Liquidity Provision

Fragmentation and optimal liquidity supply on decentralized exchanges ArXiv ID: 2307.13772 “View on arXiv” Authors: Unknown Abstract We investigate how liquidity providers (LPs) choose between high- and low-fee trading venues, in the face of a fixed common gas cost. Analyzing Uniswap data, we find that high-fee pools attract 58% of liquidity supply yet execute only 21% of volume. Large LPs dominate low-fee pools, frequently adjusting out-of-range positions in response to informed order flow. In contrast, small LPs converge to high-fee pools, accepting lower execution probabilities to mitigate adverse selection and liquidity management costs. Fragmented liquidity dominates a single-fee market, as it encourages more liquidity providers to enter the market, while fostering LP competition on the low-fee pool. ...

Integrating Tick-level Data and Periodical Signal for High-frequency Market Making ArXiv ID: 2306.17179 “View on arXiv” Authors: Unknown Abstract We focus on the problem of market making in high-frequency trading. Market making is a critical function in financial markets that involves providing liquidity by buying and selling assets. However, the increasing complexity of financial markets and the high volume of data generated by tick-level trading makes it challenging to develop effective market making strategies. To address this challenge, we propose a deep reinforcement learning approach that fuses tick-level data with periodic prediction signals to develop a more accurate and robust market making strategy. Our results of market making strategies based on different deep reinforcement learning algorithms under the simulation scenarios and real data experiments in the cryptocurrency markets show that the proposed framework outperforms existing methods in terms of profitability and risk management. ...

Detecting Depegs: Towards Safer Passive Liquidity Provision on Curve Finance ArXiv ID: 2306.10612 “View on arXiv” Authors: Unknown Abstract We consider a liquidity provider’s (LP’s) exposure to stablecoin and liquid staking derivative (LSD) depegs on Curve’s StableSwap pools. We construct a suite of metrics designed to detect potential asset depegs based on price and trading data. Using our metrics, we fine-tune a Bayesian Online Changepoint Detection (BOCD) algorithm to alert LPs of potential depegs before or as they occur. We train and test our changepoint detection algorithm against Curve LP token prices for 13 StableSwap pools throughout 2022 and 2023, focusing on relevant stablecoin and LSD depegs. We show that our model, trained on 2022 UST data, is able to detect the USDC depeg in March of 2023 at 9pm UTC on March 10th, approximately 5 hours before USDC dips below 99 cents, with few false alarms in the 17 months on which it is tested. Finally, we describe how this research may be used by Curve’s liquidity providers, and how it may be extended to dynamically de-risk Curve pools by modifying parameters in anticipation of potential depegs. This research underpins an API developed to alert Curve LPs, in real-time, when their positions might be at risk. ...

Automated Market Making and Arbitrage Profits in the Presence of Fees ArXiv ID: 2305.14604 “View on arXiv” Authors: Unknown Abstract We consider the impact of trading fees on the profits of arbitrageurs trading against an automated market maker (AMM) or, equivalently, on the adverse selection incurred by liquidity providers (LPs) due to arbitrage. We extend the model of Milionis et al. [“2022”] for a general class of two asset AMMs to introduce both fees and discrete Poisson block generation times. In our setting, we are able to compute the expected instantaneous rate of arbitrage profit in closed form. When the fees are low, in the fast block asymptotic regime, the impact of fees takes a particularly simple form: fees simply scale down arbitrage profits by the fraction of blocks which present profitable trading opportunities to arbitrageurs. This fraction decreases with an increasing block rate, hence our model yields an important practical insight: faster blockchains will result in reduced LP losses. Further introducing gas fees (fixed costs) in our model, we show that, in the fast block asymptotic regime, lower gas fees lead to smaller losses for LPs. ...