A novel artificial intelligence model for financial market prediction and algorithmic trading

The financial sector has experienced transformative advancements, particularly in predictive analytics and algorithmic trading. While advanced models excel at making accurate predictions and sound decisions, many existing systems struggle to adapt to dynamic market conditions. This research exploits a novel application named the Dynamic Wild Horse-mutated Resilient Random Forest Classifier (DWH-RRFC), aiming at achieving superior accuracy in trend prediction of algorithmic trading systems. The research utilizes a multimodal data including historical prices, market sentiment scores, trading volumes, and macroeconomic indexes from major cryptocurrency markets. Z-score normalization scales input features such as price, volume, and market-related features to all have a mean equal to zero and a standard deviation equal to one during preprocessing. The presence of dimensionality reduction methods like PCA helps in minimizing information loss during feature selection. The DWH-RRFC adds a mutation mechanism inspired by the Wild Horse model, which refines the adaptability of traditional random forest algorithms. Allowing for dynamic boundary modifications to accurately respond to changing market circumstances. The model specializes in forecasting the short-term directional movement of prices. Consequently, traders can make precise and reasonable acquisitions and sales investments on the market's future performance. The experiment showings indicate that the system may work with incredible performance, about 97 percent in accuracy, F1-score, precision, and recall, toward commercial conventional classifiers. This paradigm constitutes a revolutionary breakthrough in the field of fiscal budgeting and creates a foundation for increasingly sophisticated trading automation in active environments.