杰瑞发布于2025-09-22
Can using custom neutralization on the Alpha based on self-created groups (like historical volatility) help improve sub-universe performance? Use floor or bucket operator combined with rank operator to implement custom neutralization基于自我创建的组(如历史波动率)在Alpha上使用自定义中和可以帮助提高子宇宙的性能吗?使用地板或铲斗操作员结合等级操作员来实现自定义中和 Stocks of companies that face high differences in their prices after any news release can be subject to varying sentiments that can lead to volatile behaviour. You can try avoiding periods of extreme volatility due to recent news releases在任何新闻发布后,价格差异较大的公司股票可能会受到不同情绪的影响,从而导致波动行为。你可以试着避免因最近的新闻发布而出现极端波动的时期 The recent news event only be relevant for a limited time, can using an operator which changes Alpha weights quickly for values closer to zero rather than distant values help improve Alpha performance? Also can using trade_when operator help reduce the turnover?最近的新闻事件只在有限的时间内具有相关性,使用一个运算符来快速更改接近零的值而不是距离较远的值的Alpha权重,可以帮助提高Alpha性能吗?使用trade_when操作符也能帮助减少营业额吗? Stocks of companies that face high differences in their prices after any news release can be subject to varying sentiments that can lead to volatile behaviour. You can try avoiding periods of extreme volatility due to recent news releases在任何新闻发布后,价格差异较大的公司股票可能会受到不同情绪的影响,从而导致波动行为。你可以试着避免因最近的新闻发布而出现极端波动的时期 A higher hub score in the data field indicates that a company's customers have many connections, while a lower score suggests a more concentrated set of partners. If a company's customers have lower hub scores, it means they have fewer partners and potentially rely on the company. This can be positive for the stock as it indicates a lower risk of the company being replaced. Therefore, investing in such stocks for the long term may be a good idea数据字段中较高的中心得分表明公司的客户有很多联系,而较低的得分表明合作伙伴更集中。如果一家公司的客户中心得分较低,这意味着他们的合作伙伴较少,可能会依赖该公司。这对股票来说可能是积极的,因为它表明公司被替换的风险较低。因此,长期投资此类股票可能是个好主意 In a data science model: Imagine a model that generates a series of random numbers. Operator: The random number generator. Alpha: The output of this operator (the series of random numbers). Driver parameter: A "shape" parameter that controls how the random numbers are generated. Transformation: The model's default is to generate numbers with a normal distribution. However, by using a new value for the "shape" parameter, you can tell the generator to produce numbers that instead follow a Gamma or Beta distribution, thus "improving the Alpha" by making it more suitable for a specific task.在数据科学模型中:想象一个生成一系列随机数的模型。运算符:随机数生成器。Alpha:此运算符的输出(随机数序列)。Driver参数:控制随机数生成方式的“形状”参数。转换:模型的默认值是生成具有正态分布的数字。但是,通过为“shape”参数使用新值,您可以告诉生成器生成遵循Gamma或Beta分布的数字,从而通过使其更适合特定任务来“改进Alpha”。