  Crypto-currency market uncertainty drives the need to find adaptive solutions
to maximise gain or at least to avoid loss throughout the periods of trading
activity. Given the high dimensionality and complexity of the state-action
space in this domain, it can be treated as a "Narrow AGI" problem with the
scope of goals and environments bound to financial markets. Adaptive
Multi-Strategy Agent approach for market-making introduces a new solution to
maximise positive "alpha" in long-term handling limit order book (LOB)
positions by using multiple sub-agents implementing different strategies with a
dynamic selection of these agents based on changing market conditions. AMSA
provides no specific strategy of its own while being responsible for segmenting
the periods of market-making activity into smaller execution sub-periods,
performing internal backtesting on historical data on each of the sub-periods,
doing sub- agent performance evaluation and re-selection of them at the end of
each sub- period, and collecting returns and losses incrementally. With this
approach, the return becomes a function of hyper-parameters such as market data
granularity (refresh rate), the execution sub-period duration, number of active
sub-agents, and their individual strategies. Sub-agent selection for the next
trading sub-period is made based on return/loss and alpha values obtained
during internal backtesting as well as real trading. Experiments with the AMSA
have been performed under different market conditions relying on historical
data and proved a high probability of positive alpha throughout the periods of
trading activity in the case of properly selected hyper-parameters.
