In the Fall 2026 semester, DolphinDB and the School of Economics at Zhejiang University continued their collaboration through the graduate elective course Quantitative Finance: Theory and Applications. Running from September to November, the course brought together financial theory, data analytics, and practical applications to help students develop a deeper understanding of quantitative finance.
Designed for students pursuing a master's degree in finance and others interested in quantitative finance, the course combined foundational knowledge with hands-on applications using DolphinDB. Across seven sessions, students followed a structured learning path covering key aspects of quantitative research, from financial data processing to model development and strategy evaluation.
From Financial Data Processing to Strategy Research
The course covered a range of topics, including:
- Introduction to DolphinDB: An overview of DolphinDB and its applications in financial data analytics.
- K-Line Calculation Across Asset Classes: Exploring financial market data processing and candlestick calculations for different asset classes.
- Factor Development: Understanding factor research and implementation through financial data processing and computation.
- Multi-Factor Modeling and Inference: Exploring how individual factors can be combined to build quantitative models.
- Strategy Fundamentals, Backtesting, and Performance Attribution: Connecting data processing and factor research with strategy evaluation and analysis.
- Industry Applications and High-Performance Computing: Examining practical applications of quantitative finance technologies and computing platforms.
Bringing Classroom Learning into the Industry
On November 5, students visited DolphinDB's office for the final session of the course, gaining a closer look at how quantitative finance technologies are applied in industry.
During the session, Xu Yanyan introduced the concept of Financial Ontology and its role in organizing financial knowledge and data. Lu Xing presented DolphinDB's FICC high-performance computing platform, showcasing the underlying technologies that support complex financial computations.
The visit also gave students an opportunity to interact with industry practitioners and connect the concepts learned in class with real-world applications.
We hope this learning experience will inspire students to explore new ideas, deepen their understanding of quantitative finance, and apply what they have learned in their future studies and careers.
DolphinDB Cerulean Campus Program
To advance university collaboration, DolphinDB has officially launched the Cerulean Campus Program — an initiative dedicated to bridging industry and academia through collaborative innovation and joint talent development. The program aims to integrate DolphinDB into university curricula and cultivate a new generation of fintech professionals equipped with global perspectives, innovative thinking, and hands-on expertise. Collaboration formats include guest lectures, curriculum development, talent training, and joint research, offering students access to rich learning resources, internship opportunities, and research topics.
To date, DolphinDB has established partnerships with a number of leading universities across China, including Shanghai Jiao Tong University, Peking University, Zhejiang University, Fudan University, Nanjing University, Renmin University of China, University of Science and Technology of China, The Chinese University of Hong Kong (Shenzhen), and more.
About DolphinDB
DolphinDB is a high-performance distributed time-series database. Beyond efficient storage and querying of massive datasets, DolphinDB pioneers a fully featured programming language for complex analytics, alongside a high-throughput, low-latency, and developer-friendly stream processing framework — making it one of the most computationally powerful database systems available. DolphinDB is widely adopted by leading financial institutions across securities, fund management, banking, and insurance, as well as top enterprises in IoT-driven industries including energy, power, and industrial manufacturing — significantly enhancing large-scale data analysis efficiency and substantially reducing development costs.