This document proposes using data-driven deep learning models to design agents for artificial market simulations. It aims to make simulations more realistic by basing agent behavior on actual high-frequency trader data from the Tokyo Stock Exchange. Several deep learning models are trained to predict traders' next orders and replace the traditional agent model in simulations. While some deep learning models perform better at order prediction, traditional models still yield better simulation results due to overfitting on the training data. The number of model parameters affects this, as more complex neural networks generalize poorly to out-of-sample simulation conditions. Overall, data-driven agent modeling shows promise but requires further refinement to match actual trader behavior in simulations.

1. Data-driven Agent Design for
Artificial Market Simulation
Masanori HIRANO, Kiyoshi IZUMI, Hiroki SAKAJI
Izumi Lab.
School of Engineering, The University of Tokyo
hirano@g.ecc.u-tokyo.ac.jp
https://mhirano.jp/

2. ©M.HIRANO & Izumi Lab.
Motivation
• Instability in Financial Markets
• 2008 financial crisis
• Flush Crush
• Price fluctuation by COVID-19
• Regulations are necessary
• New regulations like Basel III
• Can avoid abovementioned crisis?
• Difficulties in Financial markets
• Nonstationary
• Rare phenomena happen frequently
• => Simulation is a good solution, but not trustable.
• Find what’s the matter
• Dealing with trustability with actual data
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DJIA on May 6, 2010 (Flush Crush)
DJIA in 2020 (COVID-19)
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3. ©M.HIRANO & Izumi Lab.
Artificial Market Simulation
• Data-Driven<=> Model-Driven
• Simulations on computer using virtual markets
• We can test hypothetical situations!
• Promising approach for financial market analysis
• But…
• Models are human-made
• Humans can overpass key features for model
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Are the simulations realistic?
=> Model based on data is needed

4. ©M.HIRANO & Izumi Lab.
Our work
• Comparing
Traditional model  Our new model w/ data
• Only focus on HFT-MM  Specific trader & strategy
• Target: Tokyo Stock Exchange
• We analyzed a special data
provided by JPX
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Tokyo Stock Exchange

5. ©M.HIRANO & Izumi Lab.
Data
• “Order-book reproduction data”
provided by Japan Exchange Group (JPX)
• Containing masked trader information
<- Called “Virtual Server (VS)”
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Time Ticker Kind Buy/sell VS Price
11:11:50.702813 A Limit Order sell VS1 2570
11:11:50.703600 B Executed buy VS4 Market Order
11:11:50.704001 A Cancel sell VS1 2570
Sample
Some columns are not shown such as volume
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What’s the HFT-MM?
• High-Frequency-Trader Market-Making strategy
• Market-making strategy:
• (Basically) order near the best price
• Get profit by the spread (1001-999=2)
• Do repeatedly
• Risk-hedge by high-frequency-trade:
• Always have price move risk (Price move >> spread)
• Do action faster & hedge risk by setting off their inventory
• The reason we focused on this strategy:
• Easy to identify in the ordering data because of the unique behavior
• Well-known simulation model exists
• We cannot handle all data because of computational limitation
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Sell
Buy
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Extraction extension of [Uno et al. 18]
1. Filter by actions per ticker (HFT extraction)
2. Calculate indices corresponding to HFT-MM
• Actions per ticker (HFT-MM publish many orders)
• Inventory ratio (HFT-MM hardly doesn’t have inventories)
• Execution ratio (HFT-MM has low execution ratio)
• Cancel ratio (HFT-MM publish many cancel orders)
• Market order ratio (HFT-MM doesn’t use market order)
• Averaged traded ticker per VS(HFT-MM uses many VS)
3. Hierarchical clustering
=> extracted one HFT-MM cluster
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Jun Uno, Keiichi Goshima, and Reiko Tobe. Cluster Analysis of Trading Behavior: An Attempt to Extract HFT [in Japanese]. In The 12th
Annual Conference of Japanese Association of Behavioral Economics and Finance, 2018.
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8. ©M.HIRANO & Izumi Lab.
Simulation Model
• Stylized agent: based on [Chiarella 2002]
• 3 factor model: Fundamental, Chartist, Noise
• HFT-MM traditional agent: based on [Avellaneda 2008]
• Modeled as optimized solution for MM
• This algorithm is also used in actual financial market
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CHIARELLA, C. & Iori, G. (2002). A simulation analysis of the microstructure of double auction markets. Quantitative
Finance, 2(5), 346–353.
AVELLANEDA, M. & Stoikov, S. (2008). High-frequency trading in a limit order book. Quantitative Finance, 8(3), 217–224.
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9. ©M.HIRANO & Izumi Lab.
Comparison of ordering behavior
• Actual data vs simulation data
• Feature of HFT-MM: How many ticks far from the best
prices their orders are placed in
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Masanori HIRANO, Kiyoshi IZUMI, Hiroyasu MATSUSHIMA, and Hiroki SAKAJI, "Comparing Actual and Simulated HFT Traders' Behavior for Agent
Design,“ Journal of Artificial Societies and Social Simulation, University of Surry, Vol.23, No.3, e6, 2020, doi.org/10.18564/jasss.4304
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10. ©M.HIRANO & Izumi Lab.
Replace Traditional Agents by DL-based model
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Next Order Pred.
Using Actual Data
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Experiments
• Change HFT-MM model among a traditional one and some
DL-based ones
1. Learning test: How well DL-based model can predict the actual
next order?
2. Simulation test: How well HFT-MM model can work like the
actual HFT-MM trader in simulations
• Criteria: MSE & KLD of order position probability from the
best prices
• Lower scores mean better performance
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Test results in Simulation
• BaseLineModel outperform at best and traditional model
• Better trained model=>Worse in simulation
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Traditional Model
2 outperforming models
Each dot represents each DL-based model
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Reason: # of model parameters
• Simulation could be out-of-data(OOD) sample
• Model w/ more parameters show weak to OOD
• Reason1: simulation is not refined enough
• Reason2: data is very specific to Tokyo Stock Exchange
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• More parameters
=> Trained better
• More parameter
=> Worse simulation
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Conclusion
• We proposed a trader model based on DL and actual data.
• Some of the proposed models show better performance.
• This suggests that DL-based agent using actual data is one
promising solution for MAS.
• # of parameters in DL model is important to avoid degrading
performance in simulations
• The performance in learning and the performance in simulation
show the reverse relationship
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Future Work
• Other agents than HFT-MM should be also modeled by DL
• Search for the best parameter size and architecture
• Auto ML tuning for this task
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