++
This commit is contained in:
jack
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任务指令
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**名称**
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机构投资者持仓分歧度因子
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**假设**
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当同一行业内多家机构投资者对某只股票的持仓方向出现显著分歧（如部分机构大幅增持而其他机构大幅减持）时，可能预示市场对该股票的未来预期存在较大不确定性。这种分歧短期内可能加剧股价波动，但中长期会随基本面明朗化逐步收敛——持仓共识度逐步提升的股票更可能形成趋势性行情。
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**实施方案**
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基于机构持仓数据，计算每季度末各股票在同类机构（如公募、保险、QFII等）群体中的持仓变动标准差，衡量持仓分歧度。使用时序衰减算子（如ts_decay_linear）对近4个季度的分歧度进行加权，捕捉分歧度的变化趋势。对分歧度持续收窄且机构净流入的股票赋予多头权重，对分歧度扩大或机构净流出的股票赋予空头权重。
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**阿尔法因子优化建议**
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建议按机构类型分组计算分歧度（如长线资金vs短线资金），并采用横截面中性化处理消除行业偏好影响。可引入动量放大器：当股价已处于上升通道且分歧度收敛时，加大权重分配；若股价与分歧度背离，则降低信号强度。
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逻辑的核心在于“趋势”与“相对位置”的交互。建议引入横截面动量算子（如“rank”）对最终的斜率值进行再排序，以强化头部与尾部信号。同时，可考虑对营业收入增长率施加条件约束（例如，仅当营收增长率非负时信号生效），以避免因收缩业务导致毛利率被动提升的噪音干扰。
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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。
|
||||
每一行是一个完整、独立、语法正确的WebSim表达式。
|
||||
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。
|
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
|
||||
**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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||||
=================================================================
|
||||
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。
|
||||
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：
|
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
|
||||
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=================================================================
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以下是错误的组合, 请勿类似的操作：
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Operator ts_product does not support event inputs
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Operator ts_zscore does not support event inputs
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Operator ts_mean does not support event inputs
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Operator ts_scale does not support event inputs
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Operator add does not support event inputs
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Operator sign does not support event inputs
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Operator subtract does not support event inputs
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Operator ts_delta does not support event inputs
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Operator ts_rank does not support event inputs
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Operator greater does not support event inputs
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Operator ts_av_diff does not support event inputs
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Operator ts_quantile does not support event inputs
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Operator ts_count_nans does not support event inputs
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Operator ts_covariance does not support event inputs
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Operator ts_arg_min does not support event inputs
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Operator divide does not support event inputs
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Operator ts_corr does not support event inputs
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Operator multiply does not support event inputs
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Operator if_else does not support event inputs
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Operator ts_sum does not support event inputs
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Operator ts_delay does not support event inputs
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Operator group_zscore does not support event inputs
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Operator ts_arg_max does not support event inputs
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Operator ts_std_dev does not support event inputs
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Operator ts_backfill does not support event inputs
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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 =======================================
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注意: Operator: 后面的是操作符(是可以使用的),
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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(禁止使用, 仅供参考), 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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{'data_set_name': '可以使用:forward_price_120', 'description': '不可使用,仅供参考: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.'}
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{'data_set_name': '可以使用:fnd6_newqeventv110_cibegniq', 'description': '不可使用,仅供参考:Comp Inc - Beginning Net Income'}
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{'data_set_name': '可以使用:anl4_netdebt_flag', 'description': '不可使用,仅供参考:Net debt - forecast type (revision/new/...)'}
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{'data_set_name': '可以使用:news_max_up_amt', 'description': '不可使用,仅供参考:The after the news high minus the price at the time of the news'}
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{'data_set_name': '可以使用:fnd2_a_ltrmdmrepopliny5', 'description': '不可使用,仅供参考:Amount of long-term debt payable, sinking fund requirements, and other securities issued that are redeemable by holder at fixed or determinable prices and dates maturing in the 5th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.'}
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========================= 数据字段结束 =======================================
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||||
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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
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@@ -0,0 +1,205 @@
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任务指令
|
||||
**Name**
|
||||
Institutional Investor Holding Divergence Factor
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**Hypothesis**
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When institutional investors within the same industry show significant divergence in their positioning toward a stock (e.g., some institutions substantially increase holdings while others sharply reduce), it may indicate high uncertainty in market expectations for the stock. Such divergence could amplify short-term price volatility, but over the medium to long term, as fundamentals become clearer, consensus tends to converge—stocks with improving holding consensus are more likely to develop trend-driven returns.
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**Implementation**
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Using institutional holding data, calculate the standard deviation of quarterly holding changes for each stock across peer institution groups (e.g., mutual funds, insurance, QFII) to measure holding divergence. Apply a time-series decay operator (e.g., ts_decay_linear) to weight divergence over the past four quarters, capturing its trend. Assign long weights to stocks with consistently narrowing divergence and net institutional inflows, and short weights to those with widening divergence or net outflows.
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**Alpha Factor Optimization Suggestions**
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Recommend calculating divergence by institution type (e.g., long-term vs. short-term capital) and applying cross-sectional neutralization to eliminate industry biases. Consider incorporating a momentum amplifier: increase weight allocation when the stock price is in an upward trend and divergence is converging; reduce signal strength if price action and divergence diverge.
|
||||
|
||||
*=========================================================================================*
|
||||
输出格式：
|
||||
输出必须是且仅是纯文本。
|
||||
每一行是一个完整、独立、语法正确的WebSim表达式。
|
||||
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。
|
||||
===================== !!! 重点(输出方式) !!! =====================
|
||||
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。
|
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
|
||||
**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)：
|
||||
表达式
|
||||
表达式
|
||||
表达式
|
||||
...
|
||||
表达式
|
||||
=================================================================
|
||||
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。
|
||||
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：
|
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
|
||||
|
||||
=================================================================
|
||||
以下是错误的组合, 请勿类似的操作：
|
||||
Operator ts_product does not support event inputs
|
||||
Operator ts_zscore does not support event inputs
|
||||
Operator ts_mean does not support event inputs
|
||||
Operator ts_scale does not support event inputs
|
||||
Operator add does not support event inputs
|
||||
Operator sign does not support event inputs
|
||||
Operator subtract does not support event inputs
|
||||
Operator ts_delta does not support event inputs
|
||||
Operator ts_rank does not support event inputs
|
||||
Operator greater does not support event inputs
|
||||
Operator ts_av_diff does not support event inputs
|
||||
Operator ts_quantile does not support event inputs
|
||||
Operator ts_count_nans does not support event inputs
|
||||
Operator ts_covariance does not support event inputs
|
||||
Operator ts_arg_min does not support event inputs
|
||||
Operator divide does not support event inputs
|
||||
Operator ts_corr does not support event inputs
|
||||
Operator multiply does not support event inputs
|
||||
Operator if_else does not support event inputs
|
||||
Operator ts_sum does not support event inputs
|
||||
Operator ts_delay does not support event inputs
|
||||
Operator group_zscore does not support event inputs
|
||||
Operator ts_arg_max does not support event inputs
|
||||
Operator ts_std_dev does not support event inputs
|
||||
Operator ts_backfill does not support event inputs
|
||||
|
||||
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
|
||||
|
||||
========================= 操作符开始 =======================================
|
||||
注意: Operator: 后面的是操作符(是可以使用的),
|
||||
Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符
|
||||
特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
|
||||
Description: Absolute value of x
|
||||
Operator: add(x, y, filter = false)
|
||||
Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
|
||||
Operator: densify(x)
|
||||
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
|
||||
Operator: divide(x, y)
|
||||
Description: x / y
|
||||
Operator: inverse(x)
|
||||
Description: 1 / x
|
||||
Operator: log(x)
|
||||
Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
|
||||
Operator: max(x, y, ..)
|
||||
Description: Maximum value of all inputs. At least 2 inputs are required
|
||||
Operator: min(x, y ..)
|
||||
Description: Minimum value of all inputs. At least 2 inputs are required
|
||||
Operator: multiply(x ,y, ... , filter=false)
|
||||
Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
|
||||
Operator: power(x, y)
|
||||
Description: x ^ y
|
||||
Operator: reverse(x)
|
||||
Description: - x
|
||||
Operator: sign(x)
|
||||
Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
|
||||
Operator: signed_power(x, y)
|
||||
Description: x raised to the power of y such that final result preserves sign of x
|
||||
Operator: sqrt(x)
|
||||
Description: Square root of x
|
||||
Operator: subtract(x, y, filter=false)
|
||||
Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
|
||||
Operator: and(input1, input2)
|
||||
Description: Logical AND operator, returns true if both operands are true and returns false otherwise
|
||||
Operator: if_else(input1, input2, input 3)
|
||||
Description: If input1 is true then return input2 else return input3.
|
||||
Operator: input1 < input2
|
||||
Description: If input1 < input2 return true, else return false
|
||||
Operator: input1 <= input2
|
||||
Description: Returns true if input1 <= input2, return false otherwise
|
||||
Operator: input1 == input2
|
||||
Description: Returns true if both inputs are same and returns false otherwise
|
||||
Operator: input1 > input2
|
||||
Description: Logic comparison operators to compares two inputs
|
||||
Operator: input1 >= input2
|
||||
Description: Returns true if input1 >= input2, return false otherwise
|
||||
Operator: input1!= input2
|
||||
Description: Returns true if both inputs are NOT the same and returns false otherwise
|
||||
Operator: is_nan(input)
|
||||
Description: If (input == NaN) return 1 else return 0
|
||||
Operator: not(x)
|
||||
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).
|
||||
Operator: or(input1, input2)
|
||||
Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
|
||||
Operator: days_from_last_change(x)
|
||||
Description: Amount of days since last change of x
|
||||
Operator: hump(x, hump = 0.01)
|
||||
Description: Limits amount and magnitude of changes in input (thus reducing turnover)
|
||||
Operator: kth_element(x, d, k)
|
||||
Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
|
||||
Operator: last_diff_value(x, d)
|
||||
Description: Returns last x value not equal to current x value from last d days
|
||||
Operator: ts_arg_max(x, d)
|
||||
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
|
||||
Operator: ts_arg_min(x, d)
|
||||
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.
|
||||
Operator: ts_av_diff(x, d)
|
||||
Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
|
||||
Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
|
||||
Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
|
||||
Operator: ts_corr(x, y, d)
|
||||
Description: Returns correlation of x and y for the past d days
|
||||
Operator: ts_count_nans(x ,d)
|
||||
Description: Returns the number of NaN values in x for the past d days
|
||||
Operator: ts_covariance(y, x, d)
|
||||
Description: Returns covariance of y and x for the past d days
|
||||
Operator: ts_decay_linear(x, d, dense = false)
|
||||
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.
|
||||
Operator: ts_delay(x, d)
|
||||
Description: Returns x value d days ago
|
||||
Operator: ts_delta(x, d)
|
||||
Description: Returns x - ts_delay(x, d)
|
||||
Operator: ts_mean(x, d)
|
||||
Description: Returns average value of x for the past d days.
|
||||
Operator: ts_product(x, d)
|
||||
Description: Returns product of x for the past d days
|
||||
Operator: ts_quantile(x,d, driver="gaussian" )
|
||||
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.
|
||||
Operator: ts_rank(x, d, constant = 0)
|
||||
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.
|
||||
Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
|
||||
Description: Returns various parameters related to regression function
|
||||
Operator: ts_scale(x, d, constant = 0)
|
||||
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
|
||||
Operator: ts_std_dev(x, d)
|
||||
Description: Returns standard deviation of x for the past d days
|
||||
Operator: ts_step(1)
|
||||
Description: Returns days' counter
|
||||
Operator: ts_sum(x, d)
|
||||
Description: Sum values of x for the past d days.
|
||||
Operator: ts_zscore(x, d)
|
||||
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.
|
||||
Operator: normalize(x, useStd = false, limit = 0.0)
|
||||
Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
|
||||
Operator: quantile(x, driver = gaussian, sigma = 1.0)
|
||||
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
|
||||
Operator: rank(x, rate=2)
|
||||
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
|
||||
Operator: scale(x, scale=1, longscale=1, shortscale=1)
|
||||
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
|
||||
Operator: winsorize(x, std=4)
|
||||
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.
|
||||
Operator: zscore(x)
|
||||
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
|
||||
Operator: vec_avg(x)
|
||||
Description: Taking mean of the vector field x
|
||||
Operator: vec_sum(x)
|
||||
Description: Sum of vector field x
|
||||
Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
|
||||
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
|
||||
Operator: trade_when(x, y, z)
|
||||
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
|
||||
Operator: group_backfill(x, group, d, std = 4.0)
|
||||
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
|
||||
Operator: group_mean(x, weight, group)
|
||||
Description: All elements in group equals to the mean
|
||||
Operator: group_neutralize(x, group)
|
||||
Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
|
||||
Operator: group_rank(x, group)
|
||||
Description: Each elements in a group is assigned the corresponding rank in this group
|
||||
Operator: group_scale(x, group)
|
||||
Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
|
||||
Operator: group_zscore(x, group)
|
||||
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.
|
||||
========================= 操作符结束 =======================================
|
||||
|
||||
========================= 数据字段开始 =======================================
|
||||
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)
|
||||
|
||||
{'data_set_name': '可以使用:forward_price_120', 'description': '不可使用,仅供参考: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.'}
|
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_cibegniq', 'description': '不可使用,仅供参考:Comp Inc - Beginning Net Income'}
|
||||
{'data_set_name': '可以使用:news_high_exc_stddev', 'description': '不可使用,仅供参考:(EODHigh - TONLast)/StdDev, where StdDev is one standard deviation for the close price for 30 calendar days'}
|
||||
========================= 数据字段结束 =======================================
|
||||
|
||||
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
|
||||
@@ -0,0 +1,211 @@
|
||||
任务指令
|
||||
**名称**
|
||||
机构投资者持仓集中度反转效应
|
||||
**假设**
|
||||
当一家公司的机构投资者持仓高度集中（如前五大机构持股比例显著高于历史中位数），可能意味着该股票短期内因资金抱团而出现估值泡沫；但随着机构调仓或共识分化，持仓集中度会逐步下降，导致股价回调。反之，若机构持仓集中度从低位持续回升，则预示机构资金重新形成共识，可能推动股价上涨。
|
||||
**实施方案**
|
||||
使用机构持股明细数据，计算每季度前五大机构持股比例之和的横截面分位数排名。通过时序差分算子（如 `ts_delta`）监测持仓集中度的变化方向与幅度，对集中度从高位（前30%）显著回落（分位数下降超过20%）的股票建立空头仓位，对集中度从低位（后30%）持续攀升（分位数上升超过20%）的股票建立多头仓位。
|
||||
**阿尔法因子优化建议**
|
||||
不同市值风格股票的机构行为差异显著（如大盘股机构持仓稳定性更高）。建议按市值分档进行动态中性化处理，并在因子中引入“机构持仓稳定性”指标（如持仓周期方差）作为调节参数，对稳定性低的股票赋予更高权重，以捕捉短期调仓带来的反转机会。
|
||||
|
||||
*=========================================================================================*
|
||||
输出格式：
|
||||
输出必须是且仅是纯文本。
|
||||
每一行是一个完整、独立、语法正确的WebSim表达式。
|
||||
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。
|
||||
===================== !!! 重点(输出方式) !!! =====================
|
||||
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。
|
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
|
||||
**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)：
|
||||
表达式
|
||||
表达式
|
||||
表达式
|
||||
...
|
||||
表达式
|
||||
=================================================================
|
||||
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。
|
||||
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：
|
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
|
||||
|
||||
=================================================================
|
||||
以下是错误的组合, 请勿类似的操作：
|
||||
Operator ts_product does not support event inputs
|
||||
Operator ts_zscore does not support event inputs
|
||||
Operator ts_mean does not support event inputs
|
||||
Operator ts_scale does not support event inputs
|
||||
Operator add does not support event inputs
|
||||
Operator sign does not support event inputs
|
||||
Operator subtract does not support event inputs
|
||||
Operator ts_delta does not support event inputs
|
||||
Operator ts_rank does not support event inputs
|
||||
Operator greater does not support event inputs
|
||||
Operator ts_av_diff does not support event inputs
|
||||
Operator ts_quantile does not support event inputs
|
||||
Operator ts_count_nans does not support event inputs
|
||||
Operator ts_covariance does not support event inputs
|
||||
Operator ts_arg_min does not support event inputs
|
||||
Operator divide does not support event inputs
|
||||
Operator ts_corr does not support event inputs
|
||||
Operator multiply does not support event inputs
|
||||
Operator if_else does not support event inputs
|
||||
Operator ts_sum does not support event inputs
|
||||
Operator ts_delay does not support event inputs
|
||||
Operator group_zscore does not support event inputs
|
||||
Operator ts_arg_max does not support event inputs
|
||||
Operator ts_std_dev does not support event inputs
|
||||
Operator ts_backfill does not support event inputs
|
||||
|
||||
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
|
||||
|
||||
========================= 操作符开始 =======================================
|
||||
注意: Operator: 后面的是操作符(是可以使用的),
|
||||
Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符
|
||||
特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
|
||||
Description: Absolute value of x
|
||||
Operator: add(x, y, filter = false)
|
||||
Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
|
||||
Operator: densify(x)
|
||||
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
|
||||
Operator: divide(x, y)
|
||||
Description: x / y
|
||||
Operator: inverse(x)
|
||||
Description: 1 / x
|
||||
Operator: log(x)
|
||||
Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
|
||||
Operator: max(x, y, ..)
|
||||
Description: Maximum value of all inputs. At least 2 inputs are required
|
||||
Operator: min(x, y ..)
|
||||
Description: Minimum value of all inputs. At least 2 inputs are required
|
||||
Operator: multiply(x ,y, ... , filter=false)
|
||||
Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
|
||||
Operator: power(x, y)
|
||||
Description: x ^ y
|
||||
Operator: reverse(x)
|
||||
Description: - x
|
||||
Operator: sign(x)
|
||||
Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
|
||||
Operator: signed_power(x, y)
|
||||
Description: x raised to the power of y such that final result preserves sign of x
|
||||
Operator: sqrt(x)
|
||||
Description: Square root of x
|
||||
Operator: subtract(x, y, filter=false)
|
||||
Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
|
||||
Operator: and(input1, input2)
|
||||
Description: Logical AND operator, returns true if both operands are true and returns false otherwise
|
||||
Operator: if_else(input1, input2, input 3)
|
||||
Description: If input1 is true then return input2 else return input3.
|
||||
Operator: input1 < input2
|
||||
Description: If input1 < input2 return true, else return false
|
||||
Operator: input1 <= input2
|
||||
Description: Returns true if input1 <= input2, return false otherwise
|
||||
Operator: input1 == input2
|
||||
Description: Returns true if both inputs are same and returns false otherwise
|
||||
Operator: input1 > input2
|
||||
Description: Logic comparison operators to compares two inputs
|
||||
Operator: input1 >= input2
|
||||
Description: Returns true if input1 >= input2, return false otherwise
|
||||
Operator: input1!= input2
|
||||
Description: Returns true if both inputs are NOT the same and returns false otherwise
|
||||
Operator: is_nan(input)
|
||||
Description: If (input == NaN) return 1 else return 0
|
||||
Operator: not(x)
|
||||
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).
|
||||
Operator: or(input1, input2)
|
||||
Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
|
||||
Operator: days_from_last_change(x)
|
||||
Description: Amount of days since last change of x
|
||||
Operator: hump(x, hump = 0.01)
|
||||
Description: Limits amount and magnitude of changes in input (thus reducing turnover)
|
||||
Operator: kth_element(x, d, k)
|
||||
Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
|
||||
Operator: last_diff_value(x, d)
|
||||
Description: Returns last x value not equal to current x value from last d days
|
||||
Operator: ts_arg_max(x, d)
|
||||
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
|
||||
Operator: ts_arg_min(x, d)
|
||||
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.
|
||||
Operator: ts_av_diff(x, d)
|
||||
Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
|
||||
Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
|
||||
Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
|
||||
Operator: ts_corr(x, y, d)
|
||||
Description: Returns correlation of x and y for the past d days
|
||||
Operator: ts_count_nans(x ,d)
|
||||
Description: Returns the number of NaN values in x for the past d days
|
||||
Operator: ts_covariance(y, x, d)
|
||||
Description: Returns covariance of y and x for the past d days
|
||||
Operator: ts_decay_linear(x, d, dense = false)
|
||||
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.
|
||||
Operator: ts_delay(x, d)
|
||||
Description: Returns x value d days ago
|
||||
Operator: ts_delta(x, d)
|
||||
Description: Returns x - ts_delay(x, d)
|
||||
Operator: ts_mean(x, d)
|
||||
Description: Returns average value of x for the past d days.
|
||||
Operator: ts_product(x, d)
|
||||
Description: Returns product of x for the past d days
|
||||
Operator: ts_quantile(x,d, driver="gaussian" )
|
||||
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.
|
||||
Operator: ts_rank(x, d, constant = 0)
|
||||
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.
|
||||
Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
|
||||
Description: Returns various parameters related to regression function
|
||||
Operator: ts_scale(x, d, constant = 0)
|
||||
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
|
||||
Operator: ts_std_dev(x, d)
|
||||
Description: Returns standard deviation of x for the past d days
|
||||
Operator: ts_step(1)
|
||||
Description: Returns days' counter
|
||||
Operator: ts_sum(x, d)
|
||||
Description: Sum values of x for the past d days.
|
||||
Operator: ts_zscore(x, d)
|
||||
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.
|
||||
Operator: normalize(x, useStd = false, limit = 0.0)
|
||||
Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
|
||||
Operator: quantile(x, driver = gaussian, sigma = 1.0)
|
||||
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
|
||||
Operator: rank(x, rate=2)
|
||||
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
|
||||
Operator: scale(x, scale=1, longscale=1, shortscale=1)
|
||||
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
|
||||
Operator: winsorize(x, std=4)
|
||||
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.
|
||||
Operator: zscore(x)
|
||||
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
|
||||
Operator: vec_avg(x)
|
||||
Description: Taking mean of the vector field x
|
||||
Operator: vec_sum(x)
|
||||
Description: Sum of vector field x
|
||||
Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
|
||||
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
|
||||
Operator: trade_when(x, y, z)
|
||||
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
|
||||
Operator: group_backfill(x, group, d, std = 4.0)
|
||||
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
|
||||
Operator: group_mean(x, weight, group)
|
||||
Description: All elements in group equals to the mean
|
||||
Operator: group_neutralize(x, group)
|
||||
Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
|
||||
Operator: group_rank(x, group)
|
||||
Description: Each elements in a group is assigned the corresponding rank in this group
|
||||
Operator: group_scale(x, group)
|
||||
Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
|
||||
Operator: group_zscore(x, group)
|
||||
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.
|
||||
========================= 操作符结束 =======================================
|
||||
|
||||
========================= 数据字段开始 =======================================
|
||||
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)
|
||||
|
||||
{'data_set_name': '可以使用:multi_factor_acceleration_score_derivative', 'description': '不可使用,仅供参考:Change in the acceleration of multi-factor score compared to previous period.'}
|
||||
{'data_set_name': '可以使用:pv13_h2_min2_1k_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'}
|
||||
{'data_set_name': '可以使用:pv13_h_min22_1000_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'}
|
||||
{'data_set_name': '可以使用:pv13_h_min24_500_sector', 'description': '不可使用,仅供参考:Grouping fields for top 500'}
|
||||
{'data_set_name': '可以使用:pv13_h_min2_focused_sector', 'description': '不可使用,仅供参考:Grouping fields for top 200'}
|
||||
{'data_set_name': '可以使用:pv13_h_min52_1k_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'}
|
||||
{'data_set_name': '可以使用:fn_comp_not_rec_stock_options_a', 'description': '不可使用,仅供参考:Unrecognized cost of unvested stock option awards.'}
|
||||
{'data_set_name': '可以使用:fn_comp_not_rec_stock_options_q', 'description': '不可使用,仅供参考:Unrecognized cost of unvested stock option awards.'}
|
||||
{'data_set_name': '可以使用:fnd2_a_ltrmdmrepopliny5', 'description': '不可使用,仅供参考:Amount of long-term debt payable, sinking fund requirements, and other securities issued that are redeemable by holder at fixed or determinable prices and dates maturing in the 5th fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.'}
|
||||
========================= 数据字段结束 =======================================
|
||||
|
||||
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
|
||||
@@ -0,0 +1,224 @@
|
||||
任务指令: 你需要根据以下这个金融逻辑, 组合创新 alpha
|
||||
Name
|
||||
Digital Asset Liquidity Premium Factor
|
||||
Hypothesis
|
||||
In the digital economy era, a non-linear relationship exists between the liquidity of digital assets held by companies (such as cryptocurrencies, digital copyrights, user data assets, blockchain-verified assets, etc.) and their market valuation. The market typically assigns a "digital liquidity premium" to companies with portfolios of highly liquid digital assets, as these assets are easily monetizable, can serve as high-quality collateral, and enable quick capture of emerging market opportunities. Conversely, if a company's digital assets suffer from poor liquidity (e.g., subject to lock-up periods, traded in private markets, lacking trading platforms), their potential value is difficult to discount immediately and may even become a liability during crises, leading to a valuation discount.
|
||||
Implementation Plan
|
||||
Construct a "Digital Asset Liquidity Score." First, identify and aggregate the categories and valuations of digital assets disclosed in company financial statement notes, special announcements, and industry databases. Second, assign a liquidity weight parameter to each asset category (based on dimensions such as breadth of trading markets, average daily trading volume, regulatory clarity, and acceptability for collateralized financing). Finally, calculate the weighted total score and divide it by the company's total market capitalization to control for size effects. Use a time-series comparison operator (e.g., ts_max) to capture the historical percentile rank of this score over the past year. Establish long positions in companies whose scores consistently rank in their own historical high percentiles and are top-ranked in peer cross-sectional comparisons.
|
||||
Alpha Factor Optimization Suggestion
|
||||
The valuation volatility of digital assets is extreme, and using the raw score alone may introduce noise. It is recommended to overlay a volatility adjustment mechanism: use ts_stddev to calculate the rolling volatility of the score's rate of change, and assign higher alpha weights to the combination of "high score and low volatility" to filter for companies whose digital asset value is being released stably. Additionally, a "Digital Asset Correlation" operator can be introduced to assess the correlation between a company's digital asset portfolio and mainstream digital market indices (e.g., a cryptocurrency index). Companies with low correlation should receive a positive adjustment to capture unique, non-systematic digital value.
|
||||
*=========================================================================================*
|
||||
输出格式：
|
||||
输出必须是且仅是纯文本。
|
||||
每一行是一个完整、独立、语法正确的WebSim表达式。
|
||||
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。
|
||||
===================== !!! 重点(输出方式) !!! =====================
|
||||
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。
|
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
|
||||
**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)：
|
||||
表达式
|
||||
表达式
|
||||
表达式
|
||||
...
|
||||
表达式
|
||||
=================================================================
|
||||
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。
|
||||
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：
|
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
|
||||
|
||||
=================================================================
|
||||
注意!!以下操作符不能使用事件类的数据集, 请勿类似的操作：
|
||||
Operator ts_product does not support event inputs
|
||||
Operator ts_zscore does not support event inputs
|
||||
Operator ts_mean does not support event inputs
|
||||
Operator ts_scale does not support event inputs
|
||||
Operator add does not support event inputs
|
||||
Operator sign does not support event inputs
|
||||
Operator subtract does not support event inputs
|
||||
Operator ts_delta does not support event inputs
|
||||
Operator ts_rank does not support event inputs
|
||||
Operator greater does not support event inputs
|
||||
Operator ts_av_diff does not support event inputs
|
||||
Operator ts_quantile does not support event inputs
|
||||
Operator ts_count_nans does not support event inputs
|
||||
Operator ts_covariance does not support event inputs
|
||||
Operator ts_arg_min does not support event inputs
|
||||
Operator divide does not support event inputs
|
||||
Operator ts_corr does not support event inputs
|
||||
Operator multiply does not support event inputs
|
||||
Operator if_else does not support event inputs
|
||||
Operator ts_sum does not support event inputs
|
||||
Operator ts_delay does not support event inputs
|
||||
Operator group_zscore does not support event inputs
|
||||
Operator ts_arg_max does not support event inputs
|
||||
Operator ts_std_dev does not support event inputs
|
||||
Operator ts_backfill does not support event inputs
|
||||
|
||||
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
|
||||
|
||||
========================= 操作符开始 =======================================
|
||||
注意: Operator: 后面的是操作符(是可以使用的),
|
||||
Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符
|
||||
特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
|
||||
Description: Absolute value of x
|
||||
Operator: add(x, y, filter = false)
|
||||
Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
|
||||
Operator: densify(x)
|
||||
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
|
||||
Operator: divide(x, y)
|
||||
Description: x / y
|
||||
Operator: inverse(x)
|
||||
Description: 1 / x
|
||||
Operator: log(x)
|
||||
Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
|
||||
Operator: max(x, y, ..)
|
||||
Description: Maximum value of all inputs. At least 2 inputs are required
|
||||
Operator: min(x, y ..)
|
||||
Description: Minimum value of all inputs. At least 2 inputs are required
|
||||
Operator: multiply(x ,y, ... , filter=false)
|
||||
Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
|
||||
Operator: power(x, y)
|
||||
Description: x ^ y
|
||||
Operator: reverse(x)
|
||||
Description: - x
|
||||
Operator: sign(x)
|
||||
Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
|
||||
Operator: signed_power(x, y)
|
||||
Description: x raised to the power of y such that final result preserves sign of x
|
||||
Operator: sqrt(x)
|
||||
Description: Square root of x
|
||||
Operator: subtract(x, y, filter=false)
|
||||
Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
|
||||
Operator: and(input1, input2)
|
||||
Description: Logical AND operator, returns true if both operands are true and returns false otherwise
|
||||
Operator: if_else(input1, input2, input 3)
|
||||
Description: If input1 is true then return input2 else return input3.
|
||||
Operator: input1 < input2
|
||||
Description: If input1 < input2 return true, else return false
|
||||
Operator: input1 <= input2
|
||||
Description: Returns true if input1 <= input2, return false otherwise
|
||||
Operator: input1 == input2
|
||||
Description: Returns true if both inputs are same and returns false otherwise
|
||||
Operator: input1 > input2
|
||||
Description: Logic comparison operators to compares two inputs
|
||||
Operator: input1 >= input2
|
||||
Description: Returns true if input1 >= input2, return false otherwise
|
||||
Operator: input1!= input2
|
||||
Description: Returns true if both inputs are NOT the same and returns false otherwise
|
||||
Operator: is_nan(input)
|
||||
Description: If (input == NaN) return 1 else return 0
|
||||
Operator: not(x)
|
||||
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).
|
||||
Operator: or(input1, input2)
|
||||
Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
|
||||
Operator: days_from_last_change(x)
|
||||
Description: Amount of days since last change of x
|
||||
Operator: hump(x, hump = 0.01)
|
||||
Description: Limits amount and magnitude of changes in input (thus reducing turnover)
|
||||
Operator: kth_element(x, d, k)
|
||||
Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
|
||||
Operator: last_diff_value(x, d)
|
||||
Description: Returns last x value not equal to current x value from last d days
|
||||
Operator: ts_arg_max(x, d)
|
||||
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
|
||||
Operator: ts_arg_min(x, d)
|
||||
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.
|
||||
Operator: ts_av_diff(x, d)
|
||||
Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
|
||||
Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
|
||||
Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
|
||||
Operator: ts_corr(x, y, d)
|
||||
Description: Returns correlation of x and y for the past d days
|
||||
Operator: ts_count_nans(x ,d)
|
||||
Description: Returns the number of NaN values in x for the past d days
|
||||
Operator: ts_covariance(y, x, d)
|
||||
Description: Returns covariance of y and x for the past d days
|
||||
Operator: ts_decay_linear(x, d, dense = false)
|
||||
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.
|
||||
Operator: ts_delay(x, d)
|
||||
Description: Returns x value d days ago
|
||||
Operator: ts_delta(x, d)
|
||||
Description: Returns x - ts_delay(x, d)
|
||||
Operator: ts_mean(x, d)
|
||||
Description: Returns average value of x for the past d days.
|
||||
Operator: ts_product(x, d)
|
||||
Description: Returns product of x for the past d days
|
||||
Operator: ts_quantile(x,d, driver="gaussian" )
|
||||
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.
|
||||
Operator: ts_rank(x, d, constant = 0)
|
||||
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.
|
||||
Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
|
||||
Description: Returns various parameters related to regression function
|
||||
Operator: ts_scale(x, d, constant = 0)
|
||||
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
|
||||
Operator: ts_std_dev(x, d)
|
||||
Description: Returns standard deviation of x for the past d days
|
||||
Operator: ts_step(1)
|
||||
Description: Returns days' counter
|
||||
Operator: ts_sum(x, d)
|
||||
Description: Sum values of x for the past d days.
|
||||
Operator: ts_zscore(x, d)
|
||||
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.
|
||||
Operator: normalize(x, useStd = false, limit = 0.0)
|
||||
Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
|
||||
Operator: quantile(x, driver = gaussian, sigma = 1.0)
|
||||
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
|
||||
Operator: rank(x, rate=2)
|
||||
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
|
||||
Operator: scale(x, scale=1, longscale=1, shortscale=1)
|
||||
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
|
||||
Operator: winsorize(x, std=4)
|
||||
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.
|
||||
Operator: zscore(x)
|
||||
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
|
||||
Operator: vec_avg(x)
|
||||
Description: Taking mean of the vector field x
|
||||
Operator: vec_sum(x)
|
||||
Description: Sum of vector field x
|
||||
Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
|
||||
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
|
||||
Operator: trade_when(x, y, z)
|
||||
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
|
||||
Operator: group_backfill(x, group, d, std = 4.0)
|
||||
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
|
||||
Operator: group_mean(x, weight, group)
|
||||
Description: All elements in group equals to the mean
|
||||
Operator: group_neutralize(x, group)
|
||||
Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
|
||||
Operator: group_rank(x, group)
|
||||
Description: Each elements in a group is assigned the corresponding rank in this group
|
||||
Operator: group_scale(x, group)
|
||||
Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
|
||||
Operator: group_zscore(x, group)
|
||||
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.
|
||||
========================= 操作符结束 =======================================
|
||||
|
||||
========================= 数据字段开始 =======================================
|
||||
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)
|
||||
|
||||
{'data_set_name': '可以使用:forward_price_120', 'description': '不可使用,仅供参考: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.'}
|
||||
{'data_set_name': '可以使用:fnd6_capxv', 'description': '不可使用,仅供参考:Capital Expend Property, Plant and Equipment Schd V'}
|
||||
{'data_set_name': '可以使用:fnd6_ciother', 'description': '不可使用,仅供参考:Comp. Inc. - Other Adj.'}
|
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_spcedq', 'description': '不可使用,仅供参考:S&P Core Earnings EPS Diluted'}
|
||||
{'data_set_name': '可以使用:fnd6_newqv1300_ciotherq', 'description': '不可使用,仅供参考:Comp Inc - Other Adj'}
|
||||
{'data_set_name': '可以使用:sales_ps', 'description': '不可使用,仅供参考:Sales per Share (Quarterly)'}
|
||||
{'data_set_name': '可以使用:fscore_bfl_total', 'description': '不可使用,仅供参考:The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.'}
|
||||
{'data_set_name': '可以使用:multi_factor_acceleration_score_derivative', 'description': '不可使用,仅供参考:Change in the acceleration of multi-factor score compared to previous period.'}
|
||||
{'data_set_name': '可以使用:anl4_netdebt_flag', 'description': '不可使用,仅供参考:Net debt - forecast type (revision/new/...)'}
|
||||
{'data_set_name': '可以使用:min_reported_eps_guidance', 'description': '不可使用,仅供参考:Reported Earnings Per Share - Minimum guidance value for the annual period'}
|
||||
{'data_set_name': '可以使用:sg_and_admin_min_guidance_value', 'description': '不可使用,仅供参考:Minimum guidance value for Selling, General & Administrative Expense on an annual basis.'}
|
||||
{'data_set_name': '可以使用:pv13_h2_min2_1k_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'}
|
||||
{'data_set_name': '可以使用:pv13_h_min22_1000_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'}
|
||||
{'data_set_name': '可以使用:pv13_h_min24_500_sector', 'description': '不可使用,仅供参考:Grouping fields for top 500'}
|
||||
{'data_set_name': '可以使用:pv13_h_min2_focused_sector', 'description': '不可使用,仅供参考:Grouping fields for top 200'}
|
||||
{'data_set_name': '可以使用:pv13_h_min52_1k_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'}
|
||||
{'data_set_name': '可以使用:news_max_up_amt', 'description': '不可使用,仅供参考:The after the news high minus the price at the time of the news'}
|
||||
{'data_set_name': '可以使用:nws18_sse', 'description': '不可使用,仅供参考:Sentiment of phrases impacting the company'}
|
||||
{'data_set_name': '可以使用:fn_def_tax_assets_liab_net_q', 'description': '不可使用,仅供参考:Amount, after allocation of valuation allowances and deferred tax liability, of deferred tax asset attributable to deductible differences and carryforwards, without jurisdictional netting.'}
|
||||
{'data_set_name': '可以使用:fn_def_tax_liab_a', 'description': '不可使用,仅供参考:Amount, after deferred tax asset, of deferred tax liability attributable to taxable differences without jurisdictional netting.'}
|
||||
{'data_set_name': '可以使用:fn_liab_fair_val_a', 'description': '不可使用,仅供参考:Liabilities Fair Value, Recurring, Total'}
|
||||
{'data_set_name': '可以使用:fn_op_lease_min_pay_due_in_2y_a', 'description': '不可使用,仅供参考:Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of 1 year due in the 2nd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.'}
|
||||
{'data_set_name': '可以使用:fn_treasury_stock_shares_a', 'description': '不可使用,仅供参考: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.'}
|
||||
========================= 数据字段结束 =======================================
|
||||
|
||||
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
|
||||
@@ -0,0 +1,235 @@
|
||||
任务指令: 你需要根据以下这个金融逻辑, 组合创新 alpha
|
||||
**Name**
|
||||
Smart Capital Allocation Efficiency Factor
|
||||
**Assumption**
|
||||
Corporate capital allocation efficiency — i.e., the free cash flow or economic value added generated per unit of capital invested — is a core indicator of management’s strategic execution. Companies that persistently allocate capital to low-return projects (e.g., ROIC < WACC), even with strong revenue growth, are likely to face downward valuation re-rating over time. Conversely, firms that precisely deploy capital into high-return areas (e.g., ROIC > WACC and expanding) may earn valuation premiums even with modest short-term growth. The market often prices capital allocation efficiency with a lag, especially after earnings seasons or strategic announcements, offering opportunities to capture alpha from expectation gaps.
|
||||
**Implementation Plan**
|
||||
Construct a “Capital Allocation Efficiency Score” = (ROIC - WACC) × Capital Expenditure Growth Rate × Free Cash Flow / Capital Expenditure. Use time-series operators to compute rolling 3-year metrics, identifying firms with persistently deteriorating efficiency (bottom 20% percentile) for short positions, and those with improving efficiency (top 20% percentile) for long positions. To mitigate industry bias, apply industry-neutralization by ranking only within peer groups.
|
||||
**Alpha Factor Optimization Suggestions**
|
||||
Introduce a “Capital Allocation Intent” textual factor: extract keywords such as “investment focus,” “return targets,” and “capital discipline” from earnings call transcripts or management discussion sections in annual reports to build a semantic matching score. Fuse this with the quantitative efficiency score via weighted combination. When textual and quantitative signals align, amplify the factor weight; when they diverge (e.g., management claims “focusing on high-return areas” while ROIC continues to decline), trigger signal decay or suppression to reduce false positives.
|
||||
|
||||
*=========================================================================================*
|
||||
输出格式：
|
||||
输出必须是且仅是纯文本。
|
||||
每一行是一个完整、独立、语法正确的WebSim表达式。
|
||||
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。
|
||||
===================== !!! 重点(输出方式) !!! =====================
|
||||
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。
|
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
|
||||
**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)：
|
||||
表达式
|
||||
表达式
|
||||
表达式
|
||||
...
|
||||
表达式
|
||||
=================================================================
|
||||
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。
|
||||
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：
|
||||
不要自行假设, 你需要用到的操作符 和 数据集, 必须从我提供给你的里面查找, 并严格按照里面的使用方法进行组合
|
||||
|
||||
=================================================================
|
||||
注意!!以下操作符不能使用事件类的数据集, 请勿类似的操作：
|
||||
Operator ts_product does not support event inputs
|
||||
Operator ts_zscore does not support event inputs
|
||||
Operator ts_mean does not support event inputs
|
||||
Operator ts_scale does not support event inputs
|
||||
Operator add does not support event inputs
|
||||
Operator sign does not support event inputs
|
||||
Operator subtract does not support event inputs
|
||||
Operator ts_delta does not support event inputs
|
||||
Operator ts_rank does not support event inputs
|
||||
Operator greater does not support event inputs
|
||||
Operator ts_av_diff does not support event inputs
|
||||
Operator ts_quantile does not support event inputs
|
||||
Operator ts_count_nans does not support event inputs
|
||||
Operator ts_covariance does not support event inputs
|
||||
Operator ts_arg_min does not support event inputs
|
||||
Operator divide does not support event inputs
|
||||
Operator ts_corr does not support event inputs
|
||||
Operator multiply does not support event inputs
|
||||
Operator if_else does not support event inputs
|
||||
Operator ts_sum does not support event inputs
|
||||
Operator ts_delay does not support event inputs
|
||||
Operator group_zscore does not support event inputs
|
||||
Operator ts_arg_max does not support event inputs
|
||||
Operator ts_std_dev does not support event inputs
|
||||
Operator ts_backfill does not support event inputs
|
||||
|
||||
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
|
||||
|
||||
========================= 操作符开始 =======================================
|
||||
注意: Operator: 后面的是操作符(是可以使用的),
|
||||
Description: 此字段后面的是操作符对应的描述或使用说明(禁止使用, 仅供参考), Description字段后面的内容是使用说明, 不是操作符
|
||||
特别注意!!!! 必须按照操作符字段Operator的使用说明生成 alphaOperator: abs(x)
|
||||
Description: Absolute value of x
|
||||
Operator: add(x, y, filter = false)
|
||||
Description: Add all inputs (at least 2 inputs required). If filter = true, filter all input NaN to 0 before adding
|
||||
Operator: densify(x)
|
||||
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
|
||||
Operator: divide(x, y)
|
||||
Description: x / y
|
||||
Operator: inverse(x)
|
||||
Description: 1 / x
|
||||
Operator: log(x)
|
||||
Description: Natural logarithm. For example: Log(high/low) uses natural logarithm of high/low ratio as stock weights.
|
||||
Operator: max(x, y, ..)
|
||||
Description: Maximum value of all inputs. At least 2 inputs are required
|
||||
Operator: min(x, y ..)
|
||||
Description: Minimum value of all inputs. At least 2 inputs are required
|
||||
Operator: multiply(x ,y, ... , filter=false)
|
||||
Description: Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1
|
||||
Operator: power(x, y)
|
||||
Description: x ^ y
|
||||
Operator: reverse(x)
|
||||
Description: - x
|
||||
Operator: sign(x)
|
||||
Description: if input > 0, return 1; if input < 0, return -1; if input = 0, return 0; if input = NaN, return NaN;
|
||||
Operator: signed_power(x, y)
|
||||
Description: x raised to the power of y such that final result preserves sign of x
|
||||
Operator: sqrt(x)
|
||||
Description: Square root of x
|
||||
Operator: subtract(x, y, filter=false)
|
||||
Description: x-y. If filter = true, filter all input NaN to 0 before subtracting
|
||||
Operator: and(input1, input2)
|
||||
Description: Logical AND operator, returns true if both operands are true and returns false otherwise
|
||||
Operator: if_else(input1, input2, input 3)
|
||||
Description: If input1 is true then return input2 else return input3.
|
||||
Operator: input1 < input2
|
||||
Description: If input1 < input2 return true, else return false
|
||||
Operator: input1 <= input2
|
||||
Description: Returns true if input1 <= input2, return false otherwise
|
||||
Operator: input1 == input2
|
||||
Description: Returns true if both inputs are same and returns false otherwise
|
||||
Operator: input1 > input2
|
||||
Description: Logic comparison operators to compares two inputs
|
||||
Operator: input1 >= input2
|
||||
Description: Returns true if input1 >= input2, return false otherwise
|
||||
Operator: input1!= input2
|
||||
Description: Returns true if both inputs are NOT the same and returns false otherwise
|
||||
Operator: is_nan(input)
|
||||
Description: If (input == NaN) return 1 else return 0
|
||||
Operator: not(x)
|
||||
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).
|
||||
Operator: or(input1, input2)
|
||||
Description: Logical OR operator returns true if either or both inputs are true and returns false otherwise
|
||||
Operator: days_from_last_change(x)
|
||||
Description: Amount of days since last change of x
|
||||
Operator: hump(x, hump = 0.01)
|
||||
Description: Limits amount and magnitude of changes in input (thus reducing turnover)
|
||||
Operator: kth_element(x, d, k)
|
||||
Description: Returns K-th value of input by looking through lookback days. This operator can be used to backfill missing data if k=1
|
||||
Operator: last_diff_value(x, d)
|
||||
Description: Returns last x value not equal to current x value from last d days
|
||||
Operator: ts_arg_max(x, d)
|
||||
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
|
||||
Operator: ts_arg_min(x, d)
|
||||
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.
|
||||
Operator: ts_av_diff(x, d)
|
||||
Description: Returns x - tsmean(x, d), but deals with NaNs carefully. That is NaNs are ignored during mean computation
|
||||
Operator: ts_backfill(x,lookback = d, k=1, ignore="NAN")
|
||||
Description: Backfill is the process of replacing the NAN or 0 values by a meaningful value (i.e., a first non-NaN value)
|
||||
Operator: ts_corr(x, y, d)
|
||||
Description: Returns correlation of x and y for the past d days
|
||||
Operator: ts_count_nans(x ,d)
|
||||
Description: Returns the number of NaN values in x for the past d days
|
||||
Operator: ts_covariance(y, x, d)
|
||||
Description: Returns covariance of y and x for the past d days
|
||||
Operator: ts_decay_linear(x, d, dense = false)
|
||||
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.
|
||||
Operator: ts_delay(x, d)
|
||||
Description: Returns x value d days ago
|
||||
Operator: ts_delta(x, d)
|
||||
Description: Returns x - ts_delay(x, d)
|
||||
Operator: ts_mean(x, d)
|
||||
Description: Returns average value of x for the past d days.
|
||||
Operator: ts_product(x, d)
|
||||
Description: Returns product of x for the past d days
|
||||
Operator: ts_quantile(x,d, driver="gaussian" )
|
||||
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.
|
||||
Operator: ts_rank(x, d, constant = 0)
|
||||
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.
|
||||
Operator: ts_regression(y, x, d, lag = 0, rettype = 0)
|
||||
Description: Returns various parameters related to regression function
|
||||
Operator: ts_scale(x, d, constant = 0)
|
||||
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
|
||||
Operator: ts_std_dev(x, d)
|
||||
Description: Returns standard deviation of x for the past d days
|
||||
Operator: ts_step(1)
|
||||
Description: Returns days' counter
|
||||
Operator: ts_sum(x, d)
|
||||
Description: Sum values of x for the past d days.
|
||||
Operator: ts_zscore(x, d)
|
||||
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.
|
||||
Operator: normalize(x, useStd = false, limit = 0.0)
|
||||
Description: Calculates the mean value of all valid alpha values for a certain date, then subtracts that mean from each element
|
||||
Operator: quantile(x, driver = gaussian, sigma = 1.0)
|
||||
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
|
||||
Operator: rank(x, rate=2)
|
||||
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
|
||||
Operator: scale(x, scale=1, longscale=1, shortscale=1)
|
||||
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
|
||||
Operator: winsorize(x, std=4)
|
||||
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.
|
||||
Operator: zscore(x)
|
||||
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
|
||||
Operator: vec_avg(x)
|
||||
Description: Taking mean of the vector field x
|
||||
Operator: vec_sum(x)
|
||||
Description: Sum of vector field x
|
||||
Operator: bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10")
|
||||
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
|
||||
Operator: trade_when(x, y, z)
|
||||
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
|
||||
Operator: group_backfill(x, group, d, std = 4.0)
|
||||
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
|
||||
Operator: group_mean(x, weight, group)
|
||||
Description: All elements in group equals to the mean
|
||||
Operator: group_neutralize(x, group)
|
||||
Description: Neutralizes Alpha against groups. These groups can be subindustry, industry, sector, country or a constant
|
||||
Operator: group_rank(x, group)
|
||||
Description: Each elements in a group is assigned the corresponding rank in this group
|
||||
Operator: group_scale(x, group)
|
||||
Description: Normalizes the values in a group to be between 0 and 1. (x - groupmin) / (groupmax - groupmin)
|
||||
Operator: group_zscore(x, group)
|
||||
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.
|
||||
========================= 操作符结束 =======================================
|
||||
|
||||
========================= 数据字段开始 =======================================
|
||||
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)
|
||||
|
||||
{'data_set_name': '可以使用:forward_price_120', 'description': '不可使用,仅供参考: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.'}
|
||||
{'data_set_name': '可以使用:fnd6_capxv', 'description': '不可使用,仅供参考:Capital Expend Property, Plant and Equipment Schd V'}
|
||||
{'data_set_name': '可以使用:fnd6_ciother', 'description': '不可使用,仅供参考:Comp. Inc. - Other Adj.'}
|
||||
{'data_set_name': '可以使用:fnd6_newa2v1300_oiadp', 'description': '不可使用,仅供参考:Operating Income After Depreciation'}
|
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_gdwliaq', 'description': '不可使用,仅供参考:Impairment of Goodwill After-tax'}
|
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_glceaq', 'description': '不可使用,仅供参考:Gain/Loss on Sale (Core Earnings Adjusted) After-tax'}
|
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_spcedq', 'description': '不可使用,仅供参考:S&P Core Earnings EPS Diluted'}
|
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_spceeps12', 'description': '不可使用,仅供参考:S&P Core Earnings EPS Basic 12MM'}
|
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_spceepsq', 'description': '不可使用,仅供参考:S&P Core Earnings EPS Basic'}
|
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_spcep12', 'description': '不可使用,仅供参考:S&P Core Earnings 12MM - Preliminary'}
|
||||
{'data_set_name': '可以使用:fnd6_newqeventv110_spcepd12', 'description': '不可使用,仅供参考:S&P Core Earnings 12MM EPS Diluted - Preliminary'}
|
||||
{'data_set_name': '可以使用:fnd6_newqv1300_ciotherq', 'description': '不可使用,仅供参考:Comp Inc - Other Adj'}
|
||||
{'data_set_name': '可以使用:fnd6_newqv1300_spcedq', 'description': '不可使用,仅供参考:S&P Core Earnings EPS Diluted'}
|
||||
{'data_set_name': '可以使用:fnd6_newqv1300_spceepsp12', 'description': '不可使用,仅供参考:S&P Core 12MM EPS - Basic - Preliminary'}
|
||||
{'data_set_name': '可以使用:fnd6_newqv1300_spceepsq', 'description': '不可使用,仅供参考:S&P Core Earnings EPS Basic'}
|
||||
{'data_set_name': '可以使用:fnd6_oiadps', 'description': '不可使用,仅供参考:Operating Income after Depreciation'}
|
||||
{'data_set_name': '可以使用:fnd6_spce', 'description': '不可使用,仅供参考:S&P Core Earnings'}
|
||||
{'data_set_name': '可以使用:sales_ps', 'description': '不可使用,仅供参考:Sales per Share (Quarterly)'}
|
||||
{'data_set_name': '可以使用:fscore_bfl_total', 'description': '不可使用,仅供参考:The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.'}
|
||||
{'data_set_name': '可以使用:fscore_total', 'description': '不可使用,仅供参考:The final score M-Score is a weighted average of both the Pentagon surface score and the Pentagon acceleration score.'}
|
||||
{'data_set_name': '可以使用:multi_factor_acceleration_score_derivative', 'description': '不可使用,仅供参考:Change in the acceleration of multi-factor score compared to previous period.'}
|
||||
{'data_set_name': '可以使用:anl4_netdebt_flag', 'description': '不可使用,仅供参考:Net debt - forecast type (revision/new/...)'}
|
||||
{'data_set_name': '可以使用:min_reported_eps_guidance', 'description': '不可使用,仅供参考:Reported Earnings Per Share - Minimum guidance value for the annual period'}
|
||||
{'data_set_name': '可以使用:pv13_h2_min2_1k_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'}
|
||||
{'data_set_name': '可以使用:pv13_h_min22_1000_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'}
|
||||
{'data_set_name': '可以使用:pv13_h_min24_500_sector', 'description': '不可使用,仅供参考:Grouping fields for top 500'}
|
||||
{'data_set_name': '可以使用:pv13_h_min2_focused_sector', 'description': '不可使用,仅供参考:Grouping fields for top 200'}
|
||||
{'data_set_name': '可以使用:pv13_h_min52_1k_sector', 'description': '不可使用,仅供参考:Grouping fields for top 1000'}
|
||||
{'data_set_name': '可以使用:news_max_up_amt', 'description': '不可使用,仅供参考:The after the news high minus the price at the time of the news'}
|
||||
{'data_set_name': '可以使用:nws18_sse', 'description': '不可使用,仅供参考:Sentiment of phrases impacting the company'}
|
||||
{'data_set_name': '可以使用:fn_def_tax_liab_a', 'description': '不可使用,仅供参考:Amount, after deferred tax asset, of deferred tax liability attributable to taxable differences without jurisdictional netting.'}
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||||
{'data_set_name': '可以使用:fn_op_lease_min_pay_due_in_2y_a', 'description': '不可使用,仅供参考:Amount of required minimum rental payments for operating leases having an initial or remaining non-cancelable lease term in excess of 1 year due in the 2nd fiscal year following the latest fiscal year. Excludes interim and annual periods when interim periods are reported on a rolling approach, from latest balance sheet date.'}
|
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{'data_set_name': '可以使用:fn_treasury_stock_shares_a', 'description': '不可使用,仅供参考: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.'}
|
||||
========================= 数据字段结束 =======================================
|
||||
|
||||
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
|
||||
Reference in New Issue
Block a user