词汇：turnover

n. 翻覆；营业额；流通量；半圆卷饼；[篮球]失误；adj. 翻过来的；可翻转的

相关场景

ts_target_tvr_hump(x, lambda_min=0, lambda_max=1, target_tvr=0.1):
Tune "hump" to have a turnover equal to a certain target with optimization weight range between lambda_min, lambda_max.
>> Operators
ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1):
Tune "ts_decay" to have a turnover equal to a certain target, with optimization weight range between lambda_min, lambda_max
>> Operators
调整“ts_decay”以使营业额等于某个目标，优化权重范围在lambda_min、lambda_max之间
ts_target_tvr_hump(x, lambda_min=0, lambda_max=1, target_tvr=0.1):
Tune "hump" to have a turnover equal to a certain target with optimization weight range between lambda_min, lambda_max.
>> Operators
ts_target_tvr_decay(x, lambda_min=0, lambda_max=1, target_tvr=0.1):
Tune "ts_decay" to have a turnover equal to a certain target, with optimization weight range between lambda_min, lambda_max
>> Operators
sales:
inventory_turnover:
库存周转率|矩阵。
fnd6_newa2v1300_sale:
fnd6_cptnewqv1300_saleq:
fnd6_cptnewqeventv110_saleq:
fnd6_cptmfmq_saleq:
trade_when(x, y, z):
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; This operator may help reduce correlation and reduce turnover.
>> Operators
用于仅在指定条件下更改Alpha值，并在其他情况下保持Alpha值。它还允许在指定条件下关闭Alpha位置（分配NaN值）；此运算符可能有助于减少相关性并降低营业额。
hump(x, hump = 0.01):
Limits amount and magnitude of changes in input (thus reducing turnover)； hump(-ts_delta(close, 5), hump = 0.00001)
>> Operators
限制投入变化的数量和幅度（从而降低换手率）
How to get a higher Sharpe? How to potentially increase returns of an alpha? How to reduce correlation of an alpha? How to reduce turnover? How to potentially decrease PnL fluctuations? How to gain intuition for Neutralization? How to avoid overfitting?
>> worldquantbrain_5_vector
如何获得更高的夏普？如何潜在地增加阿尔法的回报？如何降低阿尔法的相关性？如何减少营业额？如何潜在地降低PnL波动？如何获得中和的直觉？如何避免过度拟合？
Due to D0 Alphas normally having higher turnover than D1 Alphas, to compensate for the increasing transaction costs, higher Sharpe and higher returns are required. But there are also other tests that you have to consider, such as the SubUniverse test and the RobustUniverse test for the CHN region. Good performance in the liquid universe means that the Alpha should have higher capacity.
>> worldquantbrain_5_vector
由于D0 Alphas的营业额通常高于D1 Alphas，为了弥补不断增加的交易成本，需要更高的夏普和更高的回报。但你也必须考虑其他测试，例如针对CHN地区的亚宇宙测试和RobustUniverse测试。在液体宇宙中的良好性能意味着阿尔法应该具有更高的容量。
Below are again the average value and turnover plots for the vec_avg field. Average value hovers densely around 15,000 and turnover around 130%. Here as well, you need to reduce turnover by using ts_rank or ts_decay in your Alpha expression.
>> worldquantbrain_5_vector
下面是vec_avg字段的平均值和周转图。平均价值在15000左右，成交率在130%左右。在这里，您也需要在Alpha表达式中使用ts_rank或ts_decay来减少周转率。
The recent news event only be relevant for a limited time, can using an operator which changes Alpha weights quickly for values closer to zero rather than distant values help improve Alpha performance? Also can using trade_when operator help reduce the turnover?
>> worldquantbrain_4_program
最近的新闻事件只在有限的时间内具有相关性，使用一个运算符来快速更改接近零的值而不是距离较远的值的Alpha权重，可以帮助提高Alpha性能吗？使用trade_when操作符也能帮助减少营业额吗？
Average measure of daily trading activity: turnover signifies how often an Alpha simulates trades. It can be defined as the ratio of value traded to book size. Daily Turnover = Dollar trading volume/Booksize. Good Alphas tend to have lower turnover, since low turnover means lower transaction costs. See Simulation results page for details on Alpha simulation results details.
>> worldquantbrain_4_program
每日交易活动的平均衡量标准：成交量表示Alpha模拟交易的频率。它可以定义为交易价值与账面规模的比率。日交易量=美元交易量/账面规模。好的阿尔法往往具有较低的营业额，因为低营业额意味着较低的交易成本。有关Alpha模拟结果详细信息，请参阅模拟结果页面。
The percentage of your portfolio traded in a day (by dollar value) is called ‘turnover’. The turnover reported in simulation results is the average daily turnover over the simulation.
>> worldquantbrain_4_program
你的投资组合在一天内交易的百分比（按美元价值计算）称为“营业额”。模拟结果中报告的营业额是模拟过程中的平均日营业额。
Decay can be used to reduce turnover
>> worldquantbrain_4_program
增加收益return:
Increase the turnover of your alphas — higher turnover means more trading and potentially higher returns. Use lower decay values in the alpha settings. Work on more liquid (smaller) universes in the alpha settings. While keeping returns and drawdown at the same level, you may get higher returns if you increase the volatility of your alphas. Try using news and analyst datasets. They may have the potential to generate alphas with good returns.
>> worldquantbrain_4_program
增加阿尔法的交易额——更高的交易额意味着更多的交易和潜在的更高回报。在alpha设置中使用较低的衰减值。在阿尔法设置中研究更多液体（更小）的宇宙。在保持回报和提款水平不变的同时，如果你增加阿尔法的波动性，你可能会获得更高的回报。尝试使用新闻和分析师数据集。他们可能有潜力产生具有良好回报的阿尔法。
First, one might peruse blogs, journals and research papers on the internet to come up with an idea. The Alpha expression is entered in BRAIN and operations (like truncation,neutralization, decay) are performed on the raw Alpha. BRAIN makes investments (goes long or short) for all the instrumentsof the universe chosen in the Settings panel and the PnL is simulated. Then the performance is calculated (Sharpe, Turnover, Returns) as seen in the Simulation Results page. And if the Alpha is not deemed worthy, the Alpha idea is revised. Else, it enters production.
>> worldquantbrain_4_program
首先，人们可能会仔细阅读互联网上的博客、期刊和研究论文，想出一个想法。将Alpha表达式输入BRAIN，并对原始Alpha执行操作（如截断、中和、衰减）。BRAIN对“设置”面板中选择的所有工具进行投资（做多或做空），并模拟PnL。然后计算性能（Sharpe、营业额、回报），如“模拟结果”页面所示。如果阿尔法被认为不值得，阿尔法的想法就会被修改。否则，它将投入生产。
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
turnover = volume/(sharesout*1000000); -ts_mean(turnover,22)
>> worldquantbrain_2
Data Update Cycle:
Fundamental data has the following characteristics: Features low turnover as it's updated quarterly, semi-annually, or annually； Disclosure cycles vary by listing exchange and company size； Unlike PV data, Fundamental data can update discontinuously；
>> worldquantbrain
基础数据具有以下特点：具有低周转率，因为它每季度、每半年或每年更新一次披露周期因上市交易所和公司规模而异与PV数据不同，基础数据可以不连续地更新