{"repo":"Neyt/How-To-Backtest-Correctly","free":true,"listed":false,"github":"https://github.com/Neyt/How-To-Backtest-Correctly","clone":"git clone https://github.com/Neyt/How-To-Backtest-Correctly.git","description":"Advanced Financial Machine Learning Framework - Production-grade quant trading tools based on Lopez de Prado","language":null,"stars":14,"topics":["algorithmic-trading","backtesting","finance","machine-learning","meta-labeling","overfitting","python","quant","quantitative-finance","trading"],"license":"MIT","category":"trading","readme_excerpt":"How To Backtest Correctly Stop Losing Money to Overfitted Backtests. The open-source implementation of Marcos Lopez de Prado's Advances in Financial Machine Learning methodologies. --- Over 90% of backtested strategies fail in live trading. This framework gives you the mathematical tools to know before you deploy. Get Started Documentation Contributing --- Why This Exists Most quant traders and asset managers commit the same fatal mistakes: - They overfit strategies to historical noise and mistake it for signal - They use broken cross-validation that leaks future information into training data - They evaluate performance with a naive Sharpe Ratio that ignores multiple testing bias - They label data using fixed-time horizons that ignore realistic market microstructure This repository implements the complete scientific pipeline from Lopez de Prado's research to eliminate these pitfalls and build strategies that actually survive in production. \"Backtesting while researching is like drinking and driving. Do not research under the influence of a backtest.\" -- Marcos Lopez de Prado --- Key Features Feature What It Solves Status --- --- --- Triple-Barrier Method Replaces naive fixed-horizon labels with path-aware, realistic labeling Ready Meta-Labeling (Corrective AI) Splits side prediction from size/confidence -- dramatically improves F1-score Ready Purging & Embargoing Eliminates information leakage in time-series cross-validation Ready Combinatorial Purged CV (CPCV) Generates tho","default_branch":null,"files":null,"tree":[],"storefront":"/r/Neyt","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Neyt/How-To-Backtest-Correctly/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}