Aymeric Moulin's scientific contributions
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Publications (7)
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 pa...
Market regimes is a popular topic in quantitative finance even though there is little consensus on the details of how they should be defined. They arise as a feature both in financial market prediction problems and financial market task performing problems. In this work we use discrete event time multi-agent market simulation to freely experiment i...
Model-free Reinforcement Learning (RL) requires the ability to sample trajectories by taking actions in the original problem environment or a simulated version of it. Breakthroughs in the field of RL have been largely facilitated by the development of dedicated open source simulators with easy to use frameworks such as OpenAI Gym and its Atari envi...
Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approach, consists of simulating experimental strategies while replaying historical market scenarios. Unfortunately, this approach does not capture the market response to the e...
Citations
... However, accurately modeling the full market impact of high-frequency trading in LOB markets in a datadriven approach is an interesting direction for future research and would allow evaluating strategies with larger order sizes. Recent attempts in this vein have used agent-based models (Byrd et al., 2020b) or generative models (Coletta et al., 2021(Coletta et al., , 2022(Coletta et al., , 2023. ...
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... To the knowledge of the author, this line of research traces back to Kumar et al. [2018], Shi et al. [2019], who adapted GAN to generate BUY orders in e-commerce markets. More importantly, GAN has been utilized to model more complex stock markets and simulate various types of stock market data, including transaction event time [Xiao et al., 2017[Xiao et al., , 2018, price [Zhang et al., 2019, Da Silva and Shi, 2019, Wiese et al., 2020, Koshiyama et al., 2021, and even orders [Li et al., 2020, Coletta et al., 2021. ...
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... Using DES simulators for evaluating real-world solutions offers several advantages over numerical experimentation, using real-world data, or even real-world implementations [17]. It provides virtual experiments that simulate the behavior of complex systems without the need to deploy actual hardware or software. ...
