个人比较倾向于双底，而且会接近200日成交加权均线。但第一种可能兑现的话，也会采取相应对策。 关于技术指标有没有用的问题，首先它只是某一方面的统计特性，提供相对客观的参考依据，但无法替代系统性的建构和分析。前些天讨论概率和决策信 念时，提到过频率主义和贝叶斯主义，大多数技术指标来自于归纳和统计法，属于频率主义，但显然市场并非简单机械的频率主义。 关于技术分析，很多人是误解的，狭隘地归结为看图作业。实际上，凡是涉及到数理逻辑的，包括图形几何、加减乘除以及函数方程，都需要数据支撑， 皆为技术分析，而量化和AI不过是技术分析的高级形式。所以，技术分析早就无处不在，更没必要厚此薄彼，知道一种方法有其所擅长即可。 这几天劈里啪啦各种题材消息，沾花惹草，局部反弹比较热闹，但真正的趋势投机者不会在这个位置出手。 市场指数50日均线的凸形反压一定会起作用，要看它们在反压之下到底怎么走，情绪指标是否再次冰点粘合，指数会不会创新低，如果创新低会不会在60 分钟级别发生动量底背离。当所有这些信号次第发生或不发生时，确定性就会加强。 还有，下一轮的主线是谁，虽然显露了一些眉目，但仍然不能拍板。 如上图，假如我们把当前的位置C和之前的 ...

Group 1 - The securities market is viewed as a probability game with a win-loss distribution of approximately seven losses, two draws, and one win, which raises questions about why many are drawn to it despite low winning odds [1] - The disparity in winning probabilities leads to significant potential returns, influencing participants' beliefs and behaviors in the market [1] - The article discusses two schools of thought regarding probability: frequentism, which views probability as an objective and unchanging reality, and Bayesianism, which sees it as a subjective belief that can be adjusted based on new evidence [1][2] Group 2 - Frequentism is characterized as a conservative approach that emphasizes safety margins, utilizing extensive data to extract objective rules and minimize risk, while Bayesianism is more intuitive and flexible, updating conclusions based on prior knowledge and current evidence [2] - The article suggests that no participant in the stock market is purely a frequentist or Bayesian; rather, individuals blend both approaches, adjusting their strategies based on varying frequencies of data and personal beliefs [2] - The integration of both frequentist and Bayesian thinking is essential for navigating the complexities of the market, allowing for the recognition of patterns while remaining adaptable to uncertainties [2][3] Group 3 - The analogy of chess is used to illustrate the combination of frequentism and Bayesianism, exemplified by AlphaGo's success in defeating top human players through a blend of data analysis and real-time strategy adjustments [3]