Cryptocurrency Portfolio Optimization by Neural Networks
Quoc Minh Nguyen Dat Thanh Tran Juho Kanniainen Alexandros Iosifidis Moncef Gabbouj
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.