LSTM：利用逐笔交易数据预测加密货币短期价格走势

文章 arXiv papers · 作者: Qi Zhao

总结

本文介绍一种长短期记忆模型，利用回看期内的逐笔交易数据，在固定的短期时距上预测加密货币价格方向。其流程包括特征设计和超参数搜索，训练数据来自近一年的交易观测。

作者报告称，样本外准确率超过60%，且各测试期间的表现保持稳定；他们还介绍了一项交易模拟，其中预测结果可能被用于变现。作者还报告称，在部分交易品种上训练的参数在其他加密货币上仍保持表现。摘要未说明资产、预测时距、样本日期、模拟假设或扣除费用和滑点后的表现。因此，这些报告结果不能证明其在实盘交易中盈利，也不能证明其普遍适用于各类加密市场。

核心观点

该框架使用LSTM网络，根据逐笔交易观测预测加密货币短期价格方向。
特征设计和超参数搜索是建模流程的核心环节。
论文报告称，样本外准确率超过60%，并通过交易模拟展示预测可能变现。
论文报告称模型可迁移至其他加密货币交易品种，但未说明成本和模拟细节。

# A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data


# A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data









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.

在遵守原作品许可的前提下，附作者信息全文展示。许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。

LSTM：利用逐笔交易数据预测加密货币短期价格走势

总结

核心观点

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