NFC / Quant Finance Academy

Question.
Build.
Validate.

A community for the research and engineering behind quantitative finance. Turn financial questions into testable, data-driven models.

Explore the programme Connect with QFA

The academy

Rigorous ideas.
Practical foundations.

QFA develops motivated students through hands-on learning, collaborative projects and an introduction to the quant industry. Research and Development work together to connect an idea with a reproducible result.

01 / Research arm

Ask a better question.

Develop hypotheses, study market behaviour and evaluate trading ideas with statistical methods. Explain the mechanism, test the evidence and report the limitations.

02 / Development arm

Build a reliable answer.

Create data pipelines, backtesting frameworks and simulated execution systems. Turn research into implementations with explicit costs, tests and risk controls.

The learning journey / AY2026–27

From foundations
to a shared showcase.

Thirteen lessons across two semesters, supported by project work, industry exposure and simulated trading. Weekly sessions combine concepts with notebooks, simulations and discussion.

Semester 1

Build the foundations

Algorithmic trading, returns and risk, market microstructure, and preparation for projects and quant careers.

Semester 2

Put ideas to the test

Backtesting, derivatives, factor models, machine learning, statistical arbitrage, portfolio risk and fixed income.

Project work

Research together. Build together.

Mixed teams develop a hypothesis, reproducible implementation, experiment record and honest evaluation.

Showcase

Communicate the evidence

Present your approach, results and limitations. Learn from strategy clinics and a simulated trading competition.

The 13-lesson curriculum

Semester 14 foundation lessons

01
Introduction to algorithmic tradingTeaching week 9
02
Returns, Risk and Strategy IntuitionTeaching week 10
03
Market microstructure and trading technologyTeaching week 11
04
QFA project showcase and quant careersTeaching week 12

Semester 29 applied lessons

05
Strategy research and backtestingTeaching week 1
06
Derivatives I: contracts, payoffs and parityTeaching week 2
07
Derivatives II: valuation, Greeks and implied volatilityTeaching week 3
08
Factor models and alpha researchTeaching week 4
09
Machine learning in financeTeaching week 5
10
Pairs trading and statistical arbitrageTeaching week 6
11
Portfolio construction and risk managementTeaching week 8
12
Fixed income and interest ratesTeaching week 9
13
Research communication and showcase preparationTeaching week 11

AY2026/27 programme outline. Teaching weeks are indicative; members receive confirmed session arrangements separately. Projects and the internal competition use simulated capital.