Job Description
7 days ago
We’re partnering with a top-tier quant fund to find a skilled quantitative researcher with strong coding expertise to join their team. The team focuses on mid-frequency statistical arbitrage and event-driven equity strategies.
Core Responsibilities
• Design, develop, and enhance robust pipelines for the full strategy lifecycle: real-time event detection, data processing, alpha signal creation, historical simulation, risk-constrained portfolio optimization, trade order generation, and post-trade reconciliation (P&L / positions).
• Ensure precise and consistent handling of corporate events (e.g., stock splits, dividends, mergers, spin-offs, delistings) across raw datasets, adjusted price series, backtest environments, and live portfolio management.
• Build and refine portfolio optimization engines that incorporate practical trading constraints such as position limits, borrow availability, transaction costs, event-related risks, and exposure/beta targets.
• Implement automated, reproducible processes with strong emphasis on code versioning, unit/integration testing, scheduling/orchestration, production monitoring, alerting, and rapid incident resolution.
• Collaborate intensively with Portfolio Managers to translate trading ideas, refine risk overlays, incorporate new event signals, and deploy enhancements into reliable, production-quality code.
Key Qualifications
• 2–5 years of relevant experience as a Quantitative Researcher with hands-on coding experience within a hedge fund, proprietary trading desk, or similar high-performance environment.
• Proven track record of building and maintaining end-to-end systems, including data ingestion pipelines, backtesting frameworks, portfolio construction logic, corporate event adjustments, and live deployment workflows.
• Strong programming proficiency in Python, combined with practical experience using databases (e.g., PostgreSQL), cloud infrastructure (e.g., AWS), version control (Git), and workflow orchestration tools.
• Deep knowledge of mid-frequency equity trading strategies; familiarity with index rebalancing events or corporate actions is highly desirable.
Core Responsibilities
• Design, develop, and enhance robust pipelines for the full strategy lifecycle: real-time event detection, data processing, alpha signal creation, historical simulation, risk-constrained portfolio optimization, trade order generation, and post-trade reconciliation (P&L / positions).
• Ensure precise and consistent handling of corporate events (e.g., stock splits, dividends, mergers, spin-offs, delistings) across raw datasets, adjusted price series, backtest environments, and live portfolio management.
• Build and refine portfolio optimization engines that incorporate practical trading constraints such as position limits, borrow availability, transaction costs, event-related risks, and exposure/beta targets.
• Implement automated, reproducible processes with strong emphasis on code versioning, unit/integration testing, scheduling/orchestration, production monitoring, alerting, and rapid incident resolution.
• Collaborate intensively with Portfolio Managers to translate trading ideas, refine risk overlays, incorporate new event signals, and deploy enhancements into reliable, production-quality code.
Key Qualifications
• 2–5 years of relevant experience as a Quantitative Researcher with hands-on coding experience within a hedge fund, proprietary trading desk, or similar high-performance environment.
• Proven track record of building and maintaining end-to-end systems, including data ingestion pipelines, backtesting frameworks, portfolio construction logic, corporate event adjustments, and live deployment workflows.
• Strong programming proficiency in Python, combined with practical experience using databases (e.g., PostgreSQL), cloud infrastructure (e.g., AWS), version control (Git), and workflow orchestration tools.
• Deep knowledge of mid-frequency equity trading strategies; familiarity with index rebalancing events or corporate actions is highly desirable.
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