Building on a previous foundation work (Lussange et al. 2020), this study
introduces a multi-agent reinforcement learning (MARL) model simulating crypto
markets, which is calibrated to the Binance's daily closing prices of $153$
cryptocurrencies that were continuously traded between 2018 and 2022. Unlike
previous agent-based models (ABM) or multi-agent systems (MAS) which relied on
zero-intelligence agents or single autonomous agent methodologies, our approach
relies on endowing agents with reinforcement learning (RL) techniques in order
to model crypto markets. This integration is designed to emulate, with a
bottom-up approach to complexity inference, both individual and collective
agents, ensuring robustness in the recent volatile conditions of such markets
and during the COVID-19 era. A key feature of our model also lies in the fact
that its autonomous agents perform asset price valuation based on two sources
of information: the market prices themselves, and the approximation of the
crypto assets fundamental values beyond what those market prices are. Our MAS
calibration against real market data allows for an accurate emulation of crypto
markets microstructure and probing key market behaviors, in both the bearish
and bullish regimes of that particular time period.

通过使用多智能体强化学习模型，对加密货币市场进行建模和仿真，该模型根据 2018 年至 2022 年间 Binance 交易的 153 种加密货币的每日收盘价进行校准，利用市场价格和加密资产基本价值的近似值进行资产价格估值，从而精确模拟加密货币市场的微观结构和关键市场行为。

多智能体强化学习模拟加密市场

Modelling crypto markets by multi-agent reinforcement learning

Designing robust and accurate prediction models has been a viable research
area since a long time. While proponents of a well-functioning market
predictors believe that it is difficult to accurately predict market prices but
many scholars disagree. Robust and accurate prediction systems will not only be
helpful to the businesses but also to the individuals in making their financial
investments. This paper presents an LSTM model with two different input
approaches for predicting the short-term stock prices of two Indian companies,
Reliance Industries and Infosys Ltd. Ten years of historic data (2012-2021) is
taken from the yahoo finance website to carry out analysis of proposed
approaches. In the first approach, closing prices of two selected companies are
directly applied on univariate LSTM model. For the approach second, technical
indicators values are calculated from the closing prices and then collectively
applied on Multivariate LSTM model. Short term market behaviour for upcoming
days is evaluated. Experimental outcomes revel that approach one is useful to
determine the future trend but multivariate LSTM model with technical
indicators found to be useful in accurately predicting the future price
behaviours.

本文使用两种不同的输入方法设计了一种 LSTM 模型，以预测印度两家公司 Reliance Industries 和 Infosys Ltd 的短期股价。结果表明，使用技术指标的 Multivariate LSTM 模型可以更准确地预测未来的股价行为。

短期股市预测的单变量和多变量 LSTM 模型

Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction

Most modern financial markets use a continuous double auction mechanism to
store and match orders and facilitate trading. In this paper we develop a
microscopic dynamical statistical model for the continuous double auction under
the assumption of IID random order flow, and analyze it using simulation,
dimensional analysis, and theoretical tools based on mean field approximations.
The model makes testable predictions for basic properties of markets, such as
price volatility, the depth of stored supply and demand vs. price, the bid-ask
spread, the price impact function, and the time and probability of filling
orders. These predictions are based on properties of order flow and the limit
order book, such as share volume of market and limit orders, cancellations,
typical order size, and tick size. Because these quantities can all be measured
directly there are no free parameters. We show that the order size, which can
be cast as a nondimensional granularity parameter, is in most cases a more
significant determinant of market behavior than tick size. We also provide an
explanation for the observed highly concave nature of the price impact
function. On a broader level, this work suggests how stochastic models based on
zero-intelligence agents may be useful to probe the structure of market
institutions. Like the model of perfect rationality, a stochastic-zero
intelligence model can be used to make strong predictions based on a compact
set of assumptions, even if these assumptions are not fully believable.

本文提供了一个针对连续双重拍卖的微观动力学统计模型，该模型基于 IID 随机订单流，并使用模拟，尺寸分析以及基于平均场近似的理论工具进行分析，并展示了这些预测的基本性质，如价格波动率，存储的供求深度与价格，买卖价差，价格影响功能以及订单的时间和概率填充。此外，作者还提供了一种解释为什么价格影响功能具有高度凸性的观点。