词汇：alpha['ælfə]

n. 希腊字母的第一个字母；开端；最初

相关场景

Consultants who have submitted at least 100 Alphas in the aggregate (and which have not been decommissioned), are now eligible to submit SuperAlphas in any universe or region. By submitting a SuperAlpha each day (limit to 1 per day for consideration), you may be able to increase your total base payment accrued daily.
>> Quant Brain新的阶段
总共提交了至少100个阿尔法（且尚未退役）的顾问现在有资格在任何宇宙或地区提交超级阿尔法。通过每天提交一份SuperAlpha（每天最多1份供考虑），您可以增加每日累计的基本付款总额。
pasteurize(x):
Set to NaN if x is INF or if the underlying instrument is not in the Alpha universe. This operator may help reduce outliers. Input: Value of 7 instruments at day t: (2, 3, 5, INF, 3, 8, 10), where value 10 does not belong in Alpha universe Output: (2, 3, 5, NaN, 3, 8, NaN)
>> Operators
如果x是INF或基础仪器不在Alpha宇宙中，则设置为NaN。此运算符可能有助于减少异常值。输入：第t天7个仪器的值：（2,3,5，INF，3,8,10），其中值10不属于阿尔法宇宙输出：（2,3,5，NaN，3,8，NaN）
pasteurize(x):
Set to NaN if x is INF or if the underlying instrument is not in the Alpha universe. This operator may help reduce outliers.
>> Operators
🔓 Features:
Unlock access to create Alphas using 100,000+ data fields from 8+ regions based on consultant level, and advanced features:longer simulation periods, data visualizations, multi-simulation, leveraging BRAIN's API with Python, creating SuperAlphas
>> Quant Brain新的阶段
💰 Compensation:
Receive merit-based financial compensation. Earn up to $120 per day (Activity-based) and $8,000 or more per quarter (Performance-based) based on your Alpha submissions.
>> Quant Brain新的阶段
获得基于绩效的经济补偿。根据您的Alpha提交，每天可赚取高达120美元（基于活动），每季度可赚取8000美元或更多（基于绩效）。
Once you complete up to Step 2, you will become a Conditional Consultant and within 24 hours your BRAIN platform will be upgraded to Consultant access! You may also start to accrue fees on any submitted Alphas. For more details on the consultant onboarding process, please visit this FAQ.
>> Quant Brain新的阶段
完成第2步后，您将成为有条件顾问，在24小时内，您的BRAIN平台将升级为顾问访问权限！您还可以开始对任何提交的Alphas累计费用。有关顾问入职流程的更多详细信息，请访问此常见问题解答。
11.19:
Your questionnaire is approved! You are one step away from accruing money for you alphas!
>> Quant Brain新的阶段
您的问卷已获得批准！你离为阿尔法积累财富只有一步之遥！
11.13-11.13:
add alpha prompt . /subjects/:id/prompt
>> 事项 Creative platform
11.1:
The first alpha that can be submitted. it's average.
>> 事项 Creative platform
10.18 - done some before 12-31:
migrate the alpha code to delitao.
>> 事项 Creative platform
group_neutralize(x, group):
Neutralize alpha against groups. Difference between normalize and group_neutralize is in normalize, every element is subtracted by mean of all values of all instruments on that day whereas in group_neutralize, element is subtracted by mean of all values of the group of instruments that it belongs on that day. This operator may help reduce correlation, depending on the neutralization used.
>> Operators
中和阿尔法对团体的攻击。normalize和group_nneutralize之间的区别在于normalize，每个元素都减去当天所有仪器的所有值的平均值，而在group_neutralize中，元素减去当天所属仪器组的所有值。根据所使用的中和方法，此运算符可能有助于降低相关性。
trade_when(x, y, z):
Used in order to change Alpha values only under a specified condition and to hold Alpha values in other cases. It also allows to close Alpha positions (assign NaN values) under a specified condition; This operator may help reduce correlation and reduce turnover.
>> Operators
用于仅在指定条件下更改Alpha值，并在其他情况下保持Alpha值。它还允许在指定条件下关闭Alpha位置（分配NaN值）；此运算符可能有助于减少相关性并降低营业额。
quantile(x, driver = gaussian, sigma = 1.0):
Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector;quantile(close, driver = gaussian, sigma = 0.5 ); This operator may help reduce outliers.
>> Operators
对原始向量进行排序，移动排序后的阿尔法向量，应用分布（高斯分布、柯西分布、均匀分布）。如果驱动器是均匀的，它只需用阿尔法向量中所有阿尔法值的平均值减去每个阿尔法值；分位数（闭合，驱动器=高斯，西格玛=0.5）；此运算符可能有助于减少异常值。
normalize(x, useStd = false, limit = 0.0):
Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element.If for a certain date, instrument value of certain input x is [3,5,6,2], mean = 4 and standard deviation = 1.82
>> Operators
计算某个日期的所有有效alpha值的平均值，然后从每个元素中减去该平均值。如果在某个日期，某个输入x的仪器值为[3,5,6,2]，平均值=4，标准偏差=1.82
This ensures that your Alpha is long short neutral.
>> worldquantbrain_5_vector
这确保了你的阿尔法是长短中性的。
Suppose we have Alpha = -ts_delta (close, 5), where Alpha is the vector of values. Setting neutralization = market, would make the mean of the Alpha vector equal to zero, i.e. the Alpha vector would undergo the change: Alpha = Alpha - mean(Alpha).
>> worldquantbrain_5_vector
假设我们有Alpha=-ts_delta（close，5），其中Alpha是值的向量。设置中和=市场，将使阿尔法向量的均值等于零，即阿尔法向量将发生变化：阿尔法=阿尔法-均值（阿尔法）。
Neutralization is an operation in which the raw Alpha values are split into groups, and then normalized (the mean is subtracted from each value) within each group. The group can be the entire market, or the groups could be made using other classifications like industry or sub-industry.
>> worldquantbrain_5_vector
中和是一种操作，其中原始阿尔法值被分成几组，然后在每组内进行归一化（从每个值中减去平均值）。该组可以是整个市场，也可以使用其他分类（如行业或子行业）进行分组。
How to get a higher Sharpe? How to potentially increase returns of an alpha? How to reduce correlation of an alpha? How to reduce turnover? How to potentially decrease PnL fluctuations? How to gain intuition for Neutralization? How to avoid overfitting?
>> worldquantbrain_5_vector
如何获得更高的夏普？如何潜在地增加阿尔法的回报？如何降低阿尔法的相关性？如何减少营业额？如何潜在地降低PnL波动？如何获得中和的直觉？如何避免过度拟合？
These readings answer some of the most common problems faced during the process of making alphas. Incorporating the practices suggested above should assist you in your journey with WorldQuant Brain, and may lead to significantly better results in the long run.
>> worldquantbrain_5_vector
这些阅读材料回答了制作阿尔法过程中面临的一些最常见的问题。结合上述建议的实践应该有助于您在WorldQuant Brain的旅程中，并可能从长远来看带来更好的结果。
While the alpha idea, datasets and operators keep changing, the techniques and methodologies needed to achieve the three remain largely the same. Hence, the following posts are a generic guide for your alpha research journey and will act as solid foundations for you:
>> worldquantbrain_5_vector
虽然阿尔法思想、数据集和运算符不断变化，但实现这三个目标所需的技术和方法基本保持不变。因此，以下帖子是您阿尔法研究之旅的通用指南，将为您奠定坚实的基础：
Typically, the alpha research cycle is comprised of 3 stages: Coming up with an intuitive alpha idea; Implementing it using the available datasets and operators; and Optimizing the parameters and neutralization settings of the alpha in order to submit it in its best form.
>> worldquantbrain_5_vector
通常，阿尔法研究周期由3个阶段组成：提出一个直观的阿尔法想法；使用可用的数据集和运算符来实现它；优化阿尔法的参数和中和设置，以便以最佳形式提交。
The transition you make from being a user to a consultant is not straightforward since you will work on many open-ended problems in the process of making alphas. There are no right or wrong answers in the alpha-making process since quant research is a mixture of creative and scientific thinking.
>> worldquantbrain_5_vector
从用户到顾问的过渡并不简单，因为在制作阿尔法的过程中，你将处理许多开放式问题。在阿尔法制造过程中没有对错的答案，因为定量研究是创造性和科学思维的混合体。
Due to D0 Alphas normally having higher turnover than D1 Alphas, to compensate for the increasing transaction costs, higher Sharpe and higher returns are required. But there are also other tests that you have to consider, such as the SubUniverse test and the RobustUniverse test for the CHN region. Good performance in the liquid universe means that the Alpha should have higher capacity.
>> worldquantbrain_5_vector
由于D0 Alphas的营业额通常高于D1 Alphas，为了弥补不断增加的交易成本，需要更高的夏普和更高的回报。但你也必须考虑其他测试，例如针对CHN地区的亚宇宙测试和RobustUniverse测试。在液体宇宙中的良好性能意味着阿尔法应该具有更高的容量。
Both Delay-0 and Delay-1 Alphas[1] (referred to as D0 and D1 Alphas respectively throughout) try to capitalize by rebalancing the Alpha positions daily. D0 Alphas are Alphas that also try to benefit from using the most recent information. These Alphas utilize the same available data during the day and usually simulate trades some period before the market close.
>> worldquantbrain_5_vector
Delay-0和Delay-1阿尔法[1]（分别称为D0和D1阿尔法）都试图通过每天重新平衡阿尔法头寸来实现资本化。D0阿尔法是也试图从使用最新信息中受益的阿尔法。这些阿尔法在白天利用相同的可用数据，通常在市场收盘前的一段时间内模拟交易。
Below are again the average value and turnover plots for the vec_avg field. Average value hovers densely around 15,000 and turnover around 130%. Here as well, you need to reduce turnover by using ts_rank or ts_decay in your Alpha expression.
>> worldquantbrain_5_vector
下面是vec_avg字段的平均值和周转图。平均价值在15000左右，成交率在130%左右。在这里，您也需要在Alpha表达式中使用ts_rank或ts_decay来减少周转率。