What is quantitative finance?
Quantitative finance uses mathematics, programming, data, and models to study financial questions. A useful student project makes assumptions and limitations explicit.
Beginner explanation
Start with a question, identify data, write assumptions, choose a calculation or model, and decide in advance how you will evaluate it.
Concrete example
A student can compare daily changes in a documented price series, calculate summary statistics, visualize variation, and explain why past variation does not predict future returns.
Vocabulary
Data is the recorded input. A model is a simplified representation. An assumption is a condition treated as true. Evaluation checks whether the method answered its stated question.
Prerequisites
Probability, algebra, basic statistics, and Python are useful. Start with Python for quantitative finance.
Common mistakes
Do not mix future data into a historical test, confuse correlation with cause, or report results without limitations.
Student exercise
Choose a public dataset, calculate one descriptive statistic, make one chart, and write three assumptions that affect interpretation.
Sources
Use the Python tutorial for programming foundations.
Prerequisite math and programming
Use algebra, probability, descriptive statistics, and basic Python before adding model complexity. Learn one concept at a time and retain every assumption.
Common mistakes
Do not confuse a historical result with a forecast, hide data transformations, or omit costs, limitations, and a baseline comparison.
Simple student exercise
Write a small reproducible analysis with a documented public input, one calculation, one labeled chart, and three limits on what the result can establish.
Competition relevance
IQIC reviewers need to understand a team’s method, evidence, assumptions, and limitations. Start with the competition overview.