![]() ![]() ![]() In the last decade, we have witnessed many significant artificial intelligence (AI) milestones achieved by RL approaches in domains such as Go, video games, robotics, and science. With the usage of RL, we can train agents with near-optimal behaviour policy through optimizing task-specific reward functions. RL is an emerging subfield of ML, which provides a mathematical formulation of learning based control to extract knowledge from trial and error. Recently, deep learning becomes an appealing approach owing to not only its stellar performance but also to the attractive property of learning meaningful representations from scratch. ![]() On the other hand, computer scientists apply data-driven machine learning (ML) techniques to analyze financial data. The famous capital asset pricing model (CAPM), Almgren-Chriss model, Markowitz portfolio theory, and Fama & French factor model are a few representative examples. In the finance community, designing theories and models to understand and explain the financial market is the main focus. In general, QT research can be divided into two directions. With the advent of the AI age, it becomes more popular and accounts for more than 70% and 40% trading volumes, in developed markets (e.g., U.S.) and developing markets (e.g., China), respectively. Quantitative trading (QT) is a type of market strategy that relies on mathematical and statistical models to automatically identify investment opportunities. Skip 1INTRODUCTION Section 1 INTRODUCTION ![]()
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