A gate-based AI-driven decision framework for cryptocurrency derivative investment: mathematical modeling and computational implementation for exchange risk management

Purpose – The innovative crypto-world represents one of the most prominent manifestations of volatile, uncertain, complex and ambiguous (VUCA) environments. In this context, the use of technology plays a fundamental role in ensuring the survival of firms operating within the business environment. Recently, the cryptocurrency derivatives market reached a monthly trading volume of $1.33 trillion, exceeding traditional spot markets and creating unprecedented challenges for digital asset exchanges in managing investment decisions. This paper presents a novel gate-based artificial intelligence (AI) framework for optimizing cryptocurrency derivative investment strategies in crypto-exchange companies’ operations through the implementation of new technology-based solutions. Design/methodology/approach – The paper is supported by the quantitative methodology of system dynamics (SD). This method allows the development of a mathematical model that integrates sentiment analysis with technical momentum indicators through a cascade gate system. The mathematical formulation includes stochastic differential equations for price dynamics, Bayesian inference for sentiment analysis and multi- objective optimization for risk management. Monte Carlo simulations demonstrate the framework’s robustness across different market conditions, with Sharpe ratios consistently above 1.8. Findings – The framework returns a complex decision-making output in order to reach an optimal choices pathway for derivative investments. The framework matches leading indicators relative strength index, lagging indicators (moving averages, moving average convergence/divergence [MACD]) and volume indicators on- balance volume with real-time sentiment analysis. Our computational implementation uses historical data from major cryptocurrency exchanges (2020–2024) to validate the theoretical model, achieving risk-adjusted returns of 23.7% annually with maximum drawdown limited to 8.2%.