QuantLab, part 2 of 6
Data and features.
Preparing financial time series, building features without leakage, choosing a benchmark.
5 articles, about 37 minutes in order. Foundational first, then applied, then advanced.
- 01
Preparing financial time series
Why data preparation follows a fixed order — duplicates, price basis, missing values, calendar alignment, outliers, resampling, minimum history.
- 02
Returns, log returns and stationarity
Simple and log returns answer different questions and compound differently. Stationarity is the assumption almost every downstream statistic relies on.
- 03
Feature engineering for financial data
The main families of financial features, from price and volatility to microstructure, and why point-in-time construction makes any of them usable.
- 04
Look-ahead bias and data leakage: the ten ways it happens
Ten nameable mechanisms by which future information reaches a training set or a backtest, each with a worked example of the number it distorts.
- 05
Benchmarks and why your comparison set matters
A return means nothing alone. What it is measured against — benchmark, naive baseline, peer universe — decides whether it is evidence of anything.
Other parts of QuantLab
- 01FoundationsWhat quantitative research is, the loop it runs in, and how a result fools you.
- 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.
- 06PracticeReproducibility, checkable reports, re-runnable workflows, presenting work, teaching a term.
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.