High-frequency trading (HFT) strategies rely on machine learning models to detect market inefficiencies and execute trades within milliseconds. Common models include: 1. Reinforcement Learning (RL): Algorithms like Q-learning and Deep Q-Networks (DQNs) optimize execution strategies by adapting to changing market conditions. 2. Recurrent Neural Networks (RNNs) & LSTMs: These models process sequential data to predict short-term price movements based on historical trends. 3. Gradient Boosting (XGBoost, LightGBM): Used for feature selection and price prediction, boosting models efficiently handle large datasets. 4. Autoencoders & GANs: Used for anomaly detection, market simulation, and synthetic data generation. 5. Bayesian Networks: Useful for probabilistic modeling and estimating uncertainty in market conditions. HFT firms integrate these models with real-time data feeds and low-latency execution systems, leveraging colocation and FPGA acceleration to minimize execution delays. Continuous retraining and feature engineering are essential for maintaining competitive edges in ever-evolving markets.