Automated market-making for CBDCs and stable coins on blockchain.
problem Creating fair exchange rates for digital assets on blockchain.
method Developed an innovative approach for generating fair exchange rates.
result Illustrated the approach's efficacy on G-10 currency exchange rates.
New formula identifies and quantifies costs for automated market makers.
problem Adverse selection costs faced by liquidity providers in automated market makers.
method Derives a Black-Scholes-like formula for AMMs and identifies loss-versus-rebalancing cost.
result Closed-form expressions for LVR applicable to all automated market makers.
DFMM automates market making with adaptive pricing and risk management.
problem Challenges in decentralised automated market making (AMMs).
method Data aggregator, order routing, rebalancing, arbitrageurs, protective buffers, algorithmic accounting.
result DFMM optimises inventory risk and ensures market stability.
Paper uses SAC RL to optimize market-making strategies.
problem Optimizing market-making strategies with risk management.
method Applying SAC reinforcement learning to automate market-making decisions.
result Agent learns to optimize spreads and hedge trades.
Paper analyzes constant-product market making protocols.
problem Understanding and optimizing constant-product market making.
method Mathematical analysis of trade splitting and fee recompounding.
result Splitting trades does not affect final exchange rate.
QLAMMP optimizes fees on AMMs using Q-Learning.
problem Static AMMs cannot adapt to market changes, leading to high slippage.
method Developed a Q-Learning Agent (QLAMMP) to learn optimal fee rates.
result QLAMMP consistently outperforms static AMMs under various market conditions.
Paper proposes a new reinforcement learning framework for cryptocurrency market making.
problem Improving profit and stability in cryptocurrency market making.
method Event-based reinforcement learning environment, training two policy-based agents with neural networks and various reward functions.
result Improved profit and stability demonstrated over time-based approach.
This study optimizes trading and arbitrage in decentralized finance's CPMs, revealing convexity costs and developing efficient strategies.
problem Optimizing trading and arbitrage in decentralized finance's constant product markets (CPMs).
method Developed models for CPMs in competing centralised exchanges, CPMs, and both venues. Derived computationally efficient strategies.
result Accurately estimated convexity costs in CPMs, which are linear in trade size and nonlinear in liquidity depth and exchange rate.
Novel AMM model for pegged cryptoassets using nested OU processes.
problem Liquidity and risk management in markets for pegged cryptoassets.
method Multi-level nested Ornstein-Uhlenbeck (OU) processes for exchange rate dynamics, calibrated and filtered AMM model.
result Consistent efficient quotes and improved liquidity provision for pegged cryptoassets.
This paper explores BTC-denominated prediction markets to avoid stablecoin opportunity costs.
problem Opportunity costs and loss of BTC exposure when converting to stablecoins.
method Analyzes three methods of liquidity provision: cross-market making, automated market making, and DeFi redirection.
result Cross-market making provides the best user risk profile but requires active liquidity.
Traditional market makers are losing their importance as automated systems have largely assumed the role of liquidity provision in markets. We update the model of Glosten and Milgrom (1985) to analyze this new world: we add multiple securities and introduce an automated market maker who uses the relationships between s…
New models optimize quotes for automated market makers considering various price dynamics and demand variability.
problem Optimizing quotes for automated market makers in volatile price environments.
method Advanced models incorporating stochastic volatility, jumps, Hawkes processes, and Markov-modulated Poisson processes.
result Optimal quotes can be computed using numerical methods tailored to each model.
Enhances crypto-asset AMM with deep learning for better liquidity and efficiency.
problem Reduced slippage and improved liquidity in decentralized finance.
method Deep reinforcement learning for predicting market equilibrium and optimizing liquidity.
result Improved capital efficiency and reduced slippage for crypto-asset traders.
Uniform AMMs control loss in prediction markets.
problem Controlling loss in prediction markets.
method Loss-versus-rebalancing (LVR) framework and uniform AMMs.
result Uniform AMMs achieve proportional LVR to pool value.
Paper optimizes liquidity provision in decentralized finance markets.
problem Strategic LPs face predictable losses and concentration risk in CL pools.
method Derive optimal liquidity provision strategy based on fees, PL, and concentration risk.
result Optimal strategy increases fee revenue and profit from marginal rate changes.
This paper solves optimal market making for multiple goods, including bundling, under adverse selection.
problem Designing optimal market making mechanisms for multiple goods and adverse selection.
method Formulated as an optimal transport problem with geometric constraints, using differentiable economics.
result Optimal market making mechanisms can exploit bundling to improve prices and accept payments in kind.
We introduce a modular framework for market making. It combines cost-function based automated market makers with bandit algorithms. We obtain worst-case profits guarantee's relative to the best in hindsight within a class of natural "overround" cost functions . This combination allow us to have distribution-free guaran…
Novel method reconstructs liquidity data for CLMMs, optimizing dynamic liquidity strategies.
problem Challenges in evaluating and optimizing CLMMs due to lack of historical liquidity data.
method Reconstructs historical liquidity states from swap transaction data using machine learning.
result Identifies outperformance of dynamic liquidity strategies over uniform allocation benchmarks.
Backtesting framework for CLMMs on Uniswap V3 reduces reward estimation error.
problem Estimating rewards for CLMMs in Uniswap V3 liquidity pools.
method Parametric model for liquidity distribution, historical data analysis.
result Error in reward estimation less than 1% for each pool.
Deep RL controller outperforms market making benchmarks in a Hawkes process model.
problem Optimal market making in financial markets.
method Deep reinforcement learning on a Hawkes process-based simulator.
result Deep RL controller outperforms benchmarks in various risk-reward metrics.
Proposes a deep RL approach for high-frequency market making using tick data and periodic signals.
problem Challenges in high-frequency market making due to tick-level data complexity and high trading volume.
method Integrates tick-level data with periodic signals using deep reinforcement learning.
result The proposed framework outperforms existing methods in profitability and risk management.
Paper introduces robust market making using Wasserstein distance and entropy regularization.
problem Market making robustness under uncertainty.
method Wasserstein distance, entropy regularization, convex optimization, optimal radius selection.
result The robust market making problem can be reformulated as a convex optimization problem.
Study optimizes market making in Chinese stock market with stochastic control and scenario analysis.
problem Limited research on market making in Chinese stock market.
method Optimal market making framework with exponential CARA utility function, accounting for market conditions and risks.
result Impact of volatility and stamp duty on market maker's profit and liquidity.
This thesis studies CPMMs with CL, developing strategies for LTs and LPs.
problem Trading mechanisms and strategies for CPMMs with CL.
method Formalizes CPMMs with CL, develops strategies using market data and models.
result Derives optimal strategies for LTs and LPs in CPMMs with CL.
This research improves capital efficiency and impermanent loss in cryptocurrency markets using multi-token trading pools.
problem Poor impermanent loss and capital efficiency in automated market makers.
method Analysis and construction of a multi-token token proactive market maker (MPMM).
result MPMM shows better impermanent loss and capital efficiency than comparable market makers.
A Deep Q-Learning framework tackles market-making by incorporating closing auctions.
problem Managing end-of-day risk in market-making models.
method Developed a Deep Q-Learning framework that anticipates closing auctions and continuously refines projected clearing prices.
result The Deep Q-Learning framework outperforms classical market-making models in simulations and real data.
A new macroscopic market making model connects market making and optimal execution.
problem Connecting market making and optimal execution problems.
method Using continuous processes for orders, the model bridges the gap between market making and optimal execution.
result Demonstrates the model's effectiveness through various noise and intensity function scenarios.
Optimized execution model using interbank and internal liquidity.
problem Minimizing market impact in trading.
method Integrates interbank limit and market orders with internal market-making liquidity.
result Reduces market impact and improves execution efficiency.
We derive a formula for liquidity providers' payoff on DEXs, linking it to volatility.
problem Liquidity providers on DEXs are undercompensated for their service.
method We derive a payoff formula for liquidity providers on DEXs, assuming geometric Brownian price movements and zero arbitrage.
result The payoff from liquidity fees is a near-linear function of volatility.
A new high-frequency market making strategy using Deep Hawkes process.
problem Optimizing high-frequency trading in volatile markets.
method Developed a Deep Hawkes process to model order arrivals and their effects on the limit order book.
result The new strategy outperforms traditional methods in market making.
Optimal market making strategy for electronic markets with persistent order flows.
problem Market making on electronic markets with persistent order flows.
method Formulated as a stochastic control problem, characterized by viscosity solutions, and implemented numerically.
result Characterization of an optimal market making strategy.
This thesis applies RL to market making in China's commodity market.
problem Leverage RL for market making in China's commodity market.
method Developed an automatic trading system using RL.
result RL is feasible for market making in China's commodity market.
Market making is one of the most important aspects of algorithmic trading, and it has been studied quite extensively from a theoretical point of view. The practical implementation of so-called "optimal strategies" however suffers from the failure of most order book models to faithfully reproduce the behaviour of real m…
Modeling fees impacts on arbitrage profits and LP losses in AMMs.
problem Impact of trading fees on arbitrage profits and LP losses in AMMs.
method Extended model of AMMs with fees and Poisson block generation times, computed instantaneous rate of arbitrage profit.
result Fees scale down arbitrage profits, reducing LP losses with faster block rates and lower gas fees.
ARL and Hawkes processes improve market-making strategies with variable volatility.
problem Enhancing market-making strategies to adapt to varying volatility levels and self-exciting behaviors.
method Integrates ARL, Hawkes processes, and variable volatility levels; shifts from Poisson to Hawkes process.
result 4-action MM trained in low-volatility environment adapts to high-volatility conditions, providing stable performance.
Paper uses RL to optimize bid-ask spreads for diverse options.
problem Optimizing bid-ask spreads for options with various maturities and strikes.
method Combines stochastic policy with reinforcement learning.
result Proposes an effective approach for market making of options.
Deep Q-Learning optimizes market making by balancing price risk and spread profits.
problem Optimizing liquidity provision in financial markets.
method Reinforcement Learning applied to a market making problem with a reward function.
result Deep Q-Learning algorithms can recover the optimal market making strategy.
Modeling precious metals market making using nested Ornstein-Uhlenbeck processes.
problem Navigating liquidity provided by futures contracts in spot precious metals.
method Nested Ornstein-Uhlenbeck process for EFP spread modeling, Hamilton-Jacobi-Bellman equation approximation.
result Maximizing expected P&L while minimizing inventory risk in near real-time.
Paper uses RL for market making, improving stability in non-stationary markets.
problem Optimizing market making strategies in non-stationary limit order book dynamics.
method Reinforcement Learning (Proximal-Policy Optimization) applied to a simulator.
result RL agent outperforms closed-form optimal solution in non-stationary markets.
In most OTC markets, a small number of market makers provide liquidity to other market participants. More precisely, for a list of assets, they set prices at which they agree to buy and sell. Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money while …
The paper extends macroscopic market making to stochastic games, revealing properties and solving equations.
problem Price competition among market makers in a stochastic game setting.
method Extension of macroscopic market making framework to stochastic games, introducing multidimensional characteristic equations.
result New well-posedness results for forward-backward stochastic differential equations.
Maker-taker fees can prevent algorithmic cooperation in market making, but not always.
problem Unexpected cooperation among independent algorithms in market making.
method Modeling market making as a repeated game, experimental analysis of transaction costs and rebates.
result Maker-taker fee models can destabilize cooperation, but not always with a specific relationship between costs and rebates.
Paper uses reinforcement learning to optimize bid-ask spreads in OTC markets.
problem Optimizing bid-ask spreads in over-the-counter markets with dynamic order sizes.
method Reinforcement learning to solve high-dimensional stochastic control problem.
result Optimal bid-ask spreads follow a Gaussian distribution under certain conditions.
A learning algorithm achieves logarithmic regret in a market making model.
problem Learning the price sensitivity parameter in a market making model.
method Maximum-likelihood estimator with regularization, based on HJB equation.
result Regret upper bound of order ln^2 T in expectation.
This paper offers a framework for FX dealers to decide between internalizing and externalizing their market making to balance risk control and costs.
problem FX dealers face risk from flow uncertainty and need to decide on internalization vs. externalization strategies.
method Develops an optimal control framework that balances pricing and hedging strategies.
result Provides insights into the trade-off between risk control and transaction costs in market making.
This paper introduces a new market making approach using scaled beta distributions.
problem Inventory management challenges faced by market makers.
method Scaled beta distribution policies for flexible market making actions.
result Flexibility in volume distribution across price intervals improves market making performance.
Modeling option market making with hedging-induced price impact.
problem Tackles the challenge of market making in options markets with price impact.
method Models option order flow using Cox processes and studies the dynamics of inventory and price under hedging-induced impact.
result Establishes the well-posedness of the mixed control problem involving quoting and hedging.
In this paper, reinforcement learning is applied to the problem of optimizing market making. A multi-agent reinforcement learning framework is used to optimally place limit orders that lead to successful trades. The framework consists of two agents. The macro-agent optimizes on making the decision to buy, sell, or hold…