alpha_tools/backup_code/wqb_app/operaters.csv

1	name	category	description
2	add	Arithmetic	Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
3	exp	Arithmetic	Natural exponential function: e^x
4	multiply	Arithmetic	Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
5	sign	Arithmetic	if input = NaN; return NaN
6	subtract	Arithmetic	x-y. If filter = true, filter all input NaN to 0 before subtracting
7	pasteurize	Arithmetic	Set to NaN if x is INF or if the underlying instrument is not in the Alpha universe. This operator may help reduce outliers.
8	log	Arithmetic	Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
9	purify	Arithmetic	Clear infinities (+inf, -inf) by replacing with NaN
10	arc_tan	Arithmetic	This operator does inverse tangent of input
11	max	Arithmetic	Maximum value of all inputs. At least 2 inputs are required
12	arc_sin	Arithmetic	If -1 <= x <= 1: arcsin(x); else NaN
13	to_nan	Arithmetic	Convert value to NaN or NaN to value if reverse=true
14	abs	Arithmetic	Absolute value of x
15	round_down	Arithmetic	Round input to greatest multiple of f less than input;
16	sigmoid	Arithmetic	Returns 1 / (1 + exp(-x))
17	divide	Arithmetic	x / y
18	min	Arithmetic	Minimum value of all inputs. At least 2 inputs are required
19	nan_mask	Arithmetic	replace input with NAN if input's corresponding mask value or the second input here, is negative
20	arc_cos	Arithmetic	If -1 <= x <= 1: arccos(x); else NaN
21	tanh	Arithmetic	Hyperbolic tangent of x
22	nan_out	Arithmetic	If x < lower or x > upper return NaN, else return x. At least one of "lower", "upper" is required
23	signed_power	Arithmetic	x raised to the power of y such that final result preserves sign of x
24	inverse	Arithmetic	1 / x
25	round	Arithmetic	Round input to closest integer.
26	sqrt	Arithmetic	Square root of x
27	s_log_1p	Arithmetic	Confine function to a shorter range using logarithm such that higher input remains higher and negative input remains negative as an output of resulting function and -1 or 1 is an asymptotic value
28	reverse	Arithmetic	- x
29	replace	Arithmetic	Replace target values in input with destination values
30	log_diff	Arithmetic	Returns log(current value of input or x[t] ) - log(previous value of input or x[t-1])
31	power	Arithmetic	x ^ y
32	densify	Arithmetic	Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient
33	fraction	Arithmetic	This operator removes the whole number part and returns the remaining fraction part with sign
34	floor	Arithmetic	Nearest smaller integer
35	ceiling	Arithmetic	Nearest larger integer
36	or	Logical	Logical OR operator returns true if either or both inputs are true and returns false otherwise
37	and	Logical	Logical AND operator, returns true if both operands are true and returns false otherwise
38	not	Logical	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).
39	is_not_finite	Logical	If (input NAN or input == INF) return 1 else return 0
40	negate	Logical	The result is true if the converted operand is false; the result is false if the converted operand is true
41	is_nan	Logical	If (input == NaN) return 1 else return 0
42	is_not_nan	Logical	If (input != NaN) return 1 else return 0
43	less	Logical	If input1 < input2 return true, else return false
44	equal	Logical	Returns true if both inputs are same and returns false otherwise
45	greater	Logical	Logic comparison operators to compares two inputs
46	is_finite	Logical	If (input NaN or input == INF) return 0, else return 1
47	if_else	Logical	If input1 is true then return input2 else return input3.
48	not_equal	Logical	Returns true if both inputs are NOT the same and returns false otherwise
49	less_equal	Logical	Returns true if input1 <= input2, return false otherwise
50	greater_equal	Logical	Returns true if input1 >= input2, return false otherwise
51	ts_corr	Time Series	Returns correlation of x and y for the past d days
52	ts_zscore	Time Series	Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean: (x - tsmean(x,d)) / tsstddev(x,d). This operator may help reduce outliers and drawdown.
53	ts_returns	Time Series	Returns the relative change in the x value
54	ts_product	Time Series	Returns product of x for the past d days
55	ts_std_dev	Time Series	Returns standard deviation of x for the past d days
56	ts_triple_corr	Time Series	Returns triple correlation of x, y, z for the past d days
57	ts_backfill	Time Series	Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
58	days_from_last_change	Time Series	Amount of days since last change of x
59	last_diff_value	Time Series	Returns last x value not equal to current x value from last d days
60	ts_scale	Time Series	Returns (x - ts_min(x, d)) / (ts_max(x, d) - ts_min(x, d)) + constant. This operator is similar to scale down operator but acts in time series space
61	ts_entropy	Time Series	For each instrument, we collect values of input in the past d days and calculate the probability distribution then the information entropy via a histogram as a result
62	ts_vector_neut	Time Series	Returns x- ts_vector_proj(x,y,d)
63	ts_percentage	Time Series	Returns percentile value of x for the past d days
64	ts_step	Time Series	Returns days' counter
65	ts_sum	Time Series	Sum values of x for the past d days.
66	ts_co_kurtosis	Time Series	Returns cokurtosis of y and x for the past d days
67	ts_partial_corr	Time Series	Returns partial correlation of x, y, z for the past d days
68	inst_tvr	Time Series	Total trading value / Total holding value in the past d days
69	ts_decay_exp_window	Time Series	Returns exponential decay of x with smoothing factor for the past d days
70	ts_av_diff	Time Series	Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
71	ts_rank_gmean_amean_diff	Time Series	Returns Geometric Mean of ts_rank(input,d) of all input - Arithmetic Mean of ts_rank(input,d) of all input. This is similar to rank_gmean_amean_diff operator but in time-series space
72	ts_kurtosis	Time Series	Returns kurtosis of x for the last d days
73	ts_mean	Time Series	Returns average value of x for the past d days.
74	ts_min_max_diff	Time Series	Returns x - f * (ts_min(x, d) + ts_max(x, d)). If not specified, by default f = 0.5
75	ts_arg_max	Time Series	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
76	ts_max	Time Series	Returns max value of x for the past d days
77	ts_min_max_cps	Time Series	Returns (ts_min(x, d) + ts_max(x, d)) - f * x. If not specified, by default f = 2
78	ts_rank	Time Series	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.
79	ts_ir	Time Series	Return information ratio ts_mean(x, d) / ts_std_dev(x, d)
80	ts_delay	Time Series	Returns x value d days ago
81	ts_theilsen	Time Series	Theil Sen slope estimator of inputs for the past n days.
82	hump_decay	Time Series	This operator helps to ignore the values that changed too little corresponding to previous ones
83	ts_weighted_decay	Time Series	Instead of replacing today's value with yesterday's as in ts_decay(x, 1), it assigns weighted average of today's and yesterday's values with weight on today's value being k and yesterday's being (1-k)
84	ts_quantile	Time Series	It calculates ts_rank and apply to its value an inverse cumulative density function from driver distribution. Possible values of driver (optional ) are "gaussian", "uniform", "cauchy" distribution where "gaussian" is the default.
85	ts_min	Time Series	Returns min value of x for the past d days
86	ts_count_nans	Time Series	Returns the number of NaN values in x for the past d days
87	ts_covariance	Time Series	Returns covariance of y and x for the past d days
88	ts_co_skewness	Time Series	Returns coskewness of y and x for the past d days
89	ts_min_diff	Time Series	Returns x - ts_min(x, d)
90	ts_decay_linear	Time Series	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.
91	jump_decay	Time Series	If there is a huge jump in current data compare to previous one
92	ts_moment	Time Series	Returns K-th central moment of x for the past d days
93	ts_arg_min	Time Series	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.
94	ts_regression	Time Series	Returns various parameters related to regression function
95	ts_skewness	Time Series	Return skewness of x for the past d days
96	ts_max_diff	Time Series	Returns x - ts_max(x, d)
97	kth_element	Time Series	Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
98	hump	Time Series	Limits amount and magnitude of changes in input (thus reducing turnover)
99	ts_median	Time Series	Returns median value of x for the past d days
100	ts_delta	Time Series	Returns x - ts_delay(x, d)
101	ts_poly_regression	Time Series	Returns y - Ey, where Ey = x + x^2 + ... + x^k over d days; k must be specified as a keyword argument
102	ts_vector_proj	Time Series	Returns vector projection of x onto y in time-series space.
103	ts_target_tvr_decay	Time Series	Tune "ts_decay" to have a turnover equal to a certain target, with optimization weight range between lambda_min, lambda_max
104	ts_target_tvr_delta_limit	Time Series	Tune "ts_delta_limit" to have a turnover equal to a certain target with optimization weight range between lambda_min, lambda_max. Also, please be aware of the scaling for x and y. Besides setting y as adv20 or volume related data, you can also set y as a constant.
105	ts_target_tvr_hump	Time Series	Tune "hump" to have a turnover equal to a certain target with optimization weight range between lambda_min, lambda_max.
106	ts_delta_limit	Time Series	Limit the change in the Alpha position x between dates to a specified fraction of y. The "limit_volume" can be in the range of 0 to 1. Also, please be aware of the scaling for x and y. Besides setting y as adv20 or volume related data, you can also set y as a constant.
107	winsorize	Cross Sectional	Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std.
108	rank	Cross Sectional	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
109	regression_proj	Cross Sectional	Conducts the cross-sectional regression on the stocks with Y as target and X as the independent variable
110	vector_neut	Cross Sectional	For given vectors x and y, it finds a new vector x* (output) such that x* is orthogonal to y
111	regression_neut	Cross Sectional	Conducts the cross-sectional regression on the stocks with Y as target and X as the independent variable
112	zscore	Cross Sectional	Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean
113	scale_down	Cross Sectional	Scales all values in each day proportionately between 0 and 1 such that minimum value maps to 0 and maximum value maps to 1. Constant is the offset by which final result is subtracted
114	truncate	Cross Sectional	Operator truncates all values of x to maxPercent. Here, maxPercent is in decimal notation
115	scale	Cross Sectional	Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator
116	rank_by_side	Cross Sectional	Ranks positive and negative input separately and scale to book. For precise sorting use rate=0
117	normalize	Cross Sectional	Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
118	generalized_rank	Cross Sectional	The idea is that difference between instrument values raised to the power of m is added to the rank of instrument with bigger value and subtracted from the rank of instrument with lesser value. More details in the notes at the end of page
119	one_side	Cross Sectional	Shifts all instruments up or down so that the Alpha becomes long-only or short-only (if side = short), respectively.
120	rank_gmean_amean_diff	Cross Sectional	Operator returns difference of geometric mean and arithmetic mean of cross sectional rank of inputs
121	quantile	Cross Sectional	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
122	vector_proj	Cross Sectional	Returns vector projection of x onto y.
123	multi_regression	Cross Sectional	Perform multivariable regression of multiple independent variables (x1,x2,...) on a dependent variable (y) across number of days. If days = n (n >= 0), will include past n days in the regression. Setting days = 0 indicates that we're only using today (x1, x2, ...) to predict y. If lag = m (m >= 0), will lag (x1, x2, ...) for m days for prediction. Options for solver: "SVD"(default), "QR", "NORMAL"