Amsterdam Algo Trader - Ground-Floor Ownership

QuantFlow

Amsterdam

On-site

EUR 70,000 - 120,000

Full time

14 days+
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Benefits offered by this job

Ground-floor role in a new Amsterdam '

Job summary

QuantFlow in Amsterdam is expanding its office and seeks analytical and creative traders to build out its India index and equity options trading from Amsterdam. As one of the first hires you will gain meaningful ownership over how the operation takes shape, working closely with the trading and technology teams.

You will design, troubleshoot and optimize algorithmic trading strategies and the supporting pricing, risk management and order execution modules; improve trading models through

Qualifications

  • At least 1 year of professional experience in algorithmic trading, ideally India index and/or equity options.
  • Proven ability to solve complex analytical problems and ship robust solutions.
  • Strong background in statistics, probability, linear algebra and calculus.
  • Proficient in Python and/or R for research; SQL experience with large datasets.
  • Exposure to Linux/Unix is a plus.

Responsibilities

  • Design and optimize algorithmic trading strategies and the supporting pricing, risk management and order execution modules.
  • Improve trading models and parameters through research and data-focused analysis.
  • Investigate new opportunities for market share growth and product expansion.

Skills

Algorithmic trading
Analytical problem solving
Statistics & math
Python/R & SQL
Linux/Unix exposure

Tools

SQL
Python

Job description

QuantFlow in Amsterdam is expanding its office and seeks analytical and creative traders to build out its India index and equity options trading from Amsterdam. As one of the first hires you will gain meaningful ownership over how the operation takes shape, working closely with the trading and technology teams.

You will design, troubleshoot and optimize algorithmic trading strategies and the supporting pricing, risk management and order execution modules; improve trading models through

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