Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2211.01346v1
• Date: Wed, 28 Sep 2022 01:13:22 GMT
• Title: Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning
• Authors: Tristan Lim
• Abstract summary: The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions. The proposed 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• RediSwap: MEV Redistribution Mechanism for CFMMs [6.475701705193783]
  We introduce RediSwap, a novel AMM designed to capture Maximal Extractable Value (MEV) at the application level and refund it fairly among users and liquidity providers. At its core, RediSwap features an MEV-redistribution mechanism that manages arbitrage opportunities within the AMM pool. We prove that our mechanism is incentive-compatible and Sybil-proof, and demonstrate that it is easy for arbitrageurs to participate.
  arXiv  Detail & Related papers  (2024-10-24T05:11:41Z)
• Enhancing Financial Data Visualization for Investment Decision-Making [0.04096453902709291]
  This paper delves into the potential of Long Short-Term Memory (LSTM) networks for predicting stock dynamics. The study incorporates multiple features to enhance LSTM's capacity in capturing complex patterns. The meticulously crafted LSTM incorporates crucial price and volume attributes over a 25-day time step.
  arXiv  Detail & Related papers  (2023-12-09T07:53:25Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making [33.23156884634365]
  Reinforcement Learning technology has achieved remarkable success in quantitative trading. Most existing RL-based market making methods focus on optimizing single-price level strategies. We propose Imitative Market Maker (IMM), a novel RL framework leveraging both knowledge from suboptimal signal-based experts and direct policy interactions.
  arXiv  Detail & Related papers  (2023-08-17T11:04:09Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
  Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology. One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds. This introduces the problem of finding an optimal strategy for choosing price intervals. We formalize this problem as an online learning problem with non-stochastic rewards.
  arXiv  Detail & Related papers  (2023-02-01T17:21:40Z)
• QLAMMP: A Q-Learning Agent for Optimizing Fees on Automated Market Making Protocols [5.672898304129217]
  We develop a Q-Learning Agent for Market Making Protocols (QLAMMP) that learns the optimal fee rates and leverage coefficients for a given AMM protocol. We show that QLAMMP is consistently able to outperform its static counterparts under all the simulated test conditions.
  arXiv  Detail & Related papers  (2022-11-28T00:30:45Z)
• Predicting Status of Pre and Post M&A Deals Using Machine Learning and Deep Learning Techniques [0.0]
  Risk arbitrage or merger arbitrage is an investment strategy that speculates on the success of M&A deals. Prediction of the deal status in advance is of great importance for risk arbitrageurs. We present an ML and DL based methodology for takeover success prediction problem.
  arXiv  Detail & Related papers  (2021-08-05T21:26:45Z)
• Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT [106.83952081124195]
  Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. We propose an adaptive ADMM (asI-ADMM) algorithm and apply it to decentralized RL with edge-computing-empowered IIoT networks. Experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.
  arXiv  Detail & Related papers  (2021-06-30T16:49:07Z)
• Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning [72.28520951105207]
  Overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning. We propose a novel regularization-based update scheme that penalizes large joint action-values deviating from a baseline. We show that our method provides a consistent performance improvement on a set of challenging StarCraft II micromanagement tasks.
  arXiv  Detail & Related papers  (2021-03-22T14:18:39Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.