Quant Developer | Derivatives Pricing | HFT Systems | Statistical Arbitrage

Software engineer transitioning into quantitative finance, with experience in trading systems, order matching engines, market-data workflows, distributed financial services, and performance-sensitive backend systems. Currently focused on derivatives pricing, stochastic processes, numerical methods, market microstructure, and systematic trading.

amirphl4@gmail.com · LinkedIn: amir-pirhosseinloo · GitHub: amirphl

Quant Developer · Quant Research Engineer · Derivatives Pricing Engineer · HFT / Low-Latency Trading Engineer · Statistical Arbitrage Developer

Languages: C++, Go, Python; learning Rust Trading: Order books, matching engines, backtesting, market data, arbitrage, risk controls Quantitative: Probability, stochastic calculus, numerical methods, PDEs, option pricing, market microstructure Systems: Algorithms, concurrency, profiling, low-latency design, distributed systems Infrastructure: PostgreSQL, Kafka, Redis, Docker, Kubernetes, Linux, WebSocket, gRPC

Structured study focused on derivatives pricing and quantitative trading:

• Probability, martingales, Brownian motion, and stochastic calculus
• Risk-neutral pricing and Black–Scholes
• Numerical linear algebra and numerical pricing methods
• PDE-based derivative pricing
• Market microstructure and order-book dynamics
• Statistical arbitrage research

Core references include Williams, Shreve, Hull, Trefethen & Bau, Evans, Durrett, and Wilmott.

Go · Exchange Infrastructure · Order Books

Order matching engine inspired by cryptocurrency exchange infrastructure.

• Implemented market and limit order matching and order lifecycle handling.
• Added journaling for consistency and recovery.
• Designed for deterministic execution and future performance optimization.

GitHub: github.com/amirphl/matching-engine

C++ · Systematic Trading · Arbitrage

High-performance cryptocurrency trading framework.

• Built components for strategy execution, arbitrage detection, market interaction, and order processing.
• Focused on throughput, scalability, and performance-sensitive C++ architecture.

GitHub: github.com/amirphl/cipherTrader

Go · Backtesting · Live Trading

Algorithmic trading framework supporting historical and real-time cryptocurrency markets.

• Implemented exchange integration, candle management, backtesting, and live execution.
• Added SMA, EMA, RSI, signal generation, and risk-management logic.

GitHub: github.com/amirphl/simple-trader

May 2024 – Apr 2025

Fintech engineering across financial transactions, investment workflows, and market infrastructure.

• Developed critical microservices for real-estate micro-investment transactions.
• Built market-data analysis and transaction-processing components.
• Implemented and contributed to order matching engine functionality.
• Designed Kafka workflows with attention to idempotency and consistency.
• Increased Go microservice test coverage from 10% to 63%.
• Mentored two backend engineers.

Stack: Go, C++, Kafka, Kubernetes, PostgreSQL

Dec 2023 – Apr 2024

• Automated invoice generation and financial reporting.
• Replaced manual Excel-based workflows with integrated software processes.

Feb 2022 – Feb 2023

Founded a blockchain-based game startup and developed its product logic, Python backend, and blockchain components. Completed MIT's Blockchain and Digital Currency course.

Apr 2020 – Jan 2022

Performance and infrastructure engineering for a large-scale video conferencing platform.

• Scaled concurrent capacity from 100 to 4,000 users.
• Reduced SQL query latency by more than 90%.
• Improved p99 response time to under 2 seconds.
• Worked on Kubernetes-based production infrastructure and performance optimization.

Sep 2019 – Mar 2020

Developed Django-based internal systems and worked on Docker deployment and GitLab CI workflows.

Jul 2017 – Sep 2017

Worked with Hadoop, HBase, Kafka, and Spark on distributed search and large-scale data-processing infrastructure.

Shahid Beheshti University · 2022–2025

Relevant study: Computer Systems Performance, Distributed Systems, Advanced Networks, Blockchain.

Amirkabir University of Technology · 2016–2020

Graduated with a perfect GPA. Thesis: Distributed Task Execution on Mobile Edge Network.

Relevant study: Machine Learning, Deep Learning, Linear Algebra, Algorithms.

• CUDA/C++ voice similarity recognition
• Signal convolution simulator
• Graph clustering and network analysis
• Continuous C++ and systems-programming projects

GitHub: github.com/amirphl LinkedIn: linkedin.com/in/amir-pirhosseinloo