This paper develops a machine learning framework for detecting and predicting liquidity sweep events in XAUUSD using event-based market microstructure analysis. Using 15-minute data from 2014–2024, the study formalizes liquidity sweeps as a binary classification problem evaluated through walk-forward validation. A calibrated Random Forest classifier demonstrates statistically meaningful predictive structure while also revealing a significant prediction–execution gap: predictive accuracy does not necessarily translate into profitable trading performance under naive execution. The work contributes to quantitative finance, market microstructure research, and machine learning-based financial forecasting.
The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper…
Respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, bronchiectasis, bronchiolitis and upper respiratory tract infection (URTI) remain among the leading causes of illness and death worldwide. Conventional diagnosis relies heavily on auscul…
This comprehensive study constructs an advanced predictive analytics framework leveraging machine learning algorithms to forecast undergraduate academic performance within higher education institutions. Utilizing empirical student data from a technical institute, including Learni…
Executive Summary RAQA-Weather is a comprehensive weather intelligence system designed for Iran that combines real-time web scraping, machine learning-based spatial prediction, and interactive visualization. The system processes data from 17 synoptic stations across Iran and gene…