Deep Reinforcement Learning and Evolutionary Strategies for High Frequency Trading

• Grant number: 23/00441-9
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2023
• End date: June 30, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: André Carlos Ponce de Leon Ferreira de Carvalho
• Grantee: Thiago Ambiel
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

The objective of this work is to investigate two classes of reinforcement learning algorithms for the development of High-Frequency Trading strategies: Deep Reinforcement Learning algorithms and Reinforcement Learning algorithms through Evolutionary Optimizers. Initially, we propose the development of a realistic simulator of the Brazilian stock market, with the purpose of allowing the training and performance analysis of the algotrading algorithms. Subsequently, reinforcement learning algorithms from the above-mentioned two classes will be implemented, using Deep Neural Networks and Decision Trees as the structure for the models.

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

AMBIEL, THIAGO; CASTILHO, DOUGLAS; DE CARVALHO, ANDRE C. P. L. F.. The Strength of Influence Ties in Stock Networks: Empirical Analysis for Portfolio Selection. 2023 23RD IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS, ICDMW 2023, v. N/A, p. 9-pg., 2023-01-01. (23/00441-9)