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Trexquant

Quantitative trading via ML models

Quantitative Researcher - Futures

Quantitative ResearchUpdated on Nov 12
Full-time

About the Job

Requirements

  • A BS, MS, or PhD degree in a STEM field is required.
  • At least 2 years of experience in quantitative research is required; experience within futures markets is preferred.
  • A passion for machine learning is required.
  • Proficiency in programming languages such as Python and in statistical modeling is required.
  • Strong problem-solving skills are required.
  • The ability to work effectively independently and as part of a team is required.

Responsibilities

  • Design, implement, and optimize trading strategies to predict futures market trends using extensive financial data and a wide array of trading signals.
  • Parse and analyze large datasets to identify actionable alpha signals and develop strategies for futures trading.
  • Explore and apply academic research in quantitative finance to assess, refine, and enhance the profitability of trading strategies.
  • Improve existing models by integrating new data sources and advanced techniques to boost performance and scalability.
  • Collaborate with quantitative researchers to conduct experiments, backtest hypotheses, and refine strategies through simulations and data analysis.

Desired Qualifications

  • Experience within futures markets is preferred.

About the Company

Trexquant

Quantitative trading via ML models

Trexquant is a quantitative finance firm that builds multi-asset portfolios using machine learning. It develops investment and research tools that ingest a wide range of data variables to create complex trading models and strategies, which in turn generate trading signals. These signals guide decisions across global markets. The product works by applying advanced machine learning to large datasets to create predictive models and rule-based strategies, which drive portfolio construction and trading actions through the firm’s platform. Trexquant differentiates itself by emphasizing the breadth of data it uses and the depth of machine-learning driven models to craft multi-asset portfolios, aiming to produce signals that outperform global market conditions. The company’s goal is to generate investment performance that beats benchmarks by using data-driven signals and diversified, multi-asset strategies.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
Stamford, CT
Founded
2012

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