Quantitative Researcher (HFT)

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Overview of Rock Bund Capital

Rock Bund Capital is a proprietary trading firm established in 2019, focused on transforming the cryptocurrency industry. With an average daily trading volume exceeding $1 billion and peak trading volumes reaching $5 billion, the firm processes over 7 million transactions daily, trading more than 1,000 symbols across various cryptocurrency exchanges (both Centralized Exchanges, CEx, and Decentralized Exchanges, DEx). Rock Bund Capital collaborates with top-tier platforms, where it holds the highest VIP status, and provides liquidity to over 20 popular protocols across multiple blockchain networks.

Company Expertise

The company's strength lies in its team, which combines expertise in traditional finance, quantitative research, and advanced engineering, along with an in-depth understanding of blockchain technology. This distinctive combination allows Rock Bund Capital to excel in trading across complex crypto markets, notably within the realms of both CeFi and DeFi, while also offering capital and strategic guidance to innovative projects fostering growth in the cryptocurrency space.

Job Objective

The Quantitative Researcher (HFT) role is crucial for developing and implementing high-frequency trading strategies. Key responsibilities include:

  • Researching, developing, and executing high-frequency alpha taking and market-making strategies.
  • Contributing to the design of trading strategies and overall portfolio construction.
  • Utilizing machine learning tools for data modeling, strategy backtesting, and optimization.
  • Managing the operation and risk monitoring of deployed trading strategies.

Required Qualifications

The ideal candidate for this position will possess a strong academic background with the following qualifications:

  • A Master's degree in Science, Engineering, or Quantitative Finance.
  • 2-5 years of trading or research experience with a focus on high-frequency quantitative strategies.
  • Familiarity with market microstructure and backtesting systems.
  • Proficient in mathematical and statistical modeling, capable of analyzing large financial datasets.
  • Experience in coding, especially with languages such as Python, C++, or C. Familiarity with code acceleration methods and a good understanding of system architecture is preferred.

  • While not essential, experience in network design aimed at minimizing latency, as well as a background in cutting-edge machine learning techniques, would be advantageous.

  • Candidates should demonstrate creativity and be intellectually curious, with a passion for the digital asset space.

  • Proficiency in English and Chinese is required, as effective communication across regions is vital.

Work Environment

This position is remote-based, allowing for flexibility while promoting collaboration among team members in a casual and result-oriented work environment.

Benefits and Culture

Rock Bund Capital offers a competitive remuneration package and fosters a meritocratic culture where achievements are recognized and rewarded. Employees can expect an engaging environment that emphasizes rapid pace and productivity, along with excellent exposure to the dynamic digital asset ecosystem, providing insights into the latest market trends. Moreover, the firm emphasizes career development opportunities, making it an appealing workplace for those looking to grow and advance in the field.

Important Note

Rock Bund Capital does not accept unsolicited resumes from staffing or recruitment agencies. Any unsolicited candidate information received will not lead to placement fees if a candidate is hired.

In summary, the Quantitative Researcher (HFT) role at Rock Bund Capital is a unique opportunity within a leading trading firm in the cryptocurrency industry. It not only supports a strong team with diverse expertise but also rewards employees with competitive compensation and growth opportunities amid a rapidly evolving market.



This job offer was originally published on himalayas.app

Rock Bund Capital

Remote

Data science

Full-time

March 22, 2026

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