Microstructure Quantitative Researcher

point72 · New York

Posted about 1 month ago

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Skills

PythonRC/C++Market microstructureFeature engineeringStatistical modelingMachine learningBacktestingOrder book dataFuturesFXScikit-learnPandas

Benefits

  • ✓Cutting-edge research infrastructure
  • ✓Clean datasets
  • ✓Proprietary trading infrastructure

Job description

About the Team:

A well-established quantitative portfolio management team at Point72 is looking for an experienced quantitative professional to develop and trade systematic macro strategies, with a focus on market microstructure. The candidate will be given the resources and support to drive the build out and expansion of the quantitative macro business.

Role/Responsibilities:

  • Perform rigorous and innovative research to develop systematic signals for global macro (futures, FX, etc.) markets, with a focus on market microstructure signals
  • Perform feature engineering with order book tick data at intraday to daily horizons
  • Perform feature combination using various modeling techniques ranging from linear to machine learning models
  • Participate in the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation
  • Help drive the growth of the investment process and research capabilities of the team
  • Work in a team of highly qualified and motivated individuals with access to a cutting-edge research and trading infrastructure and clean datasets
  • Assist in building, maintenance, and continual improvement of production and trading environments

Requirements:

  • MS or PhD in physics, engineering, statistics, applied math, quantitative finance, or other quantitative fields with a strong foundation in statistics
  • 4+ years of experience in quantitative research, building statistical models for intraday to daily trading, as part of a successful proprietary trading team with a track record
  • Knowledge of market microstructure for futures and/or FX
  • Prior experience with tick data based feature generation, modelling, and monetization
  • Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas
  • Collaborative mindset with strong independent research abilities
  • Commitment to the highest ethical standards

 

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