量化研究员-Alpha挖掘方向

Description

About us Bitget is one of the world's leading digital assets ecosystems. With over 120 million registered users, Bitget has one of the most comprehensive suites of blockchain products and services available via bitget.com. Our mission is to support the growth of the digital assets industry and we believe it represents the future of finance. What we do empowers the future of finance by ensuring secure, efficient and smart digital transactions. We are one of the fastest growing companies in the digital asset sector. If you are looking for cutting-edge work, where you will have opportunities to develop your career among peers who are experts in their field, and you believe in the future of digital currency, then look no further than Bitget! What you'll do As a core quantitative researcher, you will directly participate in the entire lifecycle of the team's high-frequency trading system's strategy development, using data to extract the alpha (Alpha) of the market. High-frequency factor mining: Conduct in-depth analysis of market data at massive levels (such as Level-2, Tick-level, or order book-intensive order streams), and apply statistical and machine learning methods for high-frequency feature engineering and factor mining. Statistical modeling and strategy development: For specific long-tail assets, mainstream digital assets, or micro-market structures, build, test, and optimize high-frequency market-making (Market Making), statistical arbitrage (Stat Arb), or momentum/m

Responsibilities

• As a core quantitative researcher, you will directly participate in the entire lifecycle of the team's high-frequency trading system's strategy development, using data to extract the alpha (Alpha) of the market.
• High-frequency factor mining: Conduct in-depth analysis of market data at massive levels (such as Level-2, Tick-level, or order book-intensive order streams), and apply statistical and machine learning methods for high-frequency feature engineering and factor mining.
• Statistical modeling and strategy development: For specific long-tail assets, mainstream digital assets, or micro-market structures, build, test, and optimize high-frequency market-making (Market Making), statistical arbitrage (Stat Arb), or momentum/mean reversion trading strategies.
• Backtesting system and model validation: Write high-quality, rigorous high-frequency strategy backtesting scripts, conduct scientific validity tests, multicollinearity tests, and survival analyses on factors to ensure that the strategies are less prone to overfitting in live trading.
• Inventory and risk management: Optimize the dynamic inventory control (Inventory Management), slippage assessment, and micro-hedging mechanisms of the strategies in live trading.

Requirements

• The first degree must be a bachelor's degree or above from a domestic 985 university (or an equivalent top-notch overseas university).
• Professional background: Hardcore science and engineering majors such as mathematics, physics, statistics, computer science, engineering, etc., with strong mathematical and logical reasoning skills, as well as a solid foundation in probability theory and mathematical statistics.
• High-frequency market-making strategy or domestic spot/futures Tick-level quantitative trading practical experience or research experience (mandatory).
• Python expert: Proficiently using Python for data science work, proficiently mastering Polars (or Pandas), Numpy, Statsmodels, Scikit-learn, etc., and capable of efficiently processing and manipulating large-scale high-frequency time series data.
• Factor and modeling experience: Familiar with the factor mining process, with a deep understanding or research interest in order book imbalance (OBI), realized volatility (Realized Volatility), or microstructure (Microstructure).
• English proficiency: Possessing good academic literature reading skills in English, able to quickly convert cutting-edge quantitative papers (such as A-S market-making model variants, machine learning prediction, etc.) into code and conduct real trading tests.
• Bonus points (not mandatory but have priority): Achieving excellent results in Kaggle, mathematical modeling competitions (MCM/ICM), top-level hackathons, or various algorithm/programming competitions (such as ICPC/CCPC).