The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.
翻译:研究提议了一个具有链式保管和结算功能的报价驱动自动市场制造者(AMM)平台,以及一个具有链式保管和结算功能的预测自动市场制造者(AMM)平台,与外链预测强化学习能力一起,以提高真实世界的流动性。拟议的AMM结构是扩大Uniswap V3,一个加密货币AMM协议,利用新的市场均衡定价,以减少差异和滑坡损失;此外,拟议的结构涉及预测AMM能力,利用深混合长时短期内存和学习强化学习框架,利用深层混合的短期内存和学习强化学习框架,以期通过更好地预测流动性集中范围来提高市场效率,因此流动资金开始转向预期的集中范围,从而改善流动性的利用。扩大的协议框架预计将产生实际现实世界的影响,办法是:(一) 减少流动性提供者的差别损失;(二) 减少加密资产交易商的滑坡,同时(三) 提高为AMM协议提供流动性的资本效率。据我们所知,没有已知的协议或文献提出类似的深度学习加速AMMM实现类似的实际资本效率和损失目标。