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194 lines
19 KiB
194 lines
19 KiB
任务指令
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供应链数字化成熟度
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[[假设]]
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供应链数字化程度较高的公司能够实时优化库存与物流效率，从而显著降低运营中断风险并提升盈利能力。当行业面临外部冲击时，这类公司往往表现出更强的抗压能力，导致其股价在波动市场中获得超额回报。
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[[实施方案]]
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通过“ts_backfill”算子处理历史缺失数据，并将该指标除以行业平均值以消除行业间差异。对数字化成熟度持续上升的公司建立多头仓位，反之则建立空头仓位。
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[[阿尔法因子优化建议]]
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建议采用动态分层算子，按公司规模与所属供应链类型（如制造业/零售业）进行子行业分组，并叠加横截面分位数算子，以捕捉数字化效益在不同周期中的非线性特征。这能有效减少宏观波动对策略的干扰。
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*=========================================================================================*
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输出格式：
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输出必须是且仅是纯文本。
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每一行是一个完整、独立、语法正确的WebSim表达式。
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严禁任何形式的解释、编号、标点包裹（如引号）、Markdown格式或额外文本。
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===================== !!! 重点(输出方式) !!! =====================
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现在，请严格遵守以上所有规则，开始生成可立即在WebSim中运行的复合因子表达式。
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**输出格式**(一行一个表达式, 每个表达式中间需要添加一个空行, 只要表达式本身, 不需要赋值, 不要解释, 不需要序号, 也不要输出多余的东西)：
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表达式
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表达式
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表达式
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...
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表达式
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=================================================================
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重申：请确保所有表达式都使用WorldQuant WebSim平台函数，不要使用pandas、numpy或其他Python库函数。输出必须是一行有效的WQ表达式。
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以下是我的账号有权限使用的操作符, 请严格按照操作符, 以及我提供的数据集, 进行生成,组合 20 个alpha：
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以下是我的账号有权限使用的操作符, 请严格按照操作符, 进行生成,组合因子
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========================= 操作符开始 =======================================注意: Operator: 后面的是操作符,
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Description: 此字段后面的是操作符对应的描述或使用说明, Description字段后面的内容是使用说明, 不是操作符
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特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
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Description: Absolute value of x
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Operator: add(x, y, filter = false)
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Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
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Operator: densify(x)
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Description: Converts a grouping field of many buckets into lesser number of only available buckets so as to make working with grouping fields computationally efficient
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Operator: divide(x, y)
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Description: x / y
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Operator: inverse(x)
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Description: 1 / x
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Operator: log(x)
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Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
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Operator: max(x, y, ..)
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Description: Maximum value of all inputs. At least 2 inputs are required
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Operator: min(x, y ..)
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Description: Minimum value of all inputs. At least 2 inputs are required
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Operator: multiply(x ,y, ... , filter=false)
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Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
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Operator: power(x, y)
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Description: x ^ y
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Operator: reverse(x)
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Description: - x
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Operator: sign(x)
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Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
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Operator: signed_power(x, y)
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Description: x raised to the power of y such that final result preserves sign of x
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Operator: sqrt(x)
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Description: Square root of x
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Operator: subtract(x, y, filter=false)
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Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
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Operator: and(input1, input2)
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Description: Logical AND operator, returns true if both operands are true and returns false otherwise
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Operator: if_else(input1, input2, input 3)
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Description: If input1 is true then return input2 else return input3.
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Operator: input1 < input2
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Description: If input1 < input2 return true, else return false
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Operator: input1 <= input2
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Description: Returns true if input1 <= input2, return false otherwise
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Operator: input1 == input2
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Description: Returns true if both inputs are same and returns false otherwise
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Operator: input1 > input2
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Description: Logic comparison operators to compares two inputs
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Operator: input1 >= input2
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Description: Returns true if input1 >= input2, return false otherwise
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Operator: input1!= input2
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Description: Returns true if both inputs are NOT the same and returns false otherwise
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Operator: is_nan(input)
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Description: If (input == NaN) return 1 else return 0
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Operator: not(x)
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Description: 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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Operator: or(input1, input2)
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Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
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Operator: days_from_last_change(x)
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Description: Amount of days since last change of x
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Operator: hump(x, hump = 0.01)
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Description: Limits amount and magnitude of changes in input (thus reducing turnover)
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Operator: kth_element(x, d, k)
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Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
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Operator: last_diff_value(x, d)
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Description: Returns last x value not equal to current x value from last d days
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Operator: ts_arg_max(x, d)
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Description: 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
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Operator: ts_arg_min(x, d)
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Description: 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.
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Operator: ts_av_diff(x, d)
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Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
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Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
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Description: 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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Operator: ts_corr(x, y, d)
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Description: Returns correlation of x and y for the past d days
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Operator: ts_count_nans(x ,d)
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Description: Returns the number of NaN values in x for the past d days
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Operator: ts_covariance(y, x, d)
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Description: Returns covariance of y and x for the past d days
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Operator: ts_decay_linear(x, d, dense = false)
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Description: 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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Operator: ts_delay(x, d)
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Description: Returns x value d days ago
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Operator: ts_delta(x, d)
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Description: Returns x - ts_delay(x, d)
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Operator: ts_mean(x, d)
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Description: Returns average value of x for the past d days.
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Operator: ts_product(x, d)
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Description: Returns product of x for the past d days
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Operator: ts_quantile(x,d, driver="gaussian" )
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Description: 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.
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Operator: ts_rank(x, d, constant = 0)
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Description: 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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Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
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Description: Returns various parameters related to regression function
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Operator: ts_scale(x, d, constant = 0)
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Description: 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
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Operator: ts_std_dev(x, d)
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Description: Returns standard deviation of x for the past d days
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Operator: ts_step(1)
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Description: Returns days' counter
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Operator: ts_sum(x, d)
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Description: Sum values of x for the past d days.
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Operator: ts_zscore(x, d)
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Description: 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.
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Operator: normalize(x, useStd = false, limit = 0.0)
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Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
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Operator: quantile(x, driver = gaussian, sigma = 1.0)
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Description: 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
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Operator: rank(x, rate=2)
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Description: 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
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Operator: scale(x, scale=1, longscale=1, shortscale=1)
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Description: Scales input to booksize. We can also scale the long positions and short positions to separate scales by mentioning additional parameters to the operator
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Operator: winsorize(x, std=4)
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Description: Winsorizes x to make sure that all values in x are between the lower and upper limits, which are specified as multiple of std.
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Operator: zscore(x)
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Description: 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
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Operator: vec_avg(x)
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Description: Taking mean of the vector field x
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Operator: vec_sum(x)
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Description: Sum of vector field x
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Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
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Description: Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input
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Operator: trade_when(x, y, z)
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Description: 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
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Operator: group_backfill(x, group, d, std = 4.0)
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Description: If a certain value for a certain date and instrument is NaN, from the set of same group instruments, calculate winsorized mean of all non-NaN values over last d days
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Operator: group_mean(x, weight, group)
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Description: All elements in group equals to the mean
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Operator: group_neutralize(x, group)
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Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
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Operator: group_rank(x, group)
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Description: Each elements in a group is assigned the corresponding rank in this group
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Operator: group_scale(x, group)
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Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
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Operator: group_zscore(x, group)
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Description: Calculates group Z-score - 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. zscore = (data - mean) / stddev of x for each instrument within its group.
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========================= 操作符结束 =======================================
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========================= 数据字段开始 =======================================
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注意: data_set_name: 后面的是数据字段(可以使用), description: 此字段后面的是数据字段对应的描述或使用说明(不能使用), description_cn字段后面的内容是中文使用说明(不能使用)
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{'id': 'id', 'data_set_name': 'name', 'description': 'description', 'description_cn': 'description_cn'}
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{'id': '15', 'data_set_name': 'forward_price_120', 'description': 'Forward price at 120 days derived from a synthetic long option with payoff similar to long stock + option dynamics. Combination of long ATM call and short ATM put.', 'description_cn': '行权价格_120天_合成多头看涨期权_ATM看涨+看跌动态_long_ATM看涨_short_ATM看跌'}
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{'id': '413', 'data_set_name': 'fnd6_newa2v1300_oiadp', 'description': 'Operating Income After Depreciation', 'description_cn': '营业净利润 after depreciation 不变，无需翻译。请提供需要翻译的专业字段名。'}
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{'id': '478', 'data_set_name': 'fnd6_newqeventv110_cipenq', 'description': 'Comp Inc - Minimum Pension Adj', 'description_cn': '-compIncMinPensionAdj'}
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{'id': '691', 'data_set_name': 'fnd6_newqv1300_cipenq', 'description': 'Comp Inc - Minimum Pension Adj', 'description_cn': '-comp_inc_min_pension_adj'}
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{'id': '997', 'data_set_name': 'fscore_total', 'description': 'The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.', 'description_cn': '最终得分M-Score是pentagon表面分数和pentagon加速度分数的加权平均值。'}
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{'id': '1000', 'data_set_name': 'multi_factor_acceleration_score_derivative', 'description': 'Change in the acceleration of multi-factor score compared to previous period.', 'description_cn': '多因子评分加速度变化 Compared_to_Previous_Period_Multi_Factor_Score_Acceleration_Change'}
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{'id': '2057', 'data_set_name': 'nws12_afterhsz_tonhigh', 'description': 'Highest price reached during the session before the time of news', 'description_cn': '新闻公布前交易session中最高价'}
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{'id': '2067', 'data_set_name': 'nws12_allz_result2', 'description': 'Percent change between the price at the time of the news release and the price at the close of the session', 'description_cn': '新闻发布日至交易session结束期间价格百分比变化'}
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{'id': '2144', 'data_set_name': 'nws12_mainz_tonhigh', 'description': 'Highest price reached during the session before the time of news', 'description_cn': '新闻发布时间前交易session中最高价'}
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{'id': '2306', 'data_set_name': 'rp_nip_ratings', 'description': 'News impact projection of analyst ratings-related news', 'description_cn': '分析师评级相关新闻的/news_impact_projection'}
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{'id': '2354', 'data_set_name': 'fn_comp_not_rec_a', 'description': 'Unrecognized cost of unvested share-based compensation awards.', 'description_cn': '未兑现股份薪酬 award 的 unrecognized cost'}
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{'id': '2356', 'data_set_name': 'fn_comp_not_rec_stock_options_a', 'description': 'Unrecognized cost of unvested stock option awards.', 'description_cn': '未行权股票期权 unrecognized_cost_of_unvested_stock_options'}
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{'id': '2357', 'data_set_name': 'fn_comp_not_rec_stock_options_q', 'description': 'Unrecognized cost of unvested stock option awards.', 'description_cn': '未行权股票期权 unrecognized_cost_of_unvested_stock_options'}
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{'id': '2393', 'data_set_name': 'fn_def_tax_assets_liab_net_q', 'description': 'Amount, after allocation of valuation allowances and deferred tax liability, of deferred tax asset attributable to deductible differences and carryforwards, without jurisdictional netting.', 'description_cn': '扣除减值准备和递延税负债后，attributable_to_deferred_tax_asset_amount'}
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{'id': '2410', 'data_set_name': 'fn_entity_common_stock_shares_out_a', 'description': "Indicate number of shares or other units outstanding of each of registrant's classes of capital or common stock or other ownership interests, if and as stated on cover of related periodic report. Where multiple classes or units exist define each class/interest by adding class of stock items such as Common Class A [Member], Common Class B [Member] or Partnership Interest [Member] onto the Instrument [Domain] of the Entity Listings, Instrument.", 'description_cn': '标明每个注册类别资本或普通股票或其他所有权利益的已发行股份数量或其他单位数量，如相关定期报告封面上所示。如有多个类别或单位存在，则通过在实体列表Instrument[Domain]项下添加类似于Common Class A [Member]、Common Class B [Member]或Partnership Interest [Member]的分类来定义每个类别/利益。'}
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{'id': '2411', 'data_set_name': 'fn_entity_common_stock_shares_out_q', 'description': "Indicate number of shares or other units outstanding of each of registrant's classes of capital or common stock or other ownership interests, if and as stated on cover of related periodic report. Where multiple classes or units exist define each class/interest by adding class of stock items such as Common Class A [Member], Common Class B [Member] or Partnership Interest [Member] onto the Instrument [Domain] of the Entity Listings, Instrument.", 'description_cn': '标明每个注册类别资本或普通股或其他所有权利益的发行在外量，如相关定期报告封面所示。如有多个类别或单位存在，请在实体列表Instrument[Domain]中通过添加如CommonClassA[Member]、CommonClassB[Member]或PartnershipInterest[Member]等类别/利益项来定义每个类别/利益。'}
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{'id': '2436', 'data_set_name': 'fn_liab_fair_val_a', 'description': 'Liabilities Fair Value, Recurring, Total', 'description_cn': '看涨期权负债公允价值_ recurring_total'}
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{'id': '2437', 'data_set_name': 'fn_liab_fair_val_l1_a', 'description': 'Liabilities Fair Value, Recurring, Level 1', 'description_cn': '看涨期权负债公允价值_ recurring_level_1'}
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{'id': '2438', 'data_set_name': 'fn_liab_fair_val_l1_q', 'description': 'Liabilities Fair Value, Recurring, Level 1', 'description_cn': '看涨期权公允价值_ recurring_level_1'}
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{'id': '2439', 'data_set_name': 'fn_liab_fair_val_l2_a', 'description': 'Liabilities Fair Value, Recurring, Level 2', 'description_cn': '看涨期权公允价值_ recurring_level_2'}
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{'id': '2440', 'data_set_name': 'fn_liab_fair_val_l2_q', 'description': 'Liabilities Fair Value, Recurring, Level 2', 'description_cn': '看涨期权负债公允价值_ recurring_level_2'}
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{'id': '2468', 'data_set_name': 'fn_oth_income_loss_fx_transaction_and_tax_translation_adj_q', 'description': 'Amount after tax and reclassification adjustments of gain (loss) on foreign currency translation adjustments, foreign currency transactions designated and effective as economic hedges of a net investment in a foreign entity and intra-entity foreign currency transactions that are of a long-term-investment nature.', 'description_cn': '所得税及重分类后外币translation调整、外币交易指定并有效作为净投资经济套期和长期投资性质的内部外币交易产生的损益金额'}
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{'id': '2501', 'data_set_name': 'fn_treasury_stock_shares_a', 'description': 'Number of common and preferred shares that were previously issued and that were repurchased by the issuing entity and held in treasury on the financial statement date. This stock has no voting rights and receives no dividends.', 'description_cn': 'treasury_repossession_shares_num'}
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{'id': '2524', 'data_set_name': 'fnd2_a_fedstyitxrt', 'description': 'Effective Income Tax Rate Reconciliation - Federal Statutory Income Tax Rate %', 'description_cn': '有效所得税率 reconciliation - 国家法定所得税率 %'}
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========================= 数据字段结束 =======================================
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以上数据字段和操作符, 按照Description说明组合, 但是每一个 alpha 组合的使用的数据字段和操作符不要过于集中, 在符合语法的情况下, 多尝试不同的组合 |