The paper presents an advanced version of an adaptive market-making agent capable of performing experiential learning, exploiting a "try and fail" approach relying on a swarm of subordinate agents executed in a virtual environment to determine optimal strategies. The problem is treated as a "Narrow AGI" problem with the scope of goals and environments bound to financial markets, specifically crypto-markets. Such an agent is called an "adaptive multi-strategy agent" as it executes multiple strategies virtually and selects only a few for real execution. The presented version of the agent is extended to solve portfolio optimization and re-balancing across multiple assets so the problem of active portfolio management is being addressed. Also, an attempt is made to apply an experiential learning approach executed in the virtual environment of multi-agent simulation and backtesting based on historical market data, so the agent can learn mappings between specific market conditions and optimal strategies corresponding to these conditions. Additionally, the agent is equipped with the capacity to predict price movements based on social media data, which increases its financial performance.

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.

We have witnessed a permanent shift in the role that data and technology are playing in investment decision-making. Idea generation techniques that had mainly been seen as emerging or experimental are now increasingly being adopted as mainstream. However, one of the biggest challenges for asset managers is how to incorporate, assimilate and integrate many of these techniques into the daily investment processes of the various investment teams. Regardless of the approach taken, data and how it is integrated and analyzed is going to play an increasingly pivotal role across all investment strategies. I will touch upon some key themes in this blog, but will go into more detail in a series to follow.