Python Developer (Algo Trading, Pandas, NumPy, Backtesting Frameworks)

We are looking for an experienced Python Developer to join our team working with financial systems and the development of algorithmic trading strategies. Key responsibilities include creating, adapting, and optimizing trading indicators, implementing strategies, and integrating with trading platforms and APIs.

 

Requirements:

  • 2+ years of experience working with Python.
  • Strong knowledge of Python, NumPy, Pandas.
  • Understanding of technical analysis and trading indicators (RSI, EMA, Bollinger Bands, etc.).
  • Experience working with financial data and time series.
  • Understanding of multithreading and concurrency.
  • Ability to work with Git and version control platforms.
  • Willingness to learn new frameworks and tools in the algo trading space.

Responsibilities:

  • Develop and adapt trading indicators into trading strategies.
  • Rapidly implement new trading strategies based on descriptions and diagrams.
  • Write custom trading libraries and frameworks.
  • Optimize Python code performance to ensure fast processing of market data and timely trading decisions.
  • Test and debug trading strategies.
  • Integrate strategies with trading platforms and APIs (Binance, Bybit, etc.).

Additional Advantages:

  • Practical experience in backtesting and live trading.
  • Understanding of risk management principles in trading.
  • Python code optimization for high performance (Numba, Multithreading).
  • Experience working with WebSocket for real-time data.
  • Basic knowledge of other programming languages for code migration.

We Offer:

  • Opportunity to gain practical experience in an algorithmic trading project.
  • Mentorship on complex topics.
  • Ability to contribute ideas during development.
  • Competitive salary.
  • Opportunities for professional growth.

Required languages

English A1 - Beginner
Python, Git, REST API, Pandas/NumPy/SciPy/Matplotlib, python-binance, CCXT, NumPy, Pandas
Published 29 May ยท Updated 10 November
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