# Cryptocurrency Portfolio Optimization by Neural Networks

arXiv: https://arxiv.org/abs/2310.01148 · HTML: https://mlsanity.com/paper/2310.01148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis](https://mlsanity.com/paper/1712.00975/) (2017)
- [Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution](https://mlsanity.com/paper/2012.12620/) (2020)
- [Data Normalization for Bilinear Structures in High-Frequency Financial Time-series](https://mlsanity.com/paper/2003.00598/) (2020)
- [Reinforcement Learning for Portfolio Management](https://mlsanity.com/paper/1909.09571/) (2019)
- [Tensor Representation in High-Frequency Financial Data for Price Change Prediction](https://mlsanity.com/paper/1709.01268/) (2017)
- [Attention-based Neural Bag-of-Features Learning for Sequence Data](https://mlsanity.com/paper/2005.12250/) (2020)
- [Data-driven Neural Architecture Learning For Financial Time-series Forecasting](https://mlsanity.com/paper/1903.06751/) (2019)

## Abstract (arXiv)

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.