kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2026-23-3-600-616kaz29-3220Research ArticleЭКОНОМИКА И БИЗНЕСECONOMY AND BUSINESSЧИСЛЕННАЯ МУЛЬТИАГЕНТНАЯ МОДЕЛЬ АДАПТИВНОЙ САМООРГАНИЗУЮЩЕЙСЯ ТОРГОВОЙ СИСТЕМЫA NUMERICAL MULTI-AGENT MODEL FOR AN ADAPTIVE SELF-ORGANIZING TRADING SYSTEMhttps://orcid.org/0000-0002-3379-9969АбдуллаевУ.AbdullaevU.

Докторант

Нукус

Doctoral Student

Nukus

aulmas248@gmail.comКаракалпакский научно-исследовательский институт естественных наукУзбекистанKarakalpak Scientific Research Institute of Natural SciencesUzbekistan202628092026233600616Copyright © Абдуллаев У., 20262026Абдуллаев У.Abdullaev U.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/3220

Цель исследования – разработать адаптивную математическую модель прогнозирования финансовых рынков, основанную на принципах самоорганизации. В исследовании представлена математическая модель самоорганизующейся торговой системы, адаптированная к экономическим условиям Республики Узбекистан. Для построения адаптивного механизма принятия торговых решений в условиях высокой волатильности, характерной для развивающихся рынков, применены методы мультиагентного моделирования, теория нелинейной динамики и спектральный анализ временных рядов. Математическая модель основана на системе стохастических дифференциальных уравнений, описывающих динамику формирования цен с учетом взаимодействия множества агентов. Для решения системы применен модифицированный метод Рунге–Кутты четвертого порядка, позволивший получить среднеквадратичную ошибку (RMSE) на уровне 0,0124 при однодневном прогнозировании, что на 15,6% превосходит результаты традиционных подходов. Разработанный алгоритм идентификации рыночных режимов, основанный на самоорганизующихся картах Кохонена, продемонстрировал точность 87,6% при классификации шести выявленных кластеров рыночных состояний. Это позволило эффективно адаптировать параметры системы к изменяющимся рыночным условиям и существенно снизить ошибки прогнозирования, при этом величина улучшения варьировалась в зависимости от рыночного режима. Торговые стратегии, построенные на основе предложенной модели, обеспечили годовую доходность 37,2% при коэффициенте Шарпа 1,86, существенно превысив результаты как пассивных инвестиционных стратегий, так и традиционных методов алгоритмической торговли. Преимущество предложенного подхода особенно заметно в условиях высокой волатильности, характерной для развивающихся рынков, где точность прогнозирования направления движения цен на однодневном горизонте достигает 72,8%. Практическая значимость полученных результатов заключается в разработке адаптивной торговой системы, способной повысить эффективность биржевой торговли и ликвидность рынков Узбекистана и других развивающихся экономик.

The purpose of the study is to develop an adaptive mathematical model for forecasting financial markets based on the Financial instruments exhibit the greatest sensitivity to trading volumes (β=0.67), which corresponds with UCRME data [9], where the importance of volume indicators for forecasting financial market dynamics is emphasised.principles of self-organisation. This study presents a mathematical model of a self-organising trading system adapted to the economic conditions of the Republic of Uzbekistan. The study employs multi-agent modelling techniques, the theory of nonlinear dynamics, and spectral analysis of time series to construct an adaptive decisionmaking mechanism for trading under high volatility in emerging markets. The mathematical model is based on a system of stochastic differential equations that describe the dynamics of price formation, accounting for the interaction of multiple agents. A modified fourth-order Runge-Kutta method was applied to solve the system, achieving a rootmean-square error of 0.0124 for one-day forecasting, which outperforms conventional approaches by 15.6%. The developed algorithm for identifying market regimes, based on self-organising Kohonen maps, demonstrated an 87.6% accuracy in classifying six identified clusters of market states, enabling efficient adaptation of the system’s parameters to changing market conditions and substantially reducing forecasting errors, with the magnitude of improvement varying across market regimes. Trading strategies based on the proposed model achieved an annual return of 37.2% with a Sharpe ratio of 1.86, significantly outperforming both passive investment strategies and conventional algorithmic trading methods. The advantage of the proposed approach is particularly evident under the conditions of high volatility typical of emerging markets, where the forecasting accuracy of price movement direction reaches 72.8% for a one-day horizon. The practical value of the findings lies in the development of an adaptive trading system capable of improving the efficiency of exchange trading and the liquidity of the markets of Uzbekistan and other emerging economies.

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The authors declare that there are no conflicts of interest present.