engineering for large-scale financial datasets (e.g., QuantDataPipeline). Portfolio Analytics & Risk Systems: Built modular tools to calculate real-time institutional metrics, including Sharpe, Sortino, Calmar ratios, and drawdown analytics (e.g., PortfolioAnalytics). Scientific Computing & Simulation: Building tools for complex modeling, data mining, and research automation including factory simulations. What I Build I specialize in Legacy System Modernization—refactoring fragmented scripts and manual processes into scalable, production-grade Python systems—and Workflow Automation, creating robust ecosystems that integrate multiple platforms and APIs into a single, automated process. I have documented my core frameworks—including my Futures Backtesting Engine, Quant Data Pipeline, and Portfolio Analytics—in the portfolio section below. I am happy to provide direct GitHub access upon request.