Trade Selection with Supervised Learning and OCA

ECML PKDD worshop MIDAS 2020

ECML PKDD worshop MIDAS 2020

16 Pages Posted: 27 Dec 2018 Last revised: 21 Jun 2021

See all articles by David Saltiel

David Saltiel

Université Paris Dauphine; A.I. Square Connect; AI For Alpha

Eric Benhamou

Université Paris Dauphine; EB AI Advisory; AI For Alpha

Rida Laraki

Université Paris-Dauphine, PSL Research University

Jamal Atif

Université Paris Dauphine

Date Written: June 20, 2020

Abstract

In recent years, state-of-the-art methods for supervised learning have exploited increasingly gradient boosting techniques, with mainstream efficient implementations such as xgboost or lightgbm. One of the key points in generating proficient methods is Feature Selection (FS). It consists in selecting the right valuable effective features. When facing hundreds of these features, it becomes critical to select best features. While filter and wrappers methods have come to some maturity, embedded methods are truly necessary to find the best features set as they are hybrid methods combining features filtering and wrapping. In this work, we tackle the problem of finding through machine learning best a priori trades from an algorithmic strategy. We derive this new method using coordinate ascent optimization and using block variables. We compare our method to Recursive Feature Elimination (RFE) and Binary Coordinate Ascent (BCA). We show on a real life example the capacity of this method to select good trades a priori. Not only this method outperforms the initial trading strategy as it avoids taking loosing trades, it also surpasses other method, having the smallest feature set and the highest score at the same time. The interest of this method goes beyond this simple trade classification problem as it is a very general method to determine the optimal feature set using some information about features relationship as well as using coordinate ascent optimization.

Keywords: Trade Selection, OCA, Supervised Learning, Features Selection

JEL Classification: T01, T05

Suggested Citation

Saltiel, David and Benhamou, Eric and Laraki, Rida and Atif, Jamal, Trade Selection with Supervised Learning and OCA (June 20, 2020). ECML PKDD worshop MIDAS 2020, ECML PKDD worshop MIDAS 2020, Available at SSRN: https://ssrn.com/abstract=3298347 or http://dx.doi.org/10.2139/ssrn.3298347

David Saltiel (Contact Author)

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

A.I. Square Connect ( email )

35 Boulevard d'Inkermann
Neuilly sur Seine, 92200
France

AI For Alpha ( email )

35 boulevard d'Inkermann
Neuilly sur Seine, 92200
France

Eric Benhamou

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

EB AI Advisory ( email )

35 Boulevard d'Inkermann
Neuilly sur Seine, 92200
France

AI For Alpha ( email )

35 boulevard d'Inkermann
Neuilly sur Seine, 92200
France

Rida Laraki

Université Paris-Dauphine, PSL Research University ( email )

Place du Maréchal de Lattre de Tassigny
Paris, 75016
France

Jamal Atif

Université Paris Dauphine ( email )

Place du Maréchal de Tassigny
Paris, Cedex 16 75775
France

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