Research notes
Quant methods, explained
Educational explainers on the financial machine learning methods Voltacent builds on. Methods are attributed to their authors; no performance claims are made.
2026-10-11
Purged K-Fold Cross-Validation with Embargo in Python
Why standard K-fold leaks information in financial time series, and how purging and embargo remove overlapping labels before validation.
2026-10-11
Triple-Barrier Labelling for Financial Machine Learning
A path-dependent labelling method using profit-taking, stop-loss and time barriers, with a compact pandas implementation.
2026-10-11
The Deflated Sharpe Ratio and Backtest Overfitting
How the Deflated Sharpe Ratio corrects for multiple testing, skew and kurtosis, and how it relates to the probability of backtest overfitting.
2026-10-11
Fractional Differentiation for Stationary, Memory-Preserving Features
Integer differencing makes prices stationary but erases memory. Fractional differentiation finds the minimum order that passes a stationarity test.
2026-10-11
The AFML Division of Labour: Why One Quant Overfits
López de Prado's assembly-line model for quantitative research, and how Voltacent Agent mirrors it with isolated specialist roles.