PAMS: Platform for Artificial Market Simulations
Masanori Hirano, Ryosuke Takata, Kiyoshi Izumi
TL;DR
PAMS is a flexible Python-based platform designed for artificial market simulations, facilitating integration with deep learning for research and development in financial modeling.
Contribution
This paper introduces PAMS, a novel, user-friendly simulation platform that supports deep learning integration for artificial market research.
Findings
PAMS effectively supports deep learning-based agent simulations.
The platform enables easy modifications for diverse market scenarios.
Demonstrated successful prediction of future prices using PAMS.
Abstract
This paper presents a new artificial market simulation platform, PAMS: Platform for Artificial Market Simulations. PAMS is developed as a Python-based simulator that is easily integrated with deep learning and enabling various simulation that requires easy users' modification. In this paper, we demonstrate PAMS effectiveness through a study using agents predicting future prices by deep learning.
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Stock Market Forecasting Methods