Binance Accelerator Program - Quantitative Trading Strategy Algorithm

Job description

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, protections for investors, and unmatched portfolio of digital asset products and offerings from trading and finance to education, research, social good, payments, institutional services, and Web3 features. Binance is devoted to building an inclusive crypto ecosystem to increase the freedom of money and financial access for people around the world with crypto as the fundamental means. **About Binance Accelerator Program** Binance Accelerator Program (BAP) is a 3-6 month internship program designed for Early Career talent to have firsthand experience in the rapidly expanding digital assets space. You will be given the opportunity to develop your skills at Binance and understand what it’s like to work at the world's leading blockchain ecosystem. As part of your internship in the BAP, there will also be opportunities for networking and development, which will expand your professional network and build transferable skills to propel you forward in your career. Learn about the BAP Program [HERE](https://www.binance.com/en/blog/all/early-career-program-how-binance-gears-up-early-career-talent-6771140985933048651). **Who may apply** Current university students and recent graduates. *\*Terms of employment / engagement shall be subject to contract and local applicable laws* ### 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 1. 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. 2. Participate in the design and optimization of factor prediction models, applying machine learning and deep learning methods to enhance signal predictive power and stability. 3. Participate in the design, backtesting, and validation of trading strategies, assisting with signal generation, portfolio construction, and risk control research. 4. Participate in building the quantitative trading strategy pipeline, helping to streamline the R&D workflow from data, factors, and models to backtesting. 5. 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 1. 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. 2. Strong interest in quantitative trading strategies, familiarity with factor mining and strategy backtesting workflows, and a basic understanding of strategy return and risk. 3. Proficient in Python, knowledgeable about ML/DL methods applied in quantitative scenarios, and experienced in handling financial time-series data. 4. 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). 5. Strong learning ability and research enthusiasm, high initiative, and ability to continuously explore in a fast-iterating environment. ### Nice to Have 1. Course projects, competitions (e.g., quant competitions, Kaggle), or internship experience in quantitative research. 2. Exposure to quantitative research across both traditional finance and on-chain markets (DeFi, CEX, DEX). 3. Practical experience applying machine learning, reinforcement learning, or similar methods to financial data or trading scenarios. 4. Publications, open-source projects, or personal research outcomes in finance or mathematical modeling.