  In this paper we pursue the question of a fully online trading algorithm
(i.e. one that does not need offline training on previously gathered data). For
this task we use Double Deep $Q$-learning in the episodic setting with Fast
Learning Networks approximating the expected reward $Q$. Additionally, we
define the possible terminal states of an episode in such a way as to introduce
a mechanism to conserve some of the money in the trading pool when market
conditions are seen as unfavourable. Some of these money are taken as profit
and some are reused at a later time according to certain criteria. After
describing the algorithm, we test it using the 1-minute-tick data for Cardano's
price on Binance. We see that the agent performs better than trading with
randomly chosen actions on each timestep. And it does so when tested on the
whole dataset as well as on different subsets, capturing different market
trends.
