Quantitative Research, Data and Backtesting
Learn to design financial research with point-in-time data, time-ordered validation and economically realistic backtests.
Why study Quantitative Research, Data and Backtesting?
A backtest can look convincing while using information unavailable at the time or ignoring the costs of the trades. This programme treats research as an argument that must survive data, timing and economic checks.
Who this is for
For researchers who can analyse data and want to assess a strategy before considering deployment.
What you should be able to do
- Turn an economic hypothesis into a falsifiable research design.
- Handle data timing, overlapping labels and validation leakage.
- Evaluate backtests with costs, capacity and deployment monitoring.
An example of the work this prepares you for
A research dossier documenting a hypothesis, data availability, validation design, trading assumptions and reasons to accept or reject the result.
Prerequisites and suggested preparation
You should be comfortable with the background covered in these programmes. Equivalent knowledge from university study or independent learning is also suitable.
- Python for Quantitative Finance
- Probability, Statistics and Financial Data
- Econometrics and Financial Time Series
The goal is to use the foundations in context: explain the assumptions, carry out a calculation and interpret the result. If one of those steps feels unfamiliar, review the linked preparation first.
A preview of the learning journey
The lessons build towards these kinds of reasoning and practical work.
Frame a testable claim
Connect an economic mechanism to evidence that could disprove it.
Respect the information clock
Identify which observations were available at each decision.
Assess an executable result
Explain how costs, constraints and repeated trials affect apparent performance.
Members work through the detailed lessons, derivations, worked examples and solutions inside the learning dashboard.
Explore lesson previews by topic
Open a topic preview to see its introduction, learning objectives and prerequisites. Previous and next links let you explore the lessons in study order. Full lessons are available in the member dashboard; selected samples are free to read in full.
Free resources to start learning
Use these alongside the programme or to prepare independently. Start with the introductory material, work through an example, then return to the research papers when the underlying ideas are familiar.
The Probability of Backtest Overfitting
David H. Bailey, Jonathan M. Borwein, Marcos López de Prado and Qiji Jim Zhu
Start with the abstract and examples, then examine the consequences of strategy selection.
Python for Data Analysis
Wes McKinney · third edition
Build reliable table and time-index handling before simulating trades.
The Effect: An Introduction to Research Design and Causality
Nick Huntington-Klein
Develop a research-design habit before choosing an estimator.
Statistical Learning on YouTube
Stanford · companion lectures
Review resampling and model evaluation; financial time ordering needs additional care.
Kenneth R. French Data Library
Dartmouth · Tuck School of Business
Use public benchmark series for a bounded reproducibility exercise.
Links reviewed 14 October 2026. Free refers to the linked reading, notes or videos; printed books and certificates may cost extra.
Find more papers and learning materials in the SelfMadeQuant Directory.
Where this programme fits
- Systematic Trading and Quantitative Research · required programme
- Portfolio, Factor and Asset-Management Quant · required programme
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