金融量化研究员

This is a hands-on, end-to-end role spanning the full research and implementation lifecycle — from macroeconomic thesis formation and systematic strategy design to backtesting, deployment, and automation.


You will work closely with team members to research, develop, and deploy systematic trading strategies across multiple asset classes, including equities, gold, FX, and fixed income.


AI tools — particularly Claude — are central to how we work. You will leverage AI extensively to accelerate research, write and debug code, build LLM-powered classification and analysis tools, and improve development efficiency across the stack.
Key Responsibilities

1. Strategy Research & Development

• Lead the research, design, and backtesting of systematic trading strategies
  

• Extend, improve, and critically evaluate existing frameworks (not merely maintain them)
  

• Current research areas include:
  
• Multi-timeframe trend-following strategies
  
• Cross-asset momentum signals
  
• Long/short equity selection
  
• Pattern recognition (e.g., using vectorBT)
  
• Event-driven macro strategies around economic releases (PMI, CPI, NFP)
  

• Challenge assumptions, refine signal construction, and enhance robustness through statistical validation and stress testing
  

2. Trading Infrastructure & Automation

• Design, build, and maintain production-grade systems that convert research into executable trades
  

• Develop automated position sizing, signal pipelines, and order generation workflows (Interactive Brokers / IBKR)
  

• Manage Bloomberg data ingestion pipelines and multi-strategy portfolio consolidation
  

• Ensure robustness, reliability, and scalability of research and execution infrastructure
  

3. Macroeconomic & Thematic Research

• Produce structured, thesis-driven investment research connecting macroeconomic fundamentals to actionable portfolio positioning
  

• Develop scenario-based strategies grounded in:
  
• Monetary policy transmission
  
• Yield curve dynamics
  
• Inflation mechanisms
  
• Fiscal sustainability
  
• Cross-country yield differentials
  

• Research themes examples:
  
• Comparing counter-cyclical yield dynamics (Volcker era vs. current Fed easing cycle)
  
• Equity price behavior around FOMC decisions ("buy the rumour, sell the news" testing)
  
• FX volatility regime analysis to assess USD trajectory and equity implications
  

• This role requires synthesizing macro data into clear positioning recommendations — not just summarizing economic statistics
  

4. Shared Responsibilities

• Portfolio and asset-level risk monitoring (including CVaR reporting)
  

• Economic dashboard development
  

• Data integrity validation and monitoring
  

• Server pipeline management and infrastructure reliability
  

Requirements

Must-Have

• Bachelor's degree or above in Computer Science, Data Science, Computational Finance, Financial Engineering, Statistics, Actuarial Science, Economics, Physics, Mathematics, Engineering, or other quantitative disciplines (Candidates from non-CS backgrounds must demonstrate strong programming proficiency)
  

• 1+ years of experience in quantitative research, macro research, or quantitative development within asset management, proprietary trading, or hedge funds — or equivalent depth demonstrated through independent research or systematic trading projects
  

• Strong Python programming skills for:
  
• Backtesting frameworks
  
• Data pipelines
  
• Automation systems
  
• Statistical analysis
  

• Solid understanding of strategy performance metrics (PnL, Sharpe ratio, drawdowns, rolling risk metrics)
  

• Working knowledge of multi-asset financial markets (equities, FX, commodities, fixed income)
  

• Strong macroeconomic literacy
  

• Experience with broker APIs or execution automation (IBKR TWS preferred)
  

• Experience using AI tools (Claude, Copilot, etc.) for code generation, debugging, or building AI-powered tools
  
• You should know how to effectively prompt, validate, and direct AI output
  

• Ability to produce structured, forward-looking, thesis-driven written investment research
  

• Comfort with statistical methods: regression modelling, correlation analysis, z-score transformations, and rolling risk analytics
  

Good to Have

• Bloomberg Terminal experience (including BQL data extraction)
  

• Experience integrating LLM-based tools for research automation or signal generation
  

• Long/short equity strategy experience (factor-based selection, benchmark hedging)
  

• Event-driven trading exposure (FOMC, NFP, ISM, CPI)
  

• Familiarity with TradingView Pine Script
  

• Data integrity monitoring and pipeline reliability management
  

• Experience with Linux server management, crontab scheduling, and web dashboard development (HTML/JS)
  

Technology Stack

• Python
  
• Bloomberg Terminal
  
• Interactive Brokers API
  
• TradingView (Pine Script)
  
• Claude / LLM tools
  
• Google Sheets / Docs
  
• Linux servers
  
• Crontab
  
• HTML / JavaScript dashboards