Welcome to My Portfolio
Head of Quants | AI & ML Engineer | MSc AI (Merit) | HFT & SaaS Builder
Building intelligent systems at the intersection of AI Engineering, Quantitative Finance & Full-Stack Development
Full-Stack Engineer specialising in Python, TypeScript, React, and production-grade trading systems
About Me
Pak Ming Cheung is Head of Quants at Quant Sigma Limited (London), an AI & ML Engineer, and a Full-Stack Developer with an MSc in AI (Merit) from the University of Wolverhampton. With 10+ years leading quantitative trading across Forex, commodities, indices, CFDs, and cryptocurrency markets, I bridge the gap between cutting-edge AI engineering and financial technology.
Currently leading AI-powered HFT systems and building production-grade applications with C++, React, TypeScript, and Python. My track record includes ~100% annualised returns, scaling trading capital by 20x+, achieving Sharpe ratios of 1.5+ (Forex/CFD) to 2-6 (crypto), and reducing drawdowns by 40% via proprietary volatility models. An early Bitcoin adopter since $80 in 2013, I bring deep domain expertise in both traditional finance and DeFi/Web3.
My recent work spans multi-signal HFT models (Python, PyTorch, C++), transformer-based predictive models, distributed ML inference pipelines (<100ms latency), NLP sentiment analysis processing 500K+ articles daily, RAG systems with LLM integration, and full-stack engineering with modern frameworks. I specialise in building systems where machine learning meets financial markets.
Core Competencies
AI & ML Engineering
LLMs, LSTM, NLP, Transformers, Predictive Modelling, OpenRouter
Full-Stack Development
React, Next.js, TypeScript, Node.js, Python, REST APIs
Quantitative Trading
Forex, Crypto, Futures, Commodities, Indices, CFDs
Algorithmic & HFT Systems
HFT, MFT, Scalping, Arbitrage, FIX API, Equinix LD4
Data Science & Analytics
Statistical Modelling, Time Series, Sentiment Analysis
Blockchain, DeFi & Web3
DeFi Protocols, NFTs, Web3, CEX/DEX, Yield Farming
Ask About Pak
Have questions about my skills, experience, or background? Ask the AI assistant below.
Professional Experience
Responsibilities:
- Lead quantitative research and strategic development focused on transitioning from MFT to advanced HFT
- Architect low-latency execution infrastructure with millisecond performance and colocation at Equinix LD4
- Integrate advanced trading platforms with FIX and iLink APIs and manage real-time market data feeds
- Conduct extensive backtesting using Python-based automation and AI-driven insights
- Maintain rigorous risk frameworks, actively managing metrics such as maximum drawdown and Sharpe ratios
- Developed multi-signal HFT models using Python, PyTorch, and C++, implementing transformer-based predictive models and reinforcement learning execution systems
- Built distributed ML inference pipelines (<100ms latency), NLP sentiment analysis engines processing 500K+ articles daily, and automated feature engineering processing 100+ technical indicators
- Built AI-assisted decision workflows using LLMs with RAG for trading research and market intelligence
Key Achievements:
- Consistently achieving near 20% monthly ROI, translating to an annualized return exceeding 200%
- Successfully led the transition from MFT to HFT trading strategies
- Implemented sophisticated backtesting and simulation environments for robust algorithm deployment
- Developed proprietary volatility models that reduced drawdowns by 40%
- Created weekly investment-grade quantitative performance reports for stakeholders
Skills & Technologies:
Responsibilities:
- Designed, developed, and implemented quantitative trading strategies for Forex and cryptocurrency markets
- Developed comprehensive understanding of cryptocurrency, blockchain technologies, and NFTs
- Leveraged decentralized exchanges and DeFi protocols to enhance portfolio yield
- Applied sophisticated risk management techniques using Python and other analytical tools
- Conducted extensive backtesting using MT4/5 and Python-based automation
- Authored professional market and data analysis reports for family office investments
Key Achievements:
- Achieved Sharpe ratio of 1.5+ in Forex and CFD trading and between 2-6 in the crypto space
- Generated over 20x in profit through strategic trades and arbitrage
- Successfully navigated centralized platforms and Web3-based platforms
- Built strong relationships with blockchain technology providers and exchanges
- Pioneered NFT-focused trading strategies with consistent profitability
Skills & Technologies:
Responsibilities:
- Led strategic account management & coordinated with international merchants for brand positioning
- Developed and maintained project plans, including cost estimation and resource allocation
- Oversaw inventory management, supply chain coordination, and pricing structure
- Conducted comprehensive market analysis to support business operations
- Streamlined operations by managing product sourcing and inventory forecasting
Key Achievements:
- Successfully managed relationships with key international merchants
- Optimized pricing structures to support operational profitability
- Improved inventory management and supply chain coordination
- Increased product line revenue by 35% through strategic pricing and positioning
- Reduced operational costs by 20% through process optimization
Skills & Technologies:
Responsibilities:
- Oversaw strategic and operational aspects within the telecommunications software sector
- Led initiatives to enhance operational efficiency and support business growth
- Cultivated and maintained strong relationships with key clients in various sectors
- Directed a multi-disciplinary team, implementing development programs
- Managed the entire project lifecycle for custom software solutions
- Improved operational workflow and service quality by standardizing procedures
Key Achievements:
- Achieved a 25% annual growth rate through strategic leadership
- Successfully renewed contracts and ensured client satisfaction
- Improved operational workflow and service quality by standardizing procedures
- Reduced customer churn by 30% through improved service delivery
- Implemented new reporting systems that increased operational visibility
Skills & Technologies:
Skills & Expertise
Programming & Frameworks
AI & Machine Learning
Trading & Finance
HFT & Execution
Backend & Databases
Tools & Infrastructure
Technical & Quant Skills
Blockchain & Crypto
Education & Certifications
Education
AI & Data Science for Quantitative Finance
University of Wolverhampton
Visit WebsiteGraduated with Merit. Research in LSTM neural networks for financial forecasting, NLP sentiment analysis, and machine learning applications in trading systems.
Marketing
The Hong Kong University of Science & Technology
Visit WebsiteFoundation in business principles and marketing strategies.
Hong Kong Education Bureau
Visit WebsiteHong Kong Education Bureau
Visit WebsiteCertifications & Qualifications
The Securities and Futures Commission of Hong Kong
Visit WebsitePassed Papers 1, 7, 8, 12
Insurance Authority of Hong Kong
Visit WebsiteQualified in Papers I, III, & IV
Harvard University
Visit WebsiteIntroduction to Computer Science
Amazon Web Services
Visit WebsiteAmazon Web Services
Visit WebsiteKey Achievements
Proven Sharpe ratios: 1.5+ in Forex and CFDs, 2-6 in cryptocurrency markets with maximum drawdowns consistently maintained below 5–10% for traditional markets and below 15% for cryptocurrencies.
Early crypto pioneer - Entered Bitcoin at US$80 in 2013, leveraging first-mover insights for altcoins, NFT markets, and DeFi opportunities.
HFT Transition Leader – Spearheading Quant Sigma's shift from MFT to HFT, integrating low-latency trading infrastructure, FIX API execution, and ultra-fast data processing.
AI & LLM-Powered Trading – Utilizes LLMs (ChatGPT Pro, Mistral, DeepSeek, Claude, Qwen, Gemini, Perplexity AI) and AI-driven analytics for predictive modeling and strategy optimization.
Frequently Asked Questions
Common questions about my experience and expertise
Resources & Publications
A research paper exploring the implementation and effectiveness of algorithmic trading strategies in cryptocurrency markets. This study examines various technical indicators, market microstructure, and execution algorithms.
A comprehensive study on applying various machine learning techniques to predict cryptocurrency market movements. This research examines the effectiveness of different ML models in capturing market patterns and generating actionable trading signals.
An in-depth analysis of multi-frequency trading strategies and their application in modern financial markets, focusing on the integration of different timeframes and market dynamics.
A detailed examination of trade execution challenges in DeFi markets and proposed solutions, including gas optimization, MEV protection, and cross-chain bridging strategies.
Contact Me
pakming20@gmail.com
Phone
07920800830
Location
Tilehurst, Reading, UK