University of California, Los Angeles 1 to 4 credit Economics
Lecture, three hours. Limited to Master of Quantitative Economics students. Study bridges quantitative trading and artificial intelligence (AI) capabilities by equipping students with skills to design and implement agentic AI systems for financial markets. While conventional approaches rely on humans to interpret news, analyze information, and adjust strategies, study demonstrates how large language model (LLM)-based agents can automate these cognitive tasks while maintaining the rigorous backtesting and execution standards of quantitative finance. Students build physical systems that demonstrate how artificial intelligence can augment—not replace—human decision-making in trading. Emphasis on hands-on implementation, culminating in each student creating their own agent-based trading system that processes real alternative data and executes strategies using reinforcement learning frameworks. Letter grading.
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