SP rank with the following meaning:// 0----invalid rank //1----A+//2----A//3----A-//4----B+//5----B//6----B-//7----C+//8----C//9----C- | Matrix.
>> Fundamental Company Fundamental Data for Equity

Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
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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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Trade_When (x=triggerTradeExp, y=AlphaExp, z=triggerExitExp) If triggerExitExp > 0, Alpha = NaN. Else if triggerTradeExp > 0, Alpha = AlphaExp; else, Alpha = previousAlpha
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Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input; Examples: my_group = bucket(rank(volume), range="0.1,1,0.1"); group_neutralize(sales/assets, my_group) my_group = bucket(rank(volume), buckets ="0.2,0.5,0.7", skipBoth=True, NANGroup=True); group_neutralize(sales/assets, my_group)
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Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0; Rank(close); Rank (close, rate=0) # Sorts precisely; X = (4,3,6,10,2) => Rank(x) = (0.5, 0.25, 0.75, 1, 0)
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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.
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Returns various parameters related to regression function; ts_regression(est_netprofit, est_netdebt, 252, lag = 0, rettype = 2); Taking the data from the past 252 trading days (1 year), return the β coefficient from the equation when estimating the est_netprofit using the est_netdebt.
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Rank the values of x for each instrument over the past d days, then return the rank of the current value + constant. If not specified, by default, constant = 0.
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Returns the linear decay on x for the past d days. Dense parameter=false means operator works in sparse mode and we treat NaN as 0. In dense mode we do not.
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Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)。
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This operator returns x – ts_mean(x, d), but it deals with NaNs carefully； Example: If d = 6 and values for past 6 days are [6,2,8,5,9,NaN] then ts_mean(x,d) = 6 since NaN are ignored from mean computation. Hence, ts_av_diff(x,d) = 6-6 = 0
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Returns the relative index of the min value in the time series for the past d days; If the current day has the min value for the past d days, it returns 0; If previous day has the min value for the past d days, it returns 1. If d = 6 and values for past 6 days are [6,2,8,5,9,4] with first element being today’s value then min value is 2 and it is present 1 days before today. Hence, ts_arg_min(x, d) = 1
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Returns the relative index of the max value in the time series for the past d days. If the current day has the max value for the past d days, it returns 0. If previous day has the max value for the past d days, it returns 1；If d = 6 and values for past 6 days are [6,2,8,5,9,4] with first element being today’s value then max value is 9 and it is present 4 days before today. Hence, ts_arg_max(x, d) = 4
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Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1;Returns k-th value of input by looking through lookback days while ignoring space separated scalars in ignore list. This operator is also known as backfill operator as it can be used to backfill missing data. ignore parameter is used to provide list of separated scalars to ignore from counting Example of backfill: kth_element(sales/assets,252,k="1",ignore="NAN 0")
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Limits amount and magnitude of changes in input (thus reducing turnover)； hump(-ts_delta(close, 5), hump = 0.00001)
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Returns the logical negation of x. If x is true (1), it returns false (0), and if input is false (0), it returns true (1).
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If (input == NaN) return 1 else return 0
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x-y. If filter = true, filter all input NaN to 0 before subtracting
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x raised to the power of y such that final result preserves sign of x; sign(x) * (abs(x) ^ y) x raised to the power of y such that final result preserves sign of x. For power of 2, x ^ y will be a parabola but signed_power(x, y) will be odd and one-to-one function (unique value of x for certain value of signed_power(x, y)) unlike parabola. Example: If x = 3, y = 2 ⇒ abs(x) = 3 ⇒ abs(x) ^ y = 9 and sign(x) = +1 ⇒ sign(x) * (abs(x) ^ y) = signed_power(x, y) = 9; If x = -9, y = 0.5 ⇒ abs(x) = 9 ⇒ abs(x) ^ y = 3 and sign(x) = -1 ⇒ sign(x) * (abs(x) ^ y) = signed_power(x, y)
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if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
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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.
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Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding.
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