: Standard cross-validation fails in finance due to data leakage. These techniques remove overlapping or correlated observations to ensure the model isn't "cheating" by looking at the future.
: Moving away from standard time-based bars to Tick , Volume , or Dollar bars helps synchronized data with market activity levels. Advances in Financial Machine Learning
The field of (FinML) has moved beyond simple predictive models, largely influenced by Marcos López de Prado's seminal work, Advances in Financial Machine Learning . This discipline addresses the unique challenges of financial data, such as low signal-to-noise ratios and non-IID (Independent and Identically Distributed) properties. Core Methodologies in Modern FinML : Standard cross-validation fails in finance due to
: Creating artificial market scenarios to test strategies against conditions not present in historical data. Strategic Challenges Advances in Financial Machine Learning