Optimal market making in the presence of latency

Gao, Xuefeng; Wang, Yunhan

doi:10.1080/14697688.2020.1741670

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…Then for any point (t, s, q) in the grid, we approximate sup ∈L E (1 − ρ)(S t,s t− + Z)f ( − S t,s t− − Z) + h 0 t, ˜ (S t,s t− , Z, q, ) 1 {t≤T } (A. 18) together with the control * ∈ L that attains the supremum in (A.18), which we obtain as follows. Split the set L into intervals of size 10 −5 considering 100 ticks above and 100 ticks below S t = s -considering more ticks is not necessary because the probability that {|S t − s| > 100 × 10 −5 } is negligible.…”

Section: A3 a Note About Numerical Approximationsmentioning

confidence: 99%

“…Cartea and Sánchez-Betancourt [15] show how traders can adjust the price limit of MLOs to target a fill ratio in a given currency pair when the trader sends a sequence of orders over a trading interval (finite or infinite). In market making, Gao and Wang [18] show how an agent provides liquidity to the LOB of large-tick stocks when latency is deterministic and fixed during the trading interval. In passive trading (i.e., trading with limit orders), the work by Moallemi and Saĝlam [24] quantifies the cost of latency in equity (NASDAQ).…”

Section: Introductionmentioning

confidence: 99%

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Optimal execution with stochastic delay

Cartea

Sánchez-Betancourt

2022

Finance Stoch

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We show how traders use marketable limit orders (MLOs) to liquidate a position over a trading window when there is latency in the marketplace. MLOs are liquidity-taking orders that specify a price limit and are for immediate execution only; however, if the price limit of the MLO precludes it from being filled, the exchange cancels the order. We frame our model as an impulse control problem with stochastic latency where the trader controls the times and the price limits of the MLOs sent to the exchange. We show that impatient liquidity takers submit MLOs that may walk the book (capped by the limit price) to increase the probability of filling the trades. On the other hand, patient liquidity takers use speculative MLOs that are only filled if there has been an advantageous move in prices over the latency period. Patient traders who are fast do not use their speed to hit the quotes they observe, or to finish the execution programme early; they use speed to complete the execution programme with as many speculative MLOs as possible. We use foreign exchange data to implement the random-latency-optimal strategy and to compare it with four benchmarks. For patient traders, the random-latency-optimal strategy outperforms the benchmarks by an amount that is greater than the transaction costs paid by liquidity takers in foreign exchange markets. Around news announcements, the value of the outperformance is between two and ten times the value of the transaction costs. The superiority of the strategy is due to both the speculative MLOs that are filled and the price protection of the MLOs.

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Section: A3 a Note About Numerical Approximationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal execution with stochastic delay

Cartea

Sánchez-Betancourt

2022

Finance Stoch

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“…On a related note, Guéant et al [4] consider a variant of the AS model with inventory limits and derive asymptotic approximations to the optimal quotes in the limit T → ∞, whereas in [5] the model is extended by considering both non-martingale mid-price dynamics and inventory limits. More sophisticated analytical MM approaches, all more or less in the spirit of the original AS model, include additional features such as: stochastic market spreads [6], trading via market orders [6], interdependence in mid-price moves and limit order execution probabilities via mutually exciting Hawkes processes [7], short-term alpha trend dynamics [7], model uncertainty [8], latency [33], and LOB signals [9].…”

Section: A Related Workmentioning

confidence: 99%

Market Making With Signals Through Deep Reinforcement Learning

Gasperov

Kostanjčar

2021

IEEE Access

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Deep reinforcement learning has recently been successfully applied to a plethora of diverse and difficult sequential decision-making tasks, ranging from the Atari games to robotic motion control. Among the foremost such tasks in quantitative finance is the problem of optimal market making. Market making is the process of simultaneously quoting limit orders on both sides of the limit order book of a security with the goal of repeatedly capturing the quoted spread while minimizing the inventory risk. Most of the existing analytical approaches to market making tend to be predicated on a set of strong, naïve assumptions, whereas current machine learning-based approaches either resort to crudely discretized quotes or fail to incorporate additional predictive signals. In this paper, we present a novel framework for market making with signals based on model-free deep reinforcement learning, addressing these shortcomings. A new state space formulation incorporating outputs from standalone signal generating units, as well as a novel action space and reward function formulation, are introduced. The framework is underpinned by both ideas from adversarial reinforcement learning and neuroevolution. Experimental results on historical data demonstrate the superior reward-to-risk performance of the proposed framework over several standard market making benchmarks. More specifically, the resulting reinforcement learning agent achieves between 20-30% higher terminal wealth than the benchmarks while being exposed to only around 60% of their inventory risks. Finally, an insight into its policy is provided for the sake of interpretability.INDEX TERMS Deep reinforcement learning, genetic algorithms, high-frequency trading, machine learning, market making, stochastic control.

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“…In market making, Gao and Wang (2020) show how an agent provides liquidity to the LOB of large-tick stocks when latency is deterministic and fixed during the trading horizon. In their model, the market maker may at each discrete time step (i) cancel past orders, (ii) update the limit prices of current orders, or (iii) do nothing.…”

Section: Previous Work On Latencymentioning

confidence: 99%