🤖 AI Summary
This work proposes the first market-making framework that explicitly integrates the continuous limit order book with the closing auction mechanism, addressing a key limitation of traditional strategies that often neglect liquidity dynamics during the closing auction and rely solely on terminal inventory penalties for risk control. By leveraging deep Q-learning, the framework dynamically forecasts the auction clearing price and is trained within a generative market simulator based on the rough Heston model. The approach continuously updates its clearing price predictions as the trading session progresses. Empirical evaluations demonstrate that the proposed method significantly outperforms classical optimal market-making benchmarks—both in synthetic environments and on real-world S&P 500 data—achieving higher profitability while effectively mitigating end-of-day inventory risk.
📝 Abstract
In this work, we investigate the market-making problem on a trading session in which a continuous phase on a limit order book is followed by a closing auction. Whereas standard optimal market-making models typically rely on terminal inventory penalties to manage end-of-day risk, ignoring the significant liquidity events available in closing auctions, we propose a Deep Q-Learning framework that explicitly incorporates this mechanism. We introduce a market-making framework designed to explicitly anticipate the closing auction, continuously refining the projected clearing price as the trading session evolves. We develop a generative stochastic market model to simulate the trading session and to emulate the market. Our theoretical model and Deep Q-Learning method is applied on the generator in two settings: (1) when the mid price follows a rough Heston model with generative data from this stochastic model; and (2) when the mid price corresponds to historical data of assets from the S&P 500 index and the performance of our algorithm is compared with classical benchmarks from optimal market making.