Training Set Optimization for Machine Learning in Day Trading: A New Financial Indicator

Predicting and trading assets in the global financial market represents a complex challenge driven by the dynamic and volatile nature of the sector. This study proposes a day trading strategy that optimizes asset purchase and sale parameters using differential evolution. To this end, an innovative financial indicator was developed, and machine learning models were employed to improve returns. The work highlights the importance of optimizing training sets for machine learning algorithms based on probable asset behaviors (scenarios), which allows the development of a robust model for day trading. The empirical results demonstrate that the LSTM algorithm excelled, achieving approximately 98% higher returns and an 82% reduction in DrawDown compared to asset variation. The proposed indicator tracks asset fluctuation with comparable gains and exhibits lower variability in returns, offering a significant advantage in risk management. The strategy proves to be adaptable to periods of turbulence and economic changes, which is crucial in emerging and volatile markets.