使用袋式树学习方法预测加密货币的购买信号,以增强购买决策
Raed Alsini1, Qasem Abu Al-Haija2, Abdulaziz A Alsulami1
1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Frontiers in big data
|May 24, 2024
概括
这项研究使用袋式树模型来预测加密货币购买信号,并结合了RSI,BB和MACD等技术指标. 该模型显示了预测加密货币表现的有希望的结果.
科学领域:
- 量化金融 量化金融
- 计算金融是指计算金融.
- 机器学习 机器学习
背景情况:
- 加密货币市场高度波动,受新闻和投资者行为的影响,导致不可预测的价格波动.
- 机器学习交易系统中使用的技术指标可能会受到极端价格变化的影响,影响模型的概括.
- 对零售和机构投资者来说,准确预测加密货币价格变动至关重要.
研究的目的:
- 调查极端价格变化对基于机器学习的加密货币交易系统的影响.
- 开发和评估一个袋式树 (BT) 模型来预测加密货币购买信号.
- 评估BT模型对比特币,以太坊,卡达诺和Binance Coin等主要加密货币的预测准确度.
主要方法:
- 使用的技术指标:相对强度指数 (RSI),波林杰波段 (BB) 和移动平均趋同/分歧 (MACD).
- 采用袋式树 (BT) 模型进行特征提取和购买信号预测.
- 评估了主要加密货币的模型性能:比特币 (BTC),以太坊 (ETH),卡达诺 (ADA) 和币安币 (BNB).
主要成果:
- 包装树模型在预测加密货币购买信号方面表现出有希望的结果.
- 该研究检查了BT模型在预测各种加密货币结果中的准确性.
- 分析包括了流行的机器学习模型在预测加密货币市场行为的有效性.
结论:
- 在波动的加密货币市场中,BT模型显示了提高基于机器学习的交易系统可靠性的潜力.
- 整合RSI,BB和MACD等技术振荡器有助于预测购买信号.
- 建议交易者进一步研究基于市场知识的修改策略.
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