CryptoMixer: Fine-grained market information-aware MLP Networks for Individual Cryptocurrency Trading Prediction

Accurately predicting user trading behavior in decentralized exchanges is essential for investors to mitigate risks and optimize their trading strategies. While existing research primarily focuses on predicting trading behavior in stock markets, these methods often struggle to adapt to the distinct nature of cryptocurrency trading. Specifically, they face issues such as limited adaptivity to high-frequency and algorithmic trading, as well as an insufficient consideration of fine-grained real-time market participants' behavior.Thanks to the pending mechanism of blockchain, it becomes possible to capture traders' interactions before transactions are finalized, providing valuable insights into market state. However, accurately modeling and predicting trading behavior in decentralized exchanges presents challenges, including limited adaptability to high-frequency trading, a lack of fine-grained transaction data, and high computational costs. This work proposes CryptoMixer, a lightweight fine-grained market information-aware multilayer perceptron (MLP)-based model for high-frequency cryptocurrency trading behavior prediction. Specifically, to overcome the sparsity and asynchrony of user behavior data, CryptoMixer develops a Market Information Augmenter that aggregates historical transaction data of users. Furthermore, CryptoMixer designs a Market Information Mixer as well as a Two-stream MLP Fusion Mixer to capture fine-grained user trading behavior patterns. We evaluate CryptoMixer on real-world user trading datasets from the Uniswap decentralized finance platform. Experimental results demonstrate that CryptoMixer outperforms traditional prediction models while maintaining low computational overheads, providing a practical solution for real-time cryptocurrency trading behavior prediction. The code is available at https://github.com/aqua111000/CryptoMixer.