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用于自动交易信号的可视图RSI

文章 arXiv papers · 作者: Rafał Rak
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总结

本文介绍可视图相对强弱指数，这是一种基于价格的指标，由工具价格序列中的反向可视关系构建。指标范围为零至100，并用于自动交易策略生成信号。评估采用滚动设计，优化窗口为30天，测试窗口为7天，测试对象包括道琼斯工业平均指数、EUR/USD以及以US美元计价的黄金，覆盖2024至2025年。

报告结果显示，各市场均有利润、回撤适中、每日交易数笔，并报告了夏普比率。这些是基于三种工具的历史回测结果；文中未说明交易成本、滑点、所述固定交易投入之外的头寸风险，也未提供其他时期的表现。研究结果表明，该指标或值得进一步研究，但不能证明其在样本外或实盘交易中仍会盈利。

核心观点

  • VGRSI根据价格数据中的反向可视关系生成交易信号。
  • 该指标范围为零至100，并通过自动化策略进行评估。
  • 研究在股票指数、货币对和黄金上测试该策略。
  • 报告的表现基于历史测试，不能证明其在实盘市场中盈利。

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技术指标股票外汇大宗商品回测
全文
# Visibility graphs can make money in financial markets


# Visibility graphs can make money in financial markets









Traditional technical analysis indicators, although widely used by market participants, are often not sufficiently effective. We propose the Visibility Graphs Relative Strength Index (VGRSI), based on backward visibility relations in the price of a financial instrument. Rescaled to the 0--100 range, it can generate profitable trading signals. The performance of the indicator was evaluated using an automated trading strategy based on a 30-day optimisation window and a 7-day test window for three instruments representing different asset classes: DJI30, EUR/USD and XAU/USD over the 2024--2025 period (503 trading days). The strategy based on VGRSI signals generated a profit of USD~146,000 for DJI30, USD~69,000 for EUR/USD, and USD~125,000 for XAU/USD. This gives a total result of USD$\sim$340,000, which corresponds to an average profit of USD$\sim$676 per trading day, with a fixed investment of USD~1,000 to open a single trade. For all three assets, the strategy generated substantial profits while maintaining a moderate drawdown (10--18\% relative to a portfolio value of USD~10,000), a relatively low trading intensity (3.3--4.8 trades per day) and high Sharpe ratio values (2.55--3.6). These results indicate that VGRSI constitutes a promising technical analysis tool that goes beyond the classical trend-following approach by exploiting the geometric properties of asset price fluctuations.

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。