AI×QUANT LAB

AI × QUANT LAB｜Bring Your Strategy Idea to the Session AI SPACEHUB Session 3 × NUS ConNeXT If AI can write code, parse datasets, identify trading signals and run backtests — how high is the actual barrier to quantitative research today? For our next AI SPACEHUB event, we are launching an interactive AI × Quant Co‑Creation Lab. This is not a conventional talk‑style sharing session where guests present and the audience only listens. We aim to bring together people genuinely interested in quantitative finance: Come Prepared with One Question or Idea A fully polished quantitative strategy is not required. Upon registration, every attendee is encouraged to submit in advance:A quant‑related question you have been pondering, or a strategy / factor idea. Examples for reference: Maturity of your idea does not matter.What counts is genuine critical thinking. Selected representative questions and ideas will be curated in advance as materials for on‑site case discussions.All submitted materials are strictly for internal event discussion only and will not be externally disclosed without explicit consent. Should participants wish, their strategies, code snippets or research notes may later be contributed to our AI × Quant GitHub Repository, building an iterable shared research library. No Lectures — Collaborative Deep‑Dive Sessions We will select several real‑world cases, guided by:Invited Quant & AI Guest Speakerstogether withJunior Quant Researchers from NUS ConNeXT Starting from participants’ questions, we will unpack layer by layer: And one essential question:How far is a seemingly viable strategy from live deployment for actual trading? We do not guarantee definitive answers on the spot.Our core goal is to demonstrate how quantitative researchers frame and analyse problems. AI × Quant: Beyond This Single Session This marks the pilot experiment of the AI SPACEHUB AI × Quant Series.Upcoming themes will include:Factor LabJoint research on factors, trading signals and alpha generation. Strategy ReviewDissect real‑world strategies and backtest outputs. AI Quant ChallengeAssign AI agents to complete full workflows: research → strategy formulation → backtesting. Human × AI QuantCompare research workflows between human researchers and AI agents. Quant GitHub RepositoryContinuously archive questions, strategies, code, experimental outputs and failure cases. We strive to nurture an open quant‑research community built byStudents × Quant Enthusiasts × Traders × Researchers × AI Practitioners. Special Call for Students for This Pilot Session Professional quant experience is not mandatory.You do not even need to have built a complete trading strategy. You only need to: Bring one question.Let’s unpack it together on site. AI × QUANT LAB