Russian Federation
The article examines the development of a comprehensive dataflow architecture for machine learning models used in statistical arbitrage in digital asset markets, with a focus on developing features from heterogeneous sources. The main data sources are systematized and classified: market microstructure data, blockchain data, alternative data such as social media sentiment, monitoring of large wallets, and derived features.
cryptocurrency arbitrage, statistical arbitrage, machine learning, blockchain, BigData, streaming data processing
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