Quant

Quantitative and algorithmic strategy

The Quant track introduces members to the concepts behind systematic and algorithmic trading. Members explore how quantitative researchers turn data into testable ideas and how those ideas become rules-based strategies.

The emphasis is on rigor and process: forming a hypothesis, testing it against data, and being honest about what the results do and do not show.

Data-driven research and backtesting

Members work through the research lifecycle used by quantitative teams:

• Sourcing and cleaning data for analysis
• Signal generation and hypothesis testing
• Backtest development and evaluation
• Reasoning about systematic trading concepts and risk

AI-native, agentic tooling

Artificial intelligence is foundational to the track, so members build practical, transferable skills using agentic tools and LLM-powered workflows while learning quantitative methods. No prior tech background is required, only curiosity and a willingness to work through the data.