ABIDES-gym

Amrouni, Selim; Moulin, Aymeric; Vann, Jared; Vyetrenko, Svitlana; Balch, Tucker; Veloso, Manuela

doi:10.1145/3490354.3494433

Cited by 15 publications

(5 citation statements)

References 6 publications

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“…Of the many recent examples of ABM market simulators, one of the most popular and widely used is the Agent-Based Interactive Discrete Event Simulation (ABIDES) framework [7], which provides high-fidelity market simulations. Recent work has extended the ABIDES framework to include a reinforcement learning (RL) environment and a 'hybrid' ABM/neural network-based market simulator [1,37]. In this work, we use two custom-built ABMs for market simulation, which we describe in subsection 3.1.…”

Section: Relevant Workmentioning

confidence: 99%

Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks

Stillman,

Baggott,

Lyon

et al. 2023

4th ACM International Conference on AI in Finance

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The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenarios, such as a flash crash, a margin call, or changes in macroeconomic outlook. In recent years, agent-based models have been developed that reproduce many features of an exchange, as summarised by a set of stylised facts and statistics. However, the ability to calibrate simulators to a specific period of trading remains an open challenge. In this work, we develop a novel approach to the calibration of market simulators by leveraging recent advances in deep learning, specifically using neural density estimators and embedding networks. We demonstrate that our approach is able to correctly identify high probability parameter sets, both when applied to synthetic and historical data, and without reliance on manually selected or weighted ensembles of stylised facts.

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Section: Relevant Workmentioning

confidence: 99%

Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks

Stillman,

Baggott,

Lyon

et al. 2023

4th ACM International Conference on AI in Finance

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“…All the synthetic datasets have been generated using a state-of-art multi-agent simulator called ABIDES (Byrd et al, 2019), and its OpenAI Gym extension called ABIDES-gym (Amrouni et al, 2021). The simulator is written in Python3, and it is publicly available at https://github.com/jpmorganchase/abides-jpmc-public.…”

Section: A1 Multi-agent Synthetic Datamentioning

confidence: 99%

Learning to simulate realistic limit order book markets from data as a World Agent

Coletta

Moulin²,

Vyetrenko

et al. 2022

Proceedings of the Third ACM International Conference on AI in Finance

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Multi-agent market simulators usually require careful calibration to emulate real markets, which includes the number and the type of agents. Poorly calibrated simulators can lead to misleading conclusions, potentially causing severe loss when employed by investment banks, hedge funds, and traders to study and evaluate trading strategies. In this paper, we propose a world model simulator that accurately emulates a limit order book market -it requires no agent calibration but rather learns the simulated market behavior directly from historical data. Traditional approaches fail short to learn and calibrate trader population, as historical labeled data with details on each individual trader strategy is not publicly available. Our approach proposes to learn a unique "world" agent from historical data. It is intended to emulate the overall trader population, without the need of making assumptions about individual market agent strategies. We implement our world agent simulator models as a Conditional Generative Adversarial Network (CGAN), as well as a mixture of parametric distributions, and we compare our models against previous work. Qualitatively and quantitatively, we show that the proposed approaches consistently outperform previous work, providing more realism and responsiveness.

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“…Traders are allowed to specify both the price and the direction (buy or sell) of their orders sent to the exchange. In order to simulate trades in such a market, we employ a multi-agent market simulator called ABIDES (Byrd et al 2020;Amrouni et al 2021). ABIDES provides a selection of trading agents with different trading incentives and behaviors.…”

Section: Multi-agent Market Simulatormentioning

confidence: 99%

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Dwarakanath

Vyetrenko

Balch

2021

Proceedings of the Second ACM International Conference on AI in Finance

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Is transparency always beneficial in complex systems such as traffic networks and stock markets? How is transparency defined in multi-agent systems, and what is its optimal degree at which social welfare is highest? We take an agent-based view to define transparency (or its lacking) as delay in agent observability of environment states, and utilize simulations to analyze the impact of delay on social welfare. To model the adaptation of agent strategies with varying delays, we model agents as learners maximizing the same objectives under different delays in a simulated environment. Focusing on two agent types -constrained and unconstrained, we use multi-agent reinforcement learning to evaluate the impact of delay on agent outcomes and social welfare. Empirical demonstration of our framework in simulated financial markets shows opposing trends in outcomes of the constrained and unconstrained agents with delay, with an optimal partial transparency regime at which social welfare is maximal.

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ABIDES-gym

Cited by 15 publications

References 6 publications

Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks

Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks

Learning to simulate realistic limit order book markets from data as a World Agent

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