Taiwan-based | Open to relocation to the UK

Quantitative Trading Systems Developer focused on building event-driven trading infrastructure and data-driven execution systems.

• Design and implement event-driven trading systems for real-time signal execution
• Transform market data into systematic trading strategies
• Build end-to-end pipelines from data ingestion → signal generation → execution
• Conduct strategy research & backtesting with performance evaluation

Trading & Quant • Strategy design (rule-based / signal-driven) • Backtesting (multi-year data simulation) • Risk metrics: Sharpe Ratio, Max Drawdown, Win Rate

Systems Engineering • Event-driven architecture for low-latency decision making
• Exchange API integration (Binance, KuCoin, Gate.io)
• Order execution & position management logic

Data & Infrastructure • Data pipelines for market data processing
• Feature engineering for time-series signals
• Dockerized deployment environments

• Built a fully automated trading system integrating exchange APIs
• Designed signal-to-execution pipeline with position management
• Implemented modular architecture for scalability

👉 Focus: execution layer + system architecture

• Developed multi-year historical simulation framework
• Evaluated strategies using Sharpe Ratio & drawdown metrics
• Enabled parameter optimization and strategy comparison

👉 Focus: research infrastructure + performance evaluation

• Analyzed financial statements to extract growth and margin trends
• Built visualization pipelines for business insights

👉 Focus: bridging financial understanding with data

Looking to transition into:

• Quant Trading / Trading Systems Engineering
• Data Engineering (financial markets)
• Market Infrastructure / FinTech

Particularly interested in roles where data, execution, and systems intersect.

I focus on building real trading systems with execution logic, not just theoretical models.

This work has been explored through both backtesting and small-scale live trading using personal capital, with a focus on execution behavior and risk management rather than profit optimization.

Tested trading systems through both backtesting and small-scale live trading using personal capital.

Focused on understanding real-world execution behavior, including:

• Latency and order execution dynamics
• Slippage under varying market conditions
• Real-time risk and drawdown control

These insights were used to improve system design and align backtesting assumptions with live execution reality.