A large number of modern practices in financial forecasting rely on technical analysis, which involves several heuristics techniques of price charts visual pattern recognition as well as other technical indicators. In this study, we aim to investigate the potential use of those technical information (candlestick information as well as technical indicators) as inputs for machine learning models, especially the state-of-the-art deep learning algorithms, to generate trading signals. To properly address this problem, empirical research is conducted which applies several machine learning methods to 5 years of Bitcoin hourly data from 2017 to 2022. From the result of our study, we confirm the potential of trading strategies using machine learning approaches. We also find that among several machine learning models, deep learning models, specifically the recurrent neural networks, tend to outperform the others in time-series prediction.

中文翻译：

---

加密货币市场的深度学习和技术分析


金融预测的大量现代实践依赖于技术分析，其中涉及价格图表视觉模式识别以及其他技术指标的几种启发式技术。在本研究中，我们的目标是调查这些技术信息（烛台信息和技术指标）作为机器学习模型（尤其是最先进的深度学习算法）的输入来生成交易信号的潜在用途。为了正确解决这个问题，我们进行了实证研究，将多种机器学习方法应用于 2017 年至 2022 年 5 年的比特币每小时数据。从我们的研究结果来看，我们确认了使用机器学习方法的交易策略的潜力。我们还发现，在几种机器学习模型中，深度学习模型，特别是循环神经网络，在时间序列预测方面往往优于其他模型。