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Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market. / le Calvez, Arthur; Cliff, Dave.

2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018): Proceedings of a meeting held 18-21 November 2018, Bangalore, India. ed. / Suresh Sundaram. Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 1876-1883 8628854.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

le Calvez, A & Cliff, D 2019, Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market. in S Sundaram (ed.), 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018): Proceedings of a meeting held 18-21 November 2018, Bangalore, India., 8628854, Institute of Electrical and Electronics Engineers (IEEE), pp. 1876-1883, 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, 18/11/18. https://doi.org/10.1109/SSCI.2018.8628854

APA

le Calvez, A., & Cliff, D. (2019). Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market. In S. Sundaram (Ed.), 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018): Proceedings of a meeting held 18-21 November 2018, Bangalore, India (pp. 1876-1883). [8628854] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SSCI.2018.8628854

Vancouver

le Calvez A, Cliff D. Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market. In Sundaram S, editor, 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018): Proceedings of a meeting held 18-21 November 2018, Bangalore, India. Institute of Electrical and Electronics Engineers (IEEE). 2019. p. 1876-1883. 8628854 https://doi.org/10.1109/SSCI.2018.8628854

Author

le Calvez, Arthur ; Cliff, Dave. / Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market. 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018): Proceedings of a meeting held 18-21 November 2018, Bangalore, India. editor / Suresh Sundaram. Institute of Electrical and Electronics Engineers (IEEE), 2019. pp. 1876-1883

Bibtex

@inproceedings{b15c4fc0ae79407093e302a77e547779,
title = "Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market",
abstract = "Here we report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.",
keywords = "Financial Engineering, Financial Markets, Automated Trading, Intelligent Agents, Deep Learning",
author = "{le Calvez}, Arthur and Dave Cliff",
year = "2019",
month = "1",
day = "28",
doi = "10.1109/SSCI.2018.8628854",
language = "English",
isbn = "9781538692776",
pages = "1876--1883",
editor = "Suresh Sundaram",
booktitle = "2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018)",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
address = "United States",

}

RIS - suitable for import to EndNote

TY - GEN

T1 - Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

AU - le Calvez, Arthur

AU - Cliff, Dave

PY - 2019/1/28

Y1 - 2019/1/28

N2 - Here we report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.

AB - Here we report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.

KW - Financial Engineering

KW - Financial Markets

KW - Automated Trading

KW - Intelligent Agents

KW - Deep Learning

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U2 - 10.1109/SSCI.2018.8628854

DO - 10.1109/SSCI.2018.8628854

M3 - Conference contribution

AN - SCOPUS:85056716300

SN - 9781538692776

SP - 1876

EP - 1883

BT - 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018)

A2 - Sundaram, Suresh

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -