Impact of real-world market conditions on returns of deep learning based trading strategies

Based on recent advancements in natural language processing, computer vision and robotics, a growing number of researchers and traders attempt to predict future asset prices using deep learning techniques. Typically, the goal is to find a profitable and at the same time low-risk trading strategy. However, it is not straightforward to evaluate a found trading strategy. Evaluating solely on historic price data neglects important factors arising in real markets. In this paper, we analyze the impact of real-world market conditions in terms of trading fees, borrow interests, slippage and spreads on trading returns. For that, we propose a deep learning trading bot based on Temporal Convolutional Networks, which is deployed to a real cryptocurrency exchange. We compare the results obtained in the real market with simulated returns and investigate the impact of the different real-world market conditions. Our results show that besides trading fees (which have the biggest impact on returns), factors like slippage and spread also affect the returns of the trading strategy.