Projects / Stack

Data visualization and algorithmic trading performance metrics on a terminal interface.

Technical Ecosystem & Architecture Stack

My engineering approach is performance-obsessed and strictly production-ready. I design systems for high availability (HA), fault tolerance, and mathematical precision, ensuring 24/7 execution without memory leaks or silent failures.

1. Algorithmic Trading & Financial Engineering

Infrastructure designed for autonomous trading, quantitative analysis, and rigorous risk management.

  • Core Logic: Python (Advanced, AsyncIO, Multi-threading).
  • Execution Engine: Interactive Brokers (IBKR) API and ib_insync for asynchronous, high-availability order routing.
  • Quantitative Analysis: Vectorized operations using Pandas and NumPy to eliminate loop bottlenecks during backtesting.
  • Data Engineering: TimescaleDB and PostgreSQL, specifically optimized for high-cardinality tick-data ingestion and rapid querying.

2. High-Performance Backend & Mission-Critical Systems

Low-level optimization for industrial and enterprise operations running 24/7.

  • Compiled Languages: C++, C#.
  • System Design: Event-Driven Architecture, Stateless Design, and Microservices separation of concerns.
  • Key Applications: Real-time 2D/3D industrial vision processing and complex logistics routing optimization (slashing database I/O bottlenecks).

3. Infrastructure, DevOps & Local AI

Deploying resilient automation and bridging the gap between local hardware acceleration and cloud scalability.

  • Resilience & Deployment: Docker containerization paired with automated "Watchdog" recovery systems to survive network outages.
  • Cloud & CI/CD: AWS, GitHub Actions for automated testing pipelines, and Webhook/API integration troubleshooting (Nginx, RESTful APIs).
  • Hardware-Accelerated AI: Local machine learning pipelines leveraging CUDA and GPU Inference (NVIDIA architectures) for unstructured data analysis and LLM agent integration.