[he and Dee lead their unknown traveler into a field] Looks like you fell off the bus in the wrong part of town, amigo. In fact, I'll bet dollars to pesos that you're not from anywhere near here. [He pulls out a knife, and cuts the man's poncho. The man's clothes fall to the ground, revealing what he really is underneath -- an amphibian/reptile with two independent eyes and he also appeared to have a shell on his back, which had six small arms and hands. The only part of his camouflage not crumpled to the ground is the humanesque "head," which he still lamely holds in one of his hands. It's propped up by a stick, like a puppet, and it continues to make expressions as he holds it.] Mikey?! [Mikey replies -- an unfathomable combination of grunts, squeaks, and saliva.] Mikey, when did they let you out of jail? [Mikey replies.] Political refugee. Right.
>> Men In Black Movie 1997 Script

Gives the number of instruments in the same group (e.g. sector) which have valid values of x. For example, x=1 gives the number of instruments in each group (without regard for whether any particular field has valid data). This operator improves weight coverage and may help to reduce drawdown risk.
>> Operators

Gives the number of instruments in the same group (e.g. sector) which have valid values of x. For example, x=1 gives the number of instruments in each group (without regard for whether any particular field has valid data). This operator improves weight coverage and may help to reduce drawdown risk.
>> Operators

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

Group operators are a type of cross-sectional operator that compares stocks at a finer level, where the cross-sectional operation is applied within each group, rather than across the entire market. The group_rank operator allocates the stocks to their specified group, then within each group, it ranks the stocks based on their input value for data field x and returns an equally distributed number between 0.0 and 1.0. This operator may help reduce both outliers and drawdown while reducing correlation. Example: group_rank(x, subindustry)； The stocks are first grouped into their respective subindustry. Within each subindustry, the stocks within that subindustry are ranked based on their input value for data field x and assigned an equally distributed number between 0.0 and 1.0.
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Sum of vector field x
>> Operators

Taking mean of the vector field x
>> Operators

Returns product of x for the past d days; ts_product(x, d) can be used to calculate geometric mean of data fields. The geometric mean is generally a better method for averaging rates of return, growth rates of fundamentals. For example, geometric mean of daily stock returns for past 10 days can be calculated as: power(ts_product(returns, 10), 1/10)
>> Operators

This operator converts a grouping field with many buckets into a lesser number of only the available buckets, making working with grouping fields computationally efficient. The example below will clarify the implementation. Example: Say a grouping field is provided as an integer (e.g., industry: tech -> 0, airspace -> 1, ...) and for a certain date, we have instruments with grouping field values among {0, 1, 2, 99}. Instead of creating 100 buckets and keeping 96 of them empty, it is better to just create 4 buckets with values {0, 1, 2, 3}. So, if the number of unique values in x is n, densify maps those values between 0 and (n-1). The order of magnitude need not be preserved.
>> Operators