The opportunity
Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. Binance is trusted by more than 320 million people in 100+ countries for its industry-leading security, transparency, trading engine speed,…
What you'll do
Participate in the discovery, construction, and validation of trading: factors, exploring effective alpha signals from multi-source data including market data, fundamental data, and on-chain data.
Participate in the design and optimization of factor prediction models,: applying machine learning and deep learning methods to enhance signal predictive power and stability.
Participate in the design, backtesting, and validation of trading strategies,: assisting with signal generation, portfolio construction, and risk control research.
Participate in building the quantitative trading strategy pipeline, helping: to streamline the R&D workflow from data, factors, and models to backtesting.
Track frontier methods in quantitative and AI-driven trading, conducting: exploratory research that combines the market characteristics of traditional equities and on-chain assets.
What they're looking for
- Current Master's or PhD student in Computer Science, Mathematics, Statistics,: Financial Engineering, Physics, or a related field, with a strong quantitative foundation and programming skills, able to commit to stable weekly internship hours.
- Strong interest in quantitative trading strategies, familiarity with factor: mining and strategy backtesting workflows, and a basic understanding of strategy return and risk.
- Proficient in Python, knowledgeable about ML/DL methods applied in: quantitative scenarios, and experienced in handling financial time-series data.
- Understanding of trading mechanisms and data characteristics in at least one: market (equities, futures, or other traditional financial markets; or crypto and on-chain assets).
- Strong learning ability and research enthusiasm, high initiative, and ability: to continuously explore in a fast-iterating environment.
