Standard
Adaptive Predictive Portfolio Management Agent. / Kolonin, Anton; Glushchenko, Alexey; Fokin, Arseniy et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2023. p. 187-196 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13921 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Kolonin, A, Glushchenko, A, Fokin, A, Mari, M, Casiraghi, M & Vishwas, M 2023,
Adaptive Predictive Portfolio Management Agent. in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13921 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 187-196.
https://doi.org/10.1007/978-3-031-33469-6_19
APA
Kolonin, A., Glushchenko, A., Fokin, A., Mari, M., Casiraghi, M., & Vishwas, M. (2023).
Adaptive Predictive Portfolio Management Agent. In
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 187-196). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13921 LNCS). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.1007/978-3-031-33469-6_19
Vancouver
Kolonin A, Glushchenko A, Fokin A, Mari M, Casiraghi M, Vishwas M.
Adaptive Predictive Portfolio Management Agent. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH. 2023. p. 187-196. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-33469-6_19
Author
Kolonin, Anton ; Glushchenko, Alexey ; Fokin, Arseniy et al. /
Adaptive Predictive Portfolio Management Agent. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2023. pp. 187-196 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{70defc14e4054a5fa1ff7613d3e758aa,
title = "Adaptive Predictive Portfolio Management Agent",
abstract = "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.",
keywords = "Active Portfolio Management, Adaptive Agent, Backtesting, Crypto-Market, Experiential Learning, Limit Order Book, Market-Making, Multi-Agent Simulation, Narrow AGI, Price Prediction",
author = "Anton Kolonin and Alexey Glushchenko and Arseniy Fokin and Marcello Mari and Mario Casiraghi and Mukul Vishwas",
note = "Публикация для корректировки.",
year = "2023",
doi = "10.1007/978-3-031-33469-6_19",
language = "English",
isbn = "9783031334689",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "187--196",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}
RIS
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 -
