Architecture of Automated Crypto-Finance Agent

The subject of decentralized finance is attracting the attention of investors as well developers and scientists due to high potential financial returns, high demand for implementation of automated business applications for investments, liquidity provision, and trading using crypto-currencies. A few unique properties of cryptofinancial markets, enormous volatility and the presence of "on-chain" data such as transaction logs that may be used as an extra source of data for applications based on artificial intelligence and machine learning. The key possibility associated with decentralized finance is automated liquidity provision, also called market making, which can be performed on either centralized exchanges (CEX), such as Binance, or decentralized ones (DEX) such as smart contracts like Uniswap or Balancer on the Ethereum blockchain. How machine learning and artificial intelligence can be applied to it is a matter of active study, such as attempts to learn efficient market making strategies [1,2,3,4]. Unfortunately, the results are not that exciting so far with demonstrated ability to learn some basic principles of trading using limit book orders, with the ability to outperform "hodling" strategy (buy and hold on rising market) in very specific conditions.