Consistent alpha generation, i.e., maintaining an edge over the market, underpins the ability of asset traders to reliably generate profits. Technical indicators and trading strategies are commonly used tools to determine when to buy/hold/sell assets, yet these are limited by the fact that they operate on known values. Over the past decades, multiple studies have investigated the potential of artificial intelligence in stock trading in conventional markets, with some success. In this paper, we present RCURRENCY, an RNN-based trading engine to predict data in the highly volatile digital asset market which is able to successfully manage an asset portfolio in a live environment. By combining asset value prediction and conventional trading tools, RCURRENCY determines whether to buy, hold or sell digital currencies at a given point in time. Experimental results show that, given the data of an interval $t$, a prediction with an error of less than 0.5\% of the data at the subsequent interval $t+1$ can be obtained. Evaluation of the system through backtesting shows that RCURRENCY can be used to successfully not only maintain a stable portfolio of digital assets in a simulated live environment using real historical trading data but even increase the portfolio value over time.

翻译：技术指标和交易战略是用来确定何时购买/持有/出售资产的工具,但这些指标和交易战略却因资产以已知价值运作而受到限制。在过去几十年中,多项研究调查了传统市场股票交易中人工智能的潜力,取得了一定的成功。在本文件中,我们介绍了以RURRENCY为基础的一个基于RURRENICY的贸易引擎,用以预测高度波动的数字资产市场的数据,这种市场能够成功地管理活生生环境中的资产组合。通过将资产价值预测与常规贸易工具结合起来,RCURRENICY决定是否在某个特定时间购买、持有或出售数字货币。实验结果显示,鉴于美元间隔的数据,可以得出低于0.5美元+1美元的数据错误的预测。通过追溯测试对系统进行的评估表明,RCURRENICY不仅能够成功地在模拟的活环境中使用真实的历史贸易数据维持稳定的数字资产组合,而且能够提高投资组合的价值。