→ A tool that uses gradient descent to calculate optimal asset weights using custom matrix algebra and covariance matrices. Designed for portfolio management use.

→ A dual-stage toolset featuring a C++ parser to read and interpret structured text data and a Python converter to transform parsed output into formatted files for seamless data automation.

→ An enterprise-style backend built in Spring Framework connected to a MySQL database and Angular front end, demonstrating object-oriented design, RESTful services, and design-pattern implementation in a stateful lab environment.

→ A command-line application that tracks, validates, and persists financial asset records using object-oriented design and file operations for reliable portfolio monitoring.

→ A Spring Boot application simulating a guitar store’s inventory with customized HTML interfaces, “Buy Now” purchase logic, max/min stock validation, sample data seeding, and comprehensive unit tests.

→ A C++ console program that conducts a political survey, trains a multiple linear regression model via gradient descent on historical CSV data, and predicts user political affiliation with file-based data persistence.

→ A fully responsive, multi-page web interface for a mock library, complete with dynamic gallery, contact form, and cart functionality powered by vanilla JavaScript and browser storage.

→ A next-gen engine for high-frequency trading: core matching and execution written in C++ for ultra-low latency, with Python bindings for rapid strategy prototyping, real-time analytics, and risk management integration.

→ An enhanced Spring-Angular application featuring multithreaded English/French resource bundles, real-time time-zone conversion (ET/MT/UTC), and multi-currency display. Containerized via Docker for seamless deployment and backed by GitLab CI/CD.

→ A lightweight Python-based backtesting engine that allows rapid development and simulation of quant trading strategies, with metrics tracking and data slicing via pandas.

→ A predictive ML model trained on financial indicators and historical prices, leveraging sklearn pipelines and time-series cross-validation to generate actionable trade signals.