Binance Accelerator Program - Quantitative Trading Strategy Algorithm

About the Role

We are building an AI-driven trading system covering traditional financial assets such as equities as well as on-chain assets. We are looking for a Quantitative Trading Strategy Algorithm Intern who is passionate about trading strategies to participate in research spanning factor discovery, factor prediction, and trading strategy and system construction - combining quantitative research capabilities with AI technology to grow rapidly through real-world strategy R&D.

Responsibilities

• 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.

Requirements

• 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.

Nice to Have

• Course projects, competitions (e.g., quant competitions, Kaggle), or internship experience in quantitative research.
• Exposure to quantitative research across both traditional finance and on-chain markets (DeFi, CEX, DEX).
• Practical experience applying machine learning, reinforcement learning, or similar methods to financial data or trading scenarios.
• Publications, open-source projects, or personal research outcomes in finance or mathematical modeling.