An Algorithmic Trading Approach Merging Machine Learning With Multi-Indicator Strategies for Optimal Performance

Abstract

This study investigates the integration of machine learning techniques with multi-indicator strategies in algorithmic trading to overcome the limitations of traditional trading methods. As financial markets become increasingly complex and volatile, innovative approaches are essential to improve predictive accuracy and adaptability. This research aims to develop an algorithmic trading framework that dynamically selects relevant indicators to optimize trading performance. Researchers propose a flexible and computationally efficient model that takes advantage of advanced machine learning algorithms alongside multiple technical indicators, designed to adapt to changing market conditions. An empirical analysis evaluates the effectiveness of this approach against traditional trading indicators. The results demonstrate that the proposed algorithm achieved an impressive total return of 901%, significantly outperforming traditional indicators such as Moving Average (MA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, On-Balance Volume (OBV), and Ichimoku. Furthermore, the model maintained a high win rate during the backtesting, showcasing its robustness under various market conditions. These findings highlight the potential of hybrid strategies that combine machine learning with technical analysis. This research emphasizes the need for adaptive trading models that can respond to the dynamic nature of financial markets. The integration of machine learning with multi-indicator strategies is shown to enhance trading performance. Future work should focus on improving the interpretability of the model and exploring diverse datasets to further enhance the effectiveness of algorithmic trading methodologies.

Keywords

• Undervalued stocks
• algorithmic approaches
• machine learning
• predictive modeling
• financial markets