We introduce a new software toolbox for agent-based simulation. Facilitating rapid prototyping by offering a user-friendly Python API, its core rests on an efficient C++ implementation to support simulation of large-scale multi-agent systems. Our software environment benefits from a versatile message-driven architecture. Originally developed to support research on financial markets, it offers the flexibility to simulate a wide-range of different (easily customisable) market rules and to study the effect of auxiliary factors, such as delays, on the market dynamics. As a simple illustration, we employ our toolbox to investigate the role of the order processing delay in normal trading and for the scenario of a significant price change. Owing to its general architecture, our toolbox can also be employed as a generic multi-agent system simulator. We provide an example of such a non-financial application by simulating a mechanism for the coordination of no-regret learning agents in a multi-agent network routing scenario previously proposed in the literature.
Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
AI4Finance Foundation（AI4Finance基金会）；Dept. of Electrical Engineering, Columbia University（哥伦比亚大学电气工程系）；Mathematics of Systems Research Department, Nokia-Bell Labs（诺基亚贝尔实验室系统数学研究部）
Deep reinforcement learning for optimal trading with partial information
Scuola Normale Superiore, Pisa（比萨高等师范学院）；Department of Statistical Sciences, University of Toronto（多伦多大学统计科学系）；Oxford-Man Institute for Quantitative Finance, University of Oxford（牛津大学牛津-曼量化金融研究所）；Dipartimento di Matematica, University of Bologna（博洛尼亚大学数学系）
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market
Neuro Industry Research（神经产业研究）；Neuro Industry, Inc.（神经产业公司）；Department of Computer Science（计算机科学系）；The University of Alabama（阿拉巴马大学）；Industrial Technology Research Institute（工业技术研究 institutes）
