论文标题
从交易数据中预测数字资产价格转移的深度学习框架
A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data
论文作者
论文摘要
本文提出了一个基于长期短期存储网络（LSTM）的深度学习框架，该框架可以预测从交易数据中加密货币的价格转移。这项研究的主要重点是预测背部固定时间范围内的短期价格变化。通过仔细设计功能并详细搜索最佳的超参数，该模型经过培训，可以在近一年的按交易数据的数据上实现高性能。最佳模型在样本外测试期间提供稳定的高性能（精度超过60％）。在现实的交易模拟设置中，模型的预测很容易被货币化。此外，这项研究表明，LSTM模型可以从逐贸易数据中提取通用功能，因为学习的参数很好地维持了其在培训数据中未包含的其他加密货币仪器上的高性能。这项研究超过了所用数据的规模和精度以及实现的高预测准确性的现有研究。
This paper presents a deep learning framework based on Long Short-term Memory Network(LSTM) that predicts price movement of cryptocurrencies from trade-by-trade data. The main focus of this study is on predicting short-term price changes in a fixed time horizon from a looking back period. By carefully designing features and detailed searching for best hyper-parameters, the model is trained to achieve high performance on nearly a year of trade-by-trade data. The optimal model delivers stable high performance(over 60% accuracy) on out-of-sample test periods. In a realistic trading simulation setting, the prediction made by the model could be easily monetized. Moreover, this study shows that the LSTM model could extract universal features from trade-by-trade data, as the learned parameters well maintain their high performance on other cryptocurrency instruments that were not included in training data. This study exceeds existing researches in term of the scale and precision of data used, as well as the high prediction accuracy achieved.
