论文标题
DeepFolio：用于投资组合的卷积神经网络，具有限制订单簿数据
DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data
论文作者
论文摘要
这项工作提出了DeepFolio，这是一种基于限制顺序书籍（LOB）的数据的深层投资组合管理的新模型。 DeepFolio解决了LOB数据的最新问题中发现的问题，以预测价格变动。我们的评估包括两种情况，使用了数百万个时间序列的大量数据集。在大量和稀缺数据的情况下，这些改进均可带来卓越的结果。该实验表明，DeepFolio在基准FI-2010 LOB上的最先进。此外，我们使用DeepFolio来与重新平衡的加密资源分配最佳投资组合。为此，我们使用两个损失功能 - 夏普比率损失和最小波动性风险。我们表明，DeepFolio的表现优于文献中广泛使用的投资组合分配技术。
This work proposes DeepFolio, a new model for deep portfolio management based on data from limit order books (LOB). DeepFolio solves problems found in the state-of-the-art for LOB data to predict price movements. Our evaluation consists of two scenarios using a large dataset of millions of time series. The improvements deliver superior results both in cases of abundant as well as scarce data. The experiments show that DeepFolio outperforms the state-of-the-art on the benchmark FI-2010 LOB. Further, we use DeepFolio for optimal portfolio allocation of crypto-assets with rebalancing. For this purpose, we use two loss-functions - Sharpe ratio loss and minimum volatility risk. We show that DeepFolio outperforms widely used portfolio allocation techniques in the literature.
