TY - GEN

T1 - Adaptive Multi-strategy Market-Making Agent for Volatile Markets

AU - Raheman, Ali

AU - Kolonin, Anton

AU - Glushchenko, Alexey

AU - Fokin, Arseniy

AU - Ansari, Ikram

PY - 2023

Y1 - 2023

N2 - Crypto-currency market uncertainty drives the need to find adaptive solutions to maximize gain or at least to avoid loss throughout the periods of trading activity. Given the high dimensionality and complexity of the state-action space in this domain, it can be treated as a “Narrow AGI” problem with the scope of goals and environments bound to financial markets. Adaptive Multi-Strategy Agent approach for market-making introduces a new solution to maximize positive “alpha” in long-term handling limit order book (LOB) positions by using multiple sub-agents implementing different strategies with a dynamic selection of these agents based on changing market conditions. AMSA provides no specific strategy of its own while being responsible for segmenting the periods of market-making activity into smaller execution sub-periods, performing internal backtesting on historical data on each of the sub-periods, doing sub-agent performance evaluation and re-selection of them at the end of each sub-period, and collecting returns and losses incrementally. With this approach, the return becomes a function of hyperparameters such as market data granularity (refresh rate), the execution sub-period duration, number of active sub-agents, and their individual strategies. Sub-agent selection for the next trading sub-period is made based on return/loss and alpha values obtained during internal backtesting as well as real trading. Experiments with the AMSA have been performed under different market conditions relying on historical data and proved a high probability of positive alpha throughout the periods of trading activity in the case of properly selected hyperparameters.

AB - Crypto-currency market uncertainty drives the need to find adaptive solutions to maximize gain or at least to avoid loss throughout the periods of trading activity. Given the high dimensionality and complexity of the state-action space in this domain, it can be treated as a “Narrow AGI” problem with the scope of goals and environments bound to financial markets. Adaptive Multi-Strategy Agent approach for market-making introduces a new solution to maximize positive “alpha” in long-term handling limit order book (LOB) positions by using multiple sub-agents implementing different strategies with a dynamic selection of these agents based on changing market conditions. AMSA provides no specific strategy of its own while being responsible for segmenting the periods of market-making activity into smaller execution sub-periods, performing internal backtesting on historical data on each of the sub-periods, doing sub-agent performance evaluation and re-selection of them at the end of each sub-period, and collecting returns and losses incrementally. With this approach, the return becomes a function of hyperparameters such as market data granularity (refresh rate), the execution sub-period duration, number of active sub-agents, and their individual strategies. Sub-agent selection for the next trading sub-period is made based on return/loss and alpha values obtained during internal backtesting as well as real trading. Experiments with the AMSA have been performed under different market conditions relying on historical data and proved a high probability of positive alpha throughout the periods of trading activity in the case of properly selected hyperparameters.

KW - Adaptive agent

KW - Limit order book

KW - Market making

KW - Narrow AGI

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85148694372&origin=inward&txGid=cca458263d58feafb447910dc9f07717

UR - https://www.mendeley.com/catalogue/dbfc4ae2-4cf4-32ad-966a-006d703c049f/

U2 - 10.1007/978-3-031-19907-3_24

DO - 10.1007/978-3-031-19907-3_24

M3 - Conference contribution

SN - 9783031199066

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 250

EP - 259

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Science and Business Media Deutschland GmbH

ER -