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通过关键参与者强化学习和可适应的数据结构进行交易
1Quantitative Methods Department, Cunef University, Madrid, Madrid, Spain.
PeerJ. Computer science
|March 10, 2025
概括
本研究介绍了一种强化学习 (RL) 模型,用于自动化,短期交易,以尽量减少资本损失. 这种创新方法提高了投资代理商在原油,黄金和欧元等波动性市场的准确性.
科学领域:
- 计算金融是指计算金融.
- 机器学习 机器学习
- 算法交易是一种算法交易.
背景情况:
- 由于政治,经济和社会波动,股票市场投资面临挑战.
- 开发准确,高效的投资模式对于最小化资本损失至关重要.
研究的目的:
- 提出一个创新的,短期的,使用强化学习 (RL) 的自动投资模式.
- 增强在波动性市场中的投资代理人的学习和准确性.
- 为了减少交易操作期间的资本损失.
主要方法:
- 强化学习 (RL) 模型与演员关键神经网络的应用.
- 利用纠正线性单元 (ReLU) 神经元来产生专门的药物.
- 实施可适应的数据窗口结构,以改善学习.
主要成果:
- 在RL模型中,平均损失降低了0.03%在欧元,0.25%在黄金和0.13%在原油.
- 在不同时间段实现了更高效的交易并最大限度地减少了投资损失.
- 减少了模型的整体学习时间.
结论:
- 拟议的RL模型为短期的自动化交易提供了有效的解决方案.
- 可适应的数据窗口结构提高了外汇市场中代理商的表现.
- 这种方法有助于在动态的交易环境中最大限度地减少财务损失.
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