TY - GEN

T1 - Adaptive Predictive Portfolio Management Agent

AU - Kolonin, Anton

AU - Glushchenko, Alexey

AU - Fokin, Arseniy

AU - Mari, Marcello

AU - Casiraghi, Mario

AU - Vishwas, Mukul

N1 - Публикация для корректировки.

PY - 2023

Y1 - 2023

N2 - The paper presents an advanced version of an adaptive market-making agent capable of performing experiential learning, exploiting a “try and fail” approach relying on a swarm of subordinate agents executed in a virtual environment to determine optimal strategies. The problem is treated as a “Narrow AGI” problem with the scope of goals and environments bound to financial markets, specifically crypto-markets. Such an agent is called an “adaptive multi-strategy agent” as it executes multiple strategies virtually and selects only a few for real execution. The presented version of the agent is extended to solve portfolio optimization and re-balancing across multiple assets so the problem of active portfolio management is being addressed. Also, an attempt is made to apply an experiential learning approach executed in the virtual environment of multi-agent simulation and backtesting based on historical market data, so the agent can learn mappings between specific market conditions and optimal strategies corresponding to these conditions. Additionally, the agent is equipped with the capacity to predict price movements based on social media data, which increases its financial performance.

AB - The paper presents an advanced version of an adaptive market-making agent capable of performing experiential learning, exploiting a “try and fail” approach relying on a swarm of subordinate agents executed in a virtual environment to determine optimal strategies. The problem is treated as a “Narrow AGI” problem with the scope of goals and environments bound to financial markets, specifically crypto-markets. Such an agent is called an “adaptive multi-strategy agent” as it executes multiple strategies virtually and selects only a few for real execution. The presented version of the agent is extended to solve portfolio optimization and re-balancing across multiple assets so the problem of active portfolio management is being addressed. Also, an attempt is made to apply an experiential learning approach executed in the virtual environment of multi-agent simulation and backtesting based on historical market data, so the agent can learn mappings between specific market conditions and optimal strategies corresponding to these conditions. Additionally, the agent is equipped with the capacity to predict price movements based on social media data, which increases its financial performance.

KW - Active Portfolio Management

KW - Adaptive Agent

KW - Backtesting

KW - Crypto-Market

KW - Experiential Learning

KW - Limit Order Book

KW - Market-Making

KW - Multi-Agent Simulation

KW - Narrow AGI

KW - Price Prediction

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85163388379&origin=inward&txGid=4cd2f253259c63d0fd19dced89c4e1ed

UR - https://www.mendeley.com/catalogue/36bf60dc-1de5-3d50-a852-b7697237c1f3/

U2 - 10.1007/978-3-031-33469-6_19

DO - 10.1007/978-3-031-33469-6_19

M3 - Conference contribution

SN - 9783031334689

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

SP - 187

EP - 196

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 -