Intelligence-driven Quantitative Trading Strategies and Practices

Abstract

 Modern financial markets generate high-volume and high-speed data. Although machine learning has improved prediction and decision-making, guidelines for implementation considering costs, governance, and robustness against regime changes remain limited. This study comprehensively summarizes how supervised learning, unsupervised learning, deep learning, ensemble learning, and reinforcement learning align with the pipeline of quantitative trading in signal generation, portfolio construction, execution, and market making. Representative models, such as support vector machines, gradient boosting trees, and long short-term memory (LSTM) networks, are associated with traditional frameworks for allocation, execution, and inventory management, forming a decision-making centered approach. A structured review and a comparative mapping under realistic constraints are used. Evaluation methods to mitigate leakage and overfitting are summarized, and backtesting with pre-screening that explicitly considers trading costs and market impact is also included. The results reveal complementary strengths: ensemble trees and support vector machines provide a robust baseline, sequential models with attention improve temporal representation, reinforcement learning aligns with the goals of sequential trading but requires careful reward design and reliable offline training, and unsupervised tools reveal patterns that support diversification.