Abstract This study investigates the use of state-of-the-art software tools available on contemporary desktop computing platforms to enhance predictive modeling with machine learning methods. Existing research has not sufficiently examined how efficient utilization of such tools—specifically state-space search reduction, operation parallelization, and mechanisms for escaping local optima—affects model performance when applied to large-scale high-frequency datasets. To address this gap, we introduce new predictive models that explicitly leverage these advanced software capabilities. We further propose strategies for overcoming local optima in neural-network training and for parameter tuning in population-based metaheuristic algorithms used for forecasting high-frequency financial data. Empirical evaluation is conducted on one-minute EUR/CZK exchange rate data from 2018 and on 17 high-frequency Amazon stock price datasets spanning 2005–2021. The results demonstrate that incorporating modern software optimization tools not only improves predictive accuracy but also significantly reduces computation time, making the approach well-suited for real-time forecasting of highly dynamic financial time series