Lynn Chu
Cryptocurrency, High-Frequency Trading, Order Book Analysis, Machine Learning
This paper presents a high-frequency trading strategy for cryptocurrency markets based on order book dynamics. We develop and evaluate multiple machine learning models to predict price movements using order book data from the Binance cryptocurrency exchange. Our approach incorporates both traditional statistical features and deep learning methods to capture complex patterns in market microstructure. The results demonstrate that our LSTM-based model achieves superior performance in predicting short-term price movements compared to traditional machine learning approaches, with potential applications in algorithmic trading strategies.
Data is collected using the Binance Exchange Public OrderBook API. For more details, visit Binance Order Book API.