RCURRENCY: Live Digital Asset Trading Using a Recurrent Neural
  Network-based Forecasting System
  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.
