ML Anthology

Evology: An Empirically-Calibrated Market Ecology Agent-Based Model for Trading Strategy Search

Aymeric Vie, Maarten Peter Scholl, Doyne James Farmer
ICMLW 2022
/icmlw/2022/vie2022icmlw-evology/

Abstract

Market ecology views financial markets as ecosystems of diverse, interacting and evolving trading strategies. We present a heterogeneous, empirically calibrated multi-agent market ecology agent-based model. We outline its potential as a valuable and challenging training ground for optimising trading strategies using machine learning algorithms and defining research tasks.

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Cite

Text

Vie et al. "Evology: An Empirically-Calibrated Market Ecology Agent-Based Model for Trading Strategy Search." ICML 2022 Workshops: AI4ABM, 2022.

Markdown

[Vie et al. "Evology: An Empirically-Calibrated Market Ecology Agent-Based Model for Trading Strategy Search." ICML 2022 Workshops: AI4ABM, 2022.](https://mlanthology.org/icmlw/2022/vie2022icmlw-evology/)

BibTeX

@inproceedings{vie2022icmlw-evology,
  title     = {{Evology: An Empirically-Calibrated Market Ecology Agent-Based Model for Trading Strategy Search}},
  author    = {Vie, Aymeric and Scholl, Maarten Peter and Farmer, Doyne James},
  booktitle = {ICML 2022 Workshops: AI4ABM},
  year      = {2022},
  url       = {https://mlanthology.org/icmlw/2022/vie2022icmlw-evology/}
}