Many cryptocurrency brokers nowadays offer a variety of derivative assets
that allow traders to perform hedging or speculation. This paper proposes an
effective algorithm based on neural networks to take advantage of these
investment products. The proposed algorithm constructs a portfolio that
contains a pair of negatively correlated assets. A deep neural network, which
outputs the allocation weight of each asset at a time interval, is trained to
maximize the Sharpe ratio. A novel loss term is proposed to regulate the
network's bias towards a specific asset, thus enforcing the network to learn an
allocation strategy that is close to a minimum variance strategy. Extensive
experiments were conducted using data collected from Binance spanning 19 months
to evaluate the effectiveness of our approach. The backtest results show that
the proposed algorithm can produce neural networks that are able to make
profits in different market situations.

提出了一种基于神经网络的算法来利用加密货币衍生资产，以构建一个包含负相关资产对的投资组合，并通过训练深度神经网络来最大化夏普比率，以实现接近一个最小方差策略的资产配置策略，经过 19 个月的实验证明，该算法可在不同市场情况中获利。