{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/trade-selection-with-supervised-learning-and","title":"Trade Selection with Supervised Learning and OCA","arxiv_id":"1812.04486","date":"2018-12-09","proceeding":null,"authors":["David Saltiel","Eric Benhamou"],"abstract":"In recent years, state-of-the-art methods for supervised learning have\nexploited increasingly gradient boosting techniques, with mainstream efficient\nimplementations such as xgboost or lightgbm. One of the key points in\ngenerating proficient methods is Feature Selection (FS). It consists in\nselecting the right valuable effective features. When facing hundreds of these\nfeatures, it becomes critical to select best features. While filter and\nwrappers methods have come to some maturity, embedded methods are truly\nnecessary to find the best features set as they are hybrid methods combining\nfeatures filtering and wrapping. In this work, we tackle the problem of finding\nthrough machine learning best a priori trades from an algorithmic strategy. We\nderive this new method using coordinate ascent optimization and using block\nvariables. We compare our method to Recursive Feature Elimination (RFE) and\nBinary Coordinate Ascent (BCA). We show on a real life example the capacity of\nthis method to select good trades a priori. Not only this method outperforms\nthe initial trading strategy as it avoids taking loosing trades, it also\nsurpasses other method, having the smallest feature set and the highest score\nat the same time. The interest of this method goes beyond this simple trade\nclassification problem as it is a very general method to determine the optimal\nfeature set using some information about features relationship as well as using\ncoordinate ascent optimization.","url_abs":"http://arxiv.org/abs/1812.04486v1","url_pdf":"http://arxiv.org/pdf/1812.04486v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"trade-selection-with-supervised-learning-and","repo_url":"https://github.com/davidsaltiel/OCA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}