Binance Accelerator Program - Quantitative Trading Strategy Algorithm

Overview

The organization is building an AI-driven trading system covering both traditional financial assets (such as equities) and on-chain assets. This role supports research and development across factor discovery, factor prediction, and trading strategy/system construction, combining quantitative research with AI.

Responsibilities

• Participate in the discovery, construction, and validation of trading factors, exploring alpha signals from multi-source data including market data, fundamental data, and on-chain data.
• Contribute to the design and optimization of factor prediction models, applying machine learning and deep learning methods to improve signal predictive power and stability.
• Support the design, backtesting, and validation of trading strategies, assisting with signal generation, portfolio construction, and risk control research.
• Help build and improve a quantitative trading strategy pipeline to streamline the R&D workflow from data, factors, and models to backtesting.
• Track frontier methods in quantitative and AI-driven trading and conduct exploratory research connecting characteristics of traditional equities and on-chain assets.

Requirements

• Currently enrolled 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.
• Ability 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 settings.
• Experience handling financial time-series data.
• Understanding of trading mechanisms and data characteristics in at least one market, such as equities, futures, other traditional financial markets, or crypto and on-chain assets.
• Strong learning ability and research enthusiasm, with high initiative and the ability to continuously explore in a fast-iterating environment.

Preferred Qualifications

• 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.

Compensation & Benefits

• Competitive salary and company benefits.

Location

Remote-first, work-from-home arrangement (may vary depending on the nature of the role and team).