rank 高频英语单词排名2355 淘英语

词汇：rank

n. 等级；队列；排；军衔

相关场景

fnd6_rank:

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

5650:

排名 Competition Status Start Date End Date | Challenge Participating Rank: 5650 Ongoing
>> Quant Brain新的阶段

group_rank(x, group):

Group operators are a type of cross-sectional operator that compares stocks at a finer level, where the cross-sectional operation is applied within each group, rather than across the entire market. The group_rank operator allocates the stocks to their specified group, then within each group, it ranks the stocks based on their input value for data field x and returns an equally distributed number between 0.0 and 1.0. This operator may help reduce both outliers and drawdown while reducing correlation. Example: group_rank(x, subindustry)； The stocks are first grouped into their respective subindustry. Within each subindustry, the stocks within that subindustry are ranked based on their input value for data field x and assigned an equally distributed number between 0.0 and 1.0.
>> Operators

集团运营商是一种横截面运营商，它在更精细的层面上比较股票，其中横截面操作适用于每个集团，而不是整个市场。group_rank运算符将股票分配到指定的组，然后在每个组内，它根据数据字段x的输入值对股票进行排名，并返回一个在0.0和1.0之间均匀分布的数字。此运算符可能有助于减少异常值和下跌，同时减少相关性。示例：group_rank（x，子行业）这些股票首先被归入各自的子行业。在每个子行业内，该子行业内的股票根据其数据字段x的输入值进行排名，并分配一个在0.0和1.0之间均匀分布的数字。

bucket(rank(x), range="0, 1, 0.1" or buckets = "2,5,6,7,10"):

Convert float values into indexes for user-specified buckets. Bucket is useful for creating group values, which can be passed to GROUP as input; Examples: my_group = bucket(rank(volume), range="0.1,1,0.1"); group_neutralize(sales/assets, my_group) my_group = bucket(rank(volume), buckets ="0.2,0.5,0.7", skipBoth=True, NANGroup=True); group_neutralize(sales/assets, my_group)
>> Operators

将浮点值转换为用户指定bucket的索引。Bucket可用于创建组值，这些值可以作为输入传递给group；示例：my_group=bucket（排名（体积），范围=“0.1,1,0.1”）；group_neutralize（销售/资产，my_group）my_group=bucket（排名（数量），bucket=“0.2,0.5,0.7”，skipBoth=True，NANGroup=True）；group_中性化（销售/资产，my_group）

rank(x, rate=2):

Ranks the input among all the instruments and returns an equally distributed number between 0.0 and 1.0. For precise sort, use the rate as 0; Rank(close); Rank (close, rate=0) # Sorts precisely; X = (4,3,6,10,2) => Rank(x) = (0.5, 0.25, 0.75, 1, 0)
>> Operators

quantile(x, driver = gaussian, sigma = 1.0):

Rank the raw vector, shift the ranked Alpha vector, apply distribution (gaussian, cauchy, uniform). If driver is uniform, it simply subtract each Alpha value with the mean of all Alpha values in the Alpha vector;quantile(close, driver = gaussian, sigma = 0.5 ); This operator may help reduce outliers.
>> Operators

对原始向量进行排序，移动排序后的阿尔法向量，应用分布（高斯分布、柯西分布、均匀分布）。如果驱动器是均匀的，它只需用阿尔法向量中所有阿尔法值的平均值减去每个阿尔法值；分位数（闭合，驱动器=高斯，西格玛=0.5）；此运算符可能有助于减少异常值。

ts_rank(x, d, constant = 0):

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.
>> Operators

multiply(x ,y, ... , filter=false), x * y:

Multiply all inputs. At least 2 inputs are required. Filter sets the NaN values to 1;multiply(rank(-returns), rank(volume/adv20), filter=true)
>> Operators

Can using custom neutralization on the Alpha based on self-created groups (like historical volatility) help improve sub-universe performance? Use floor or bucket operator combined with rank operator to implement custom neutralization
>> worldquantbrain_4_program

基于自我创建的组（如历史波动率）在Alpha上使用自定义中和可以帮助提高子宇宙的性能吗？使用地板或铲斗操作员结合等级操作员来实现自定义中和

quantile(x, driver = gaussian, sigma = 1.0):

对原始向量进行排序，移动排序后的阿尔法向量，应用分布（高斯分布、柯西分布、均匀分布）。如果驱动程序是统一的，它只需用Alpha向量中所有Alpha值的平均值减去每个Alpha值

Based on those daily positions, PnL is calculated and displayed. By default, the BRAIN platform will normalize your weights (according to the operations you enter) and create a portfolio of $20 million (total booksize) worth of equity. (Note that a portfolio is just a collection of securities.) This can be better understood with the help of the PnL chart of the Alpha in our example rank(-returns)
>> worldquantbrain_4_program

基于这些每日位置，计算并显示PnL。默认情况下，BRAIN平台将使您的权重标准化（根据您输入的操作），并创建价值2000万美元（总账面规模）的股权投资组合。（请注意，投资组合只是证券的集合。）在我们的示例排名（-回报）中，借助阿尔法的PnL图可以更好地理解这一点

Note: On each iteration/day, the expression rank(-returns) will have access to all the data for returns up to that day, and the matrix will grow by one line every day until it reaches the most recent date. The role of the expression is to transform the input matrix to an output vector of weights as we see in this hypothetical example.
>> worldquantbrain_4_program

注意：在每一次迭代/天中，表达式rank（-returns）将访问到当天为止的所有返回数据，矩阵将每天增长一行，直到达到最近的日期。该表达式的作用是将输入矩阵转换为权重的输出向量，正如我们在这个假设的例子中看到的那样。

The resulting vector depends on the operators used in the Alpha expression. In our case, since we’ve used the rank operator, we see equally distributed values between 0 and 1 in Column D. Note that the stock with the lowest return has the highest value, and vice versa, in line with our hypothesis.
>> worldquantbrain_4_program

生成的向量取决于Alpha表达式中使用的运算符。在我们的例子中，由于我们使用了秩运算符，我们在D列中看到了0到1之间的等分布值。请注意，根据我们的假设，回报率最低的股票具有最高的价值，反之亦然。

Step1: Evaluate the expression for each stock to generate the Alpha vector for the given date. In our case, this date would be February 2nd, because we’ve assumed Delay 1 settings. The Delay 1 setting uses data as of T-1 date to create the Alpha vector as of T date. To produce the Alpha vector, the simulator performs the rank operation on negative returns and produces a vector of values corresponding to each stock.
>> worldquantbrain_4_program

步骤1：计算每只股票的表达式，以生成给定日期的Alpha向量。在我们的例子中，这个日期将是2月2日，因为我们假设了延迟1的设置。延迟1设置使用截至T-1日期的数据来创建截至T日期的Alpha向量。为了生成Alpha向量，模拟器对负回报执行排名操作，并生成与每只股票对应的值向量。

ranks the values of x for each stock over the past n days, then return the rank of the current value.
>> worldquantbrain_4_program

对过去n天内每只股票的x值进行排名，然后返回当前值的排名。

ranks the input among all the stocks and returns an equally distributed number between 0.0 and 1.0
>> worldquantbrain_4_program

在所有股票中对输入进行排名，并返回一个在0.0和1.0之间均匀分布的数字

ts_rank(x,d,constant=0):

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.
>> worldquantbrain_2

对过去d天内每个仪器的x值进行排名，然后返回当前值+常数的排名。如果未指定，默认情况下，常量=0。

Alpha signals can vary depending on position allocation. Operators like rank, signed_power, and log change how Alpha distributes positions. Generally, distributions with more extreme values tend to have higher volatility and returns. Be careful of overfitting risk when positions are too concentrated in few instruments.
>> worldquantbrain_2

阿尔法信号可能因位置分配而异。rank、signed_power和log等运算符会改变Alpha分配位置的方式。一般来说，具有更极值的分布往往具有更高的波动性和回报率。当头寸过于集中在少数仪器中时，要小心过度拟合的风险。

ts_rank(bookvalue_ps /close, 252); ts-rank(earnings /enterprise_value, 252); ts-rank(ts_delta(revenue, 252), 126); ts_rank(free_cash_flow / enterprise_value, 252); ts_rank(gross_margin, 252); ts-rank(return_12m, 126); ts_rank(return_equity, 252); ts_rank(return_assets, 252); group_neutralize(ts_rank(earnings_yield, 252) * ts_rank(return_equity, 126), industry); group_neutralize(ts_rank(ebitda / enterprise_value, 252) * ts_rank(operating_income/ assets, 126), industry);
>> worldquantbrain_2

Available Alpha Ideas:

Calculate average sentiment; Apply time series operators ; Set rank-based conditions; Implement using conditional logic.
>> worldquantbrain_2

rank(ts_sum(vec_avg(nws12_afterhsz_sl),60)) > 0.5 ? 1: rank(-ts_delta(close, 2))
>> worldquantbrain_2

Apply time series operators with a suitable period to reduce noise in this daily data. ts_sum or ts_mean work well for this purpose. Rank the data and set a threshold. For example, to classify half the stocks as True and the rest as False, use a condition like rank(X) > 0.5.
>> worldquantbrain_2

使用具有适当周期的时间序列运算符来减少日常数据中的噪声。ts_sum或ts_mean很好地用于此目的。对数据进行排序并设置阈值。例如，要将一半的股票分类为True，其余的分类为False，请使用rank（X）>0.5这样的条件。

Task: Use vec_avg(nws12_afterhsz_sl) to calculate the average sentiment from news data for each company. Use ts_sum or ts_mean (ts operator) and rank to set proper condition. Use if_else or form of ?: to implement the Alpha idea.
>> worldquantbrain_2

任务：使用vec_avg（nws12_afterhsz_sl）从每家公司的新闻数据中计算平均情绪。使用ts_sum或ts_mean（ts运算符）和rank设置适当的条件。使用if_else还是形式？：实施阿尔法理念。

turnover = volume/(sharesout*1000000); singnal = (ts_std_dev(turnover, 5)/ts_std_dev(turnover, 500)) - 1; group_neutralize(-singnal, bucket(rank(cap), range="0.1,1,0.1"));
>> worldquantbrain_2

turnover = volume/(sharesout*1000000); group_neutralize(-ts_mean(turnover,22), bucket(rank(cap), range="0.1,1,0.1"));
>> worldquantbrain_2