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无量纲情景框架与技术分析

文章 arXiv papers · 作者: J. V. Andersen et al.
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总结

本文提出一种概率方法，根据价格速度和加速度对市场行为分类。研究使用两个无量纲量描述偏离随机游走的情形：比较加速度与速度的弗劳德数，以及按训练期缩放的预测期限。由此形成的情景框架不受价格计量单位变化影响，并考虑给定相同价格历史时可能出现的多条路径。

根据缩放后的预测期限，市场可能呈现趋势跟随或逆势模式。所述方法通过评估预测市场轨迹的稳定性，在两者之间进行选择。测试使用主要股票指数、债券和货币的日收益数据，跨度约为九至三十年。作者报告称，该方法在几乎所有市场阶段均具有统计显著的预测能力，而单独的趋势策略和趋势与加速度策略仅在特定阶段表现良好。摘要没有提供详细方法、交易成本或样本外检验方案，因此这些结果本身不能证明策略具有可交易的盈利能力。

核心观点

  • 该框架通过速度和加速度描述价格历史。
  • 无量纲弗劳德数和缩放后的预测期限共同定义情景分类。
  • 趋势跟随和逆势行为可能并存，其相关性取决于预测期限。
  • 实证测试涵盖股票指数、债券和货币，并报告其在各市场阶段均有预测能力。
  • 描述未提供交易成本或详细的样本外证据。

标签

多资产技术指标趋势跟踪均值回归统计学
全文
# Fundamental Framework for Technical Analysis


# Fundamental Framework for Technical Analysis









Starting from the characterization of the past time evolution of market prices in terms of two fundamental indicators, price velocity and price acceleration, we construct a general classification of the possible patterns characterizing the deviation or defects from the random walk market state and its time-translational invariant properties. The classification relies on two dimensionless parameters, the Froude number characterizing the relative strength of the acceleration with respect to the velocity and the time horizon forecast dimensionalized to the training period. Trend-following and contrarian patterns are found to coexist and depend on the dimensionless time horizon. The classification is based on the symmetry requirements of invariance with respect to change of price units and of functional scale-invariance in the space of scenarii. This ``renormalized scenario'' approach is fundamentally probabilistic in nature and exemplifies the view that multiple competing scenarii have to be taken into account for the same past history. Empirical tests are performed on on about nine to thirty years of daily returns of twelve data sets comprising some major indices (Dow Jones, SP500, Nasdaq, DAX, FTSE, Nikkei), some major bonds (JGB, TYX) and some major currencies against the US dollar (GBP, CHF, DEM, JPY). Our ``renormalized scenario'' exhibits statistically significant predictive power in essentially all market phases. In constrast, a trend following strategy and trend + acceleration following strategy perform well only on different and specific market phases. The value of the ``renormalized scenario'' approach lies in the fact that it always finds the best of the two, based on a calculation of the stability of their predicted market trajectories.

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。