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Trexquant

Quantitative trading via ML models

Quantitative Researcher

Quantitative ResearchConfirmed live in the last 24 hours
Full-timeBeijing, ChinaNo salary listedIn-person

About the Job

Requirements

  • A BS, MS, or PhD degree in any STEM field is required.
  • At least 3 years of experience in a systematic trading environment is required; a live trading track record is preferred.
  • A strong interest in machine learning is required.
  • Fluency in programming languages such as Python is required.
  • Strong problem-solving skills are required.
  • The ability to work effectively both independently and as part of a team is required.
  • Strong English communication skills for daily professional interactions are required.

Responsibilities

  • Design, implement, and optimize machine learning models to predict liquid assets using financial data and trading signals.
  • Parse and analyze large datasets to identify actionable alpha signals and develop strategies for systematic trading.
  • Investigate and implement state-of-the-art academic research in quantitative finance.
  • Improve existing models by integrating new data sources and advanced techniques to increase performance and scalability.
  • Collaborate with quantitative researchers to conduct experiments, backtest hypotheses, and refine strategies through simulations and data analysis.

Desired Qualifications

  • A live trading track record 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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