lopez-de-prado

scikit-learn-compatible time-series cross-validation: purging, embargo, combinatorial purged CV, and deflated Sharpe ratios

Empirical asset-pricing research framework (Python) with formal overfitting control — Combinatorial Purged CV, Deflated Sharpe Ratio, Probability of Backtest Overfitting. ~20 pre-registered experiments on a survivorship-free, point-in-time S&P 500 universe, with an honest public kill-log.

Honest Lopez de Prado-style validation harness: Deflated Sharpe + Combinatorial Purged CV + alpha/beta + bootstrap — kills false 'beta illusion' edges before risking capital.

Independent, MIT-licensed Python implementation of the López de Prado causal factor-investing and falsification framework. Run a falsification battery, measure how much of a backtest edge survives search and selection, and falsify your edge before you risk capital.

Python toolkit for financial machine learning, implementing methods from López de Prado’s Advances in Financial Machine Learning: purged cross-validation, triple-barrier labels, fractional differentiation, PBO, deflated Sharpe, HRP and the Monte Carlo experiment.

Living technical reference for Disuza Quantitative — private quantitative research laboratory, Madrid, Spain. Architecture, anti-overfit methodology (CPCV / DSR / PBO per López de Prado), regulatory posture. Source code proprietary.

López de Prado's preprocessing, step by step: dollar bars, fractional differentiation, Marchenko-Pastur denoising, triple-barrier labels and time-aware splits. MIAX, pair work.

[ESP] Master’s Coursework (mIAx): Bayesian networks for financial causal discovery: DAG inference on macro asset returns and reproduction of the factor mirage (López de Prado & Zoonekynd, 2025).

An honest-invalidation study of cross-sectional return prediction on Brazilian (B3) equities — López de Prado pipeline, CPCV, Deflated Sharpe. Not a trading system.

[ESP] Master’s Coursework (mIAx): Deep learning for S&P 500 return forecasting: 64-model grid (MLP/RNN/CNN/mixed) + López de Prado investigation track and a model-driven portfolio.

[ESP] Master’s Coursework (mIAx) - Quantitative Finance Studies: fixed income, equity microstructure, derivatives, risk management, backtesting, ML preprocessing (López de Prado), fund of funds.