We present a novel agent-based approach to simulating an over-the-counter
(OTC) financial market in which trades are intermediated solely by market
makers and agent visibility is constrained to a network topology. Dynamics,
such as changes in price, result from agent-level interactions that
ubiquitously occur via market maker agents acting as liquidity providers. Two
additional agents are considered: trend investors use a deep convolutional
neural network paired with a deep Q-learning framework to inform trading
decisions by analysing price history; and value investors use a static
price-target to determine their trade directions and sizes. We demonstrate that
our novel inclusion of a network topology with market makers facilitates
explorations into various market structures. First, we present the model and an
overview of its mechanics. Second, we validate our findings via comparison to
the real-world: we demonstrate a fat-tailed distribution of price changes,
auto-correlated volatility, a skew negatively correlated to market maker
positioning, predictable price-history patterns and more. Finally, we
demonstrate that our network-based model can lend insights into the effect of
market-structure on price-action. For example, we show that markets with
sparsely connected intermediaries can have a critical point of fragmentation,
beyond which the market forms distinct clusters and arbitrage becomes rapidly
possible between the prices of different market makers. A discussion is
provided on future work that would be beneficial.

我们提出了一种新颖的基于代理的方法来模拟仅通过市场制造商进行交易，并且代理的可见性受到网络拓扑结构的限制的场外金融市场。我们证明了网络拓扑结构与市场制造商的结合可以探索各种市场结构的作用，包括价格行为、市场的形成以及不同市场制造商之间的套利效应。

多智能体场外交易市场的网络模拟

A Network Simulation of OTC Markets with Multiple Agents

In corporate bond markets, which are mainly OTC markets, market makers play a
central role by providing bid and ask prices for a large number of bonds to
asset managers from all around the globe. Determining the optimal bid and ask
quotes that a market maker should set for a given universe of bonds is a
complex task. Useful models exist, most of them inspired by that of Avellaneda
and Stoikov. These models describe the complex optimization problem faced by
market makers: proposing bid and ask prices in an optimal way for making money
out of the difference between bid and ask prices while mitigating the market
risk associated with holding inventory. While most of the models only tackle
one-asset market making, they can often be generalized to a multi-asset
framework. However, the problem of solving numerically the equations
characterizing the optimal bid and ask quotes is seldom tackled in the
literature, especially in high dimension. In this paper, our goal is to propose
a numerical method for approximating the optimal bid and ask quotes over a
large universe of bonds in a model \`a la Avellaneda-Stoikov. Because we aim at
considering a large universe of bonds, classical finite difference methods as
those discussed in the literature cannot be used and we present therefore a
discrete-time method inspired by reinforcement learning techniques. More
precisely, the approach we propose is a model-based actor-critic-like algorithm
involving deep neural networks.

该研究提出了一种基于深度神经网络的模型的 Actor-Critic 算法，用于解决市场做市商在企业债券市场中的报价优化问题。

企业债市场做市的深度强化学习：打败维度诅咒

Deep reinforcement learning for market making in corporate bonds:  beating the curse of dimensionality

Market makers provide liquidity to other market participants: they propose
prices at which they stand ready to buy and sell a wide variety of assets. They
face a complex optimization problem with both static and dynamic components.
They need indeed to propose bid and offer/ask prices in an optimal way for
making money out of the difference between these two prices (their bid-ask
spread). Since they seldom buy and sell simultaneously, and therefore hold long
and/or short inventories, they also need to mitigate the risk associated with
price changes, and subsequently skew their quotes dynamically. In this paper,
(i) we propose a general modeling framework which generalizes (and reconciles)
the various modeling approaches proposed in the literature since the
publication of the seminal paper "High-frequency trading in a limit order book"
by Avellaneda and Stoikov, (ii) we prove new general results on the existence
and the characterization of optimal market making strategies, (iii) we obtain
new closed-form approximations for the optimal quotes, (iv) we extend the
modeling framework to the case of multi-asset market making and we obtain
general closed-form approximations for the optimal quotes of a multi-asset
market maker, and (v) we show how the model can be used in practice in the
specific (and original) case of two credit indices.

本研究提出了一个通用建模框架，综合了文献中提出的各种建模方法，证明了最优市场制造策略的存在和特征，并针对多资产市场制造将模型框架扩展至通用的闭式近似方法。