Predicting Bullish Reversals with LightGBM: A Systematic Trading Strategy for Brazilian Equities - Alves, Wiliams; Callou, Gustavo (Anais do Seminário Integrado de Software e Hardware (SEMISH), 2026)
Predicting stock market behavior is a complex challenge. This article presents an automated trading system based on machine learning, focused on identifying bullish reversals in the Brazilian financial market. The methodology combines technical and sectoral indicators, the LightGBM classifier, signal post-processing with non-maximum suppression, and backtesting with ATR-based risk management. Evaluated on assets representing different sectors of the economy (ABEV3, GGBR4, ITUB4, and VIVT3), the approach achieved PR-AUC values ranging from 0.955 to 0.979, win rates between 54.8% and 78.6%, and average returns per trade between 2.20% and 5.08%, consistently outperforming the buy-and-hold benchmark.