The subject of algorithmic trading is attracting attention of investors, developers, and scientists due to high potential financial returns, high demand for implementation of automated business applications for investments, and liquidity provision and trading across all sorts of financial markets, including crypto-currencies. One of the popular applications of that is so called “yield farming” in the crypto-industry, which makes it possible to create investment portfolios consisting of crypto-assets being used for automated liquidity provision also called market making. Yield farming can be per- formed either on centralized exchanges (CEX) such as Binance or decentralized ones (DEX) with smart contracts on Uniswap or Balancer on the Ethereum blockchain. Respectively, there is are a lot of studies on how machine learning and artificial intelligence can be applied to it, such as attempts to learn efficient market making strategies [1,2,3,4]. Unfortunately, the known results are not that exciting so far with demonstrated ability to learn some basic principles of trading using limit book orders, and some ability to outperform “hodling” strategies (buy and hold on rising market) in very specific conditions. So more effort is required to take in this area.