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:
Students getting started with quant
Individuals who have built their own trading strategies
Hobbyist quantitative traders
Practitioners with Python, data or finance background yet to deploy a full‑fledged strategy
Experimenters leveraging AI for research, factor mining, backtesting and strategy optimisation
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:
“Under what market conditions does momentum work best?”
“Can news sentiment serve as a tradable factor?”
“Why do AI‑generated strategies perform well in backtests yet fail in live trading?”
“I have spotted a signal but cannot figure out its underlying economic rationale.”
“This strategy has passed backtesting, yet I am unsure whether it suffers from overfitting.”
“I am a total beginner but curious about certain quant‑related puzzles.”
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 Speakers together with Junior Quant Researchers from NUS ConNeXT
Starting from participants’ questions, we will unpack layer by layer:
Why might this idea hold merit?
What is its underlying economic logic?
Is this genuine alpha, or merely noise?
How should the factor be defined?
Where will the data source come from?
How to design a robust backtest?
Are there look‑ahead biases?
Is overfitting a concern?
Will the conclusion still hold across different markets and time horizons?
Which segments of the research pipeline can AI assist with?
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 Lab Joint research on factors, trading signals and alpha generation.
Strategy Review Dissect real‑world strategies and backtest outputs.
AI Quant Challenge Assign AI agents to complete full workflows: research → strategy formulation → backtesting.
Human × AI Quant Compare research workflows between human researchers and AI agents.
Quant GitHub Repository Continuously archive questions, strategies, code, experimental outputs and failure cases.
We strive to nurture an open quant‑research community built by Students × 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:
Have genuine interest in quantitative finance
Be learning AI / Finance / Python / Data Science
Have pondered practical trading‑related questions
Tried preliminary strategy building or backtesting
Or simply wonder: In the AI era, can ordinary students get their own quant‑research copilot?
Bring one question. Let’s unpack it together on site.
AI × QUANT LAB