How-To-Backtest-Correctly
Unverified ML strategy on Multi by Neyt. BotFinder score 18 out of 100.
Advanced Financial Machine Learning Framework - Production-grade quant trading tools based on Lopez de Prado
Source: github
BotFinder analysis pending.
How-To-Backtest-Correctly
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 ---
⚠ No verified equity curve — no track-record source connected.
Drawdown profile
Data unavailable — contact the owner.
Verification ledger
How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
No score history is stored yet — only the current score is shown.
Reviews & comments
No reviews collected from the source yet.
⚠ No live verification account connected — ask for proof before buying.
Alerts on changes: coming soon
Prop-firm compatibility not provided.
Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)