March 2025
Intermediate to advanced
214 pages
5h 26m
French
Content preview from Apprentissage par renforcement pour la finance
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Chapitre 3. Apprentissage Q financier
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Les programmes de trading algorithmique d'aujourd'hui sont relativement simples et ne font qu'un usage limité de l'IA. Cela va certainement changer.
Murray Shanahan (2015)
Le chapitre précédent montre qu'un agent de type deep Q-learning (DQL) peut apprendre à jouer assez bien au jeu de CartPole. Qu'en est-il des applications financières ? Comme le montre ce chapitre, l'agent peut également apprendre à jouer à un jeu financier qui consiste à prédire les mouvements futurs d'un marché financier. À cette fin, ce chapitre met en œuvre un environnement Finance qui imite le comportement de l'environnement CartPole et forme l'agent DQL du chapitre précédent en fonction des exigences de l'environnement Finance.
Ce chapitre est bref, mais il illustre un point important : avec l'environnement approprié, DQL peut être appliqué aux problèmes financiers essentiellement de la même manière qu'il est appliqué aux jeux et dans d'autres domaines. "Finance Environment" développe pas à pas la classe Finance qui imite le comportement de la classe CartPole. "DQL Agent" modifie légèrement la classe DQLAgent de "CartPole as an Example". Les ajustements sont faits pour refléter le nouveau contexte. L'agent DQL peut apprendre à prédire les futurs mouvements du marché avec une marge significative par rapport à la précision de base de 50 ...
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