Quantitative Trading Strategy Algorithm Engineer

Responsibilities

• Factor Mining & Validation: Discover, construct, and validate trading factors from multi-source data including market data, fundamental data, and on-chain data. Continuously iterate the factor library to identify effective alpha signals.
• Factor Prediction Modeling: Design and optimize prediction models using machine learning and deep learning methods to improve signal accuracy and stability while controlling overfitting and strategy decay.
• Strategy Design & Backtesting: Lead the design, backtesting, and live deployment validation of trading strategies — covering signal generation, portfolio construction, risk control, and execution optimization. Take ownership of strategy P&L and risk performance.
• Quant Strategy Pipeline Development: Build and refine the end-to-end quantitative trading strategy pipeline — from data ingestion, factor computation, model prediction, backtesting through to live execution — improving research efficiency, deployability, and reproducibility.
• Trading System Integration: Collaborate with engineering and data teams to solve technical challenges including data connectivity, low-latency execution, and strategy deployment, ensuring stable strategy operation in production.
• Cross-Market AI Trading: Explore the adaptation and implementation of AI-driven trading across both traditional financial markets (equities, futures) and on-chain asset markets, leveraging the unique characteristics of each.

Requirements

• Master's degree or above in Computer Science, Mathematics, Statistics, Financial Engineering, Physics, or related fields, with a solid quantitative foundation and programming proficiency.
• Proven experience in quantitative trading strategy R&D, familiar with the full workflow of factor mining, factor prediction, strategy backtesting, and live deployment. Deep understanding of strategy P&L, risk, and alpha decay.
• Proficient in Python, with hands-on experience applying ML/DL methods in quantitative scenarios and processing large-scale financial time-series data.
• Familiarity with trading mechanisms and data characteristics of at least one market (equities, futures, or other traditional financial markets; or cryptocurrency / on-chain assets). Understanding of real-world factors such as trading costs, liquidity, and execution slippage.
• Experience building a complete strategy pipeline or quantitative research platform, with the ability to independently deliver an end-to-end strategy loop from data to live trading.
• Strong research capability and results-driven mindset, with the ability to continuously optimize strategy performance in a fast-iteration environment.

Bonus Qualifications