QuantLab, part 6 of 6
Practice.
Reproducibility, checkable reports, re-runnable workflows, presenting work, teaching a term.
5 articles, about 39 minutes in order. Foundational first, then applied, then advanced.
- 01
Reproducible research: snapshots, hashes and audit trails
How a workspace can prove a result was produced the way it claims, and what a content hash and an append-only ledger actually guarantee.
- 02
Writing a research report someone else can check
A structure for quant research reports that a sceptical reader can verify, section by section, without asking the author anything.
- 03
Building a research workflow you can re-run
The difference between a workflow specification and a workflow run, and why validating a pipeline before running it is what makes it reusable.
- 04
From coursework to portfolio: presenting quant work
What a reviewer actually looks for in a student quant project, and how to present a result honestly enough to survive questions.
- 05
A term plan for teaching quant research with a workspace
A twelve-week course structure that follows the eight-stage research journey, with assessment ideas for each stage.
Other parts of QuantLab
- 01FoundationsWhat quantitative research is, the loop it runs in, and how a result fools you.
- 02Data and featuresPreparing financial time series, building features without leakage, choosing a benchmark.
- 03ModelsModel families and when to use them, linear against trees, hyperparameters, forecasting.
- 04ValidationChronological splits, purging and embargoing, walk-forward folds, the naive baseline, overfitting.
- 05Portfolio and riskOptimisation from first principles, risk metrics that matter, stress testing, explainability.
Read it here, then run it in the workspace.
Every article links to the platform page that implements the idea. Beta access is free and opens by cohort.