Comparing Actual and Simulated HFT Traders' Behavior for Agent Design

Hirano, Minoru; Izumi, Kiyoshi; Matsushima, Hiroyasu; Sakaji, Hiroki

doi:10.18564/jasss.4304

Cited by 12 publications

(6 citation statements)

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“…The agent prefers to select buy and sell limit orders with price difference 4 and 6 and volume 5 while limit orders with price difference 0 and volume 5, market orders with volume 5, and cancel orders were rarely selected. Previous studies about high-frequency-trader and market making indicate that traders in real markets have similar investment strategies to our LV-DRL model (Hirano et al 2019). On the other hand, since the reward function of the CO-DRL model does not consider the inventory of the agent, the CO-DRL model seems to pursue a momentum-trend-following strategy without much regard for risk.…”

Section: Resultssupporting

confidence: 58%

Deep Reinforcement Learning in Agent Based Financial Market Simulation

Maeda

deGraw²,

Kitano

et al. 2020

JRFM

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Prediction of financial market data with deep learning models has achieved some level of recent success. However, historical financial data suffer from an unknowable state space, limited observations, and the inability to model the impact of your own actions on the market can often be prohibitive when trying to find investment strategies using deep reinforcement learning. One way to overcome these limitations is to augment real market data with agent based artificial market simulation. Artificial market simulations designed to reproduce realistic market features may be used to create unobserved market states, to model the impact of your own investment actions on the market itself, and train models with as much data as necessary. In this study we propose a framework for training deep reinforcement learning models in agent based artificial price-order-book simulations that yield non-trivial policies under diverse conditions with market impact. Our simulations confirm that the proposed deep reinforcement learning model with unique task-specific reward function was able to learn a robust investment strategy with an attractive risk-return profile.

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Section: Resultssupporting

confidence: 58%

Deep Reinforcement Learning in Agent Based Financial Market Simulation

Maeda

deGraw²,

Kitano

et al. 2020

JRFM

Self Cite

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“…Next, we will explain the HFT agent model in detail. Among the market making strategy models that have been proposed [51], [52], [55], we modeled the HFT agent on the basis of Kusada et al [52] for simplicity. There is only one HFT agent.…”

Section: Artificial Market Modelmentioning

confidence: 99%

“…As has been noted, the validation of artificial market models with actual data requires much work, and it would be a separate research project [35], [50], [51]. LeBaron [50] and Chen et al [35] investigated the validation of previous artificial market models by changing various factors in the models.…”

Section: Introductionmentioning

confidence: 99%

“…LeBaron [50] and Chen et al [35] investigated the validation of previous artificial market models by changing various factors in the models. Hirano et al [51] proposed an HFT model for an artificial market, compared the simulation results obtained from their model with data that seemed to be HFT transactions exacted from actual data, and found some inconsistencies between the two. Solving this problem will take more time because it is necessary to check whether the extracted data are really only HFT transactions, whether the HFT model is inappropriate, etc.…”

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confidence: 99%

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Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity

Yagi

Masuda

Mizuta

2020

IEEE Trans. Comput. Soc. Syst.

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Many empirical studies have discussed market liquidity, which is regarded as a measure of a booming financial market. Further, various indicators for objectively evaluating market liquidity have also been proposed and their merits have been discussed. In recent years, the impact of high-frequency traders (HFTs) on financial markets has been a focal concern, but no studies have systematically discussed their relationship with major market liquidity indicators, including volume, tightness, resiliency, and depth. In this study, we used agent-based simulations to compare the major liquidity indicators in an artificial market where an HFT participated was compared to one where no HFT participated. The results showed that all liquidity indicators in the market where an HFT participated improved more than those in the market where no HFT participated. Furthermore, as a result of investigating the correlations between the major liquidity indicators in our simulations and the extant empirical literature, we found that market liquidity can be measured not only by the major liquidity indicators but also by execution rate. Therefore, it is suggested that it could be appropriate to employ execution rate as a novel liquidity indicator in future studies.

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“…Furthermore, this dataset includes virtual server IDs, a unit of trading accounts on the TSE. Although the virtual server ID is not technically equivalent to the membership ID, we can effectively define trader IDs to track individual trader behaviour with high resolution by appropriately aggregating virtual server IDs [10,11]; see also [9] for more technical details.…”

mentioning

confidence: 99%

Direct quantitative evidence of the order-splitting hypothesis as the microscopic origin of long-range correlations in market order flow

Yuki¹,

Kanazawa²

2023

Preprint

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In financial markets, the market order sign exhibits strong persistence, widely known as the long-range correlation (LRC) of order flow; specifically, the sign correlation function displays long memory with power-law exponent γ, such that C(τ ) ∝ τ −γ for large time-lag τ . One of the most promising microscopic hypotheses is the order-splitting behaviour at the level of individual traders. Indeed, Lillo, Mike, and Farmer (LMF) introduced in 2005 a simple microscopic model of order-splitting behaviour, which predicts that the macroscopic sign correlation is quantitatively associated with the microscopic distribution of metaorders. While this hypothesis has been a central issue of debate in econophysics, its direct quantitative validation has been missing because it requires large microscopic datasets with high resolution to observe the order-splitting behaviour of all individual traders. Here we present the first quantitative validation of this LFM prediction by analysing a large microscopic dataset in the Tokyo Stock Exchange market for more than nine years. On classifying all traders as either order-splitting traders or random traders as a statistical clustering, we directly measured the metaorder-length distributions P (L) ∝ L −α−1 as the microscopic parameter of the LMF model and examined the theoretical prediction on the macroscopic order correlation: γ ≈ α − 1. We discover that the LMF prediction agrees with the actual data even at the quantitative level. Our work provides the first solid support of the microscopic model and solves directly a long-standing problem in the field of econophysics and market microstructure.

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Comparing Actual and Simulated HFT Traders' Behavior for Agent Design

Cited by 12 publications

References 0 publications

Deep Reinforcement Learning in Agent Based Financial Market Simulation

Deep Reinforcement Learning in Agent Based Financial Market Simulation

Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity

Direct quantitative evidence of the order-splitting hypothesis as the microscopic origin of long-range correlations in market order flow

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