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。