任务指令 【策略类型】优化因子 multiply(ts_rank(divide(ts_backfill(fnd6_drc, 252), assets), 126), subtract(group_rank(divide(ts_backfill(fnd6_drc, 252), assets), sector), 0.5)) 【当前情况】 { "id": "88qMa31V", "type": "REGULAR", "author": "YC93384", "settings": { "instrumentType": "EQUITY", "region": "USA", "universe": "TOP3000", "delay": 1, "decay": 0, "neutralization": "INDUSTRY", "truncation": 0.08, "pasteurization": "ON", "unitHandling": "VERIFY", "nanHandling": "OFF", "maxTrade": "OFF", "language": "FASTEXPR", "visualization": false, "startDate": "2018-01-20", "endDate": "2023-01-20" }, "regular": { "code": "multiply(ts_rank(divide(ts_backfill(fnd6_drc, 252), assets), 126), subtract(group_rank(divide(ts_backfill(fnd6_drc, 252), assets), sector), 0.5))", "description": null, "operatorCount": 8 }, "dateCreated": "2025-12-25T03:16:45-05:00", "dateSubmitted": null, "dateModified": "2025-12-25T03:16:45-05:00", "name": null, "favorite": false, "hidden": false, "color": null, "category": null, "tags": [], "classifications": [ { "id": "DATA_USAGE:SINGLE_DATA_SET", "name": "Single Data Set Alpha" } ], "grade": "INFERIOR", "stage": "IS", "status": "UNSUBMITTED", "is": { "pnl": 2429129, "bookSize": 20000000, "longCount": 966, "shortCount": 1435, "turnover": 0.028, "returns": 0.0491, "drawdown": 0.0485, "margin": 0.003504, "sharpe": 1.36, "fitness": 0.85, "startDate": "2018-01-20", "checks": [ { "name": "LOW_SHARPE", "result": "PASS", "limit": 1.25, "value": 1.36 }, { "name": "LOW_FITNESS", "result": "FAIL", "limit": 1.0, "value": 0.85 }, { "name": "LOW_TURNOVER", "result": "PASS", "limit": 0.01, "value": 0.028 }, { "name": "HIGH_TURNOVER", "result": "PASS", "limit": 0.7, "value": 0.028 }, { "name": "CONCENTRATED_WEIGHT", "result": "PASS" }, { "name": "LOW_SUB_UNIVERSE_SHARPE", "result": "PASS", "limit": 0.59, "value": 0.68 }, { "name": "SELF_CORRELATION", "result": "PENDING" }, { "name": "MATCHES_COMPETITION", "result": "PASS", "competitions": [ { "id": "challenge", "name": "Challenge" } ] } ] }, "os": null, "train": null, "test": null, "prod": null, "competitions": null, "themes": null, "pyramids": null, "pyramidThemes": null, "team": null } *=========================================================================================* 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。 **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 10 个alpha： 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, Description: 此字段后面的是操作符对应的描述或使用说明, Description字段后面的内容是使用说明, 不是操作符 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) {'id': '98', 'data_set_name': 'fnd6_acdo', 'description': 'Current Assets of Discontinued Operations', 'description_cn': '已终止经营current资产'} {'id': '473', 'data_set_name': 'fnd6_newqeventv110_cibegniq', 'description': 'Comp Inc - Beginning Net Income', 'description_cn': 'comp_inc_beginning_net_income'} {'id': '517', 'data_set_name': 'fnd6_newqeventv110_glceaq', 'description': 'Gain/Loss on Sale (Core Earnings Adjusted) After-tax', 'description_cn': '税后核心 earnings 润亏'} {'id': '624', 'data_set_name': 'fnd6_newqeventv110_spcedq', 'description': 'S&P Core Earnings EPS Diluted', 'description_cn': 'SPCE earnings per share diluted'} {'id': '625', 'data_set_name': 'fnd6_newqeventv110_spceeps12', 'description': 'S&P Core Earnings EPS Basic 12MM', 'description_cn': '标普核心 earnings 每股基本值_12M'} {'id': '628', 'data_set_name': 'fnd6_newqeventv110_spceepsq', 'description': 'S&P Core Earnings EPS Basic', 'description_cn': '标普核心 earnings 每股基本值'} {'id': '629', 'data_set_name': 'fnd6_newqeventv110_spcep12', 'description': 'S&P Core Earnings 12MM - Preliminary', 'description_cn': '标普核心 earnings 12个月 - 预liminary'} {'id': '630', 'data_set_name': 'fnd6_newqeventv110_spcepd12', 'description': 'S&P Core Earnings 12MM EPS Diluted - Preliminary', 'description_cn': 'S&P核心 earnings_12个月稀释后每股盈亏平衡点_初步'} {'id': '774', 'data_set_name': 'fnd6_newqv1300_spcedq', 'description': 'S&P Core Earnings EPS Diluted', 'description_cn': '标准普尔核心 earnings 每股稀释后利润'} {'id': '777', 'data_set_name': 'fnd6_newqv1300_spceepsq', 'description': 'S&P Core Earnings EPS Basic', 'description_cn': '标普核心 earnings EPS 基本'} {'id': '872', 'data_set_name': 'fnd6_spce', 'description': 'S&P Core Earnings', 'description_cn': '标准普尔核心 earnings'} {'id': '1000', 'data_set_name': 'multi_factor_acceleration_score_derivative', 'description': 'Change in the acceleration of multi-factor score compared to previous period.', 'description_cn': '多因子评分加速度变化 Compared_to_Previous_Period_Multi_Factor_Score_Acceleration_Change'} {'id': '2393', 'data_set_name': 'fn_def_tax_assets_liab_net_q', 'description': 'Amount, after allocation of valuation allowances and deferred tax liability, of deferred tax asset attributable to deductible differences and carryforwards, without jurisdictional netting.', 'description_cn': '扣除减值准备和递延税负债后，attributable_to_deferred_tax_asset_amount'} {'id': '2436', 'data_set_name': 'fn_liab_fair_val_a', 'description': 'Liabilities Fair Value, Recurring, Total', 'description_cn': '看涨期权负债公允价值_ recurring_total'} ========================= 数据字段结束 =======================================