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.

翻译：本文探讨了一种完全在线的交易算法（即无需对先前收集的数据进行离线训练）。为此，我们在情节式设置中使用双深度$Q$-学习，配合快速学习网络逼近期望回报$Q$。此外，我们以引入一种机制的方式定义了情节的可能终止状态，以便在市场条件被认为不利时保留交易池中的部分资金。其中一部分资金作为利润提取，另一部分则根据特定标准在后续时间重新使用。在描述算法后，我们使用币安（Binance）上卡尔达诺（Cardano）价格的1分钟逐笔数据对其进行测试。我们发现，该智能体的表现优于在每个时间步随机选择动作的交易策略。无论是针对整个数据集还是捕捉不同市场趋势的多个子集进行测试，均得出此结论。