The那个 methodolgy方法论 or the或 sql for obtaining the data数据库获取数据 these curves from the private data export have already been explained in detail, but briefly…这些曲线的私人数据的出口已经详细解释，但简单...
- The Y axis is the percentage of all loans orginated of a given age that are currently 1 month late or worse…在Y轴的比例是所有贷款orginated某一年龄，目前1月下旬或更糟...
- The X axis is the days loan origination.在X轴是天的贷款来源。
- The curves stop when there is less than 250 loans in the “bucket.”停止曲线时，小于250贷款的“水桶” 。
- These curves are for all loans of a given Credit Grade. One would get different curves (flatter slopes=better); for exmaple, if you were to choose loans with 2 or less inquires and 5 years of credit history you would see flatter slopes.这些曲线是所有贷款某一信用等级。之一将得到不同的曲线（平坦的斜坡=更好） ;例如，如果您有选择贷款2或更少询问和5岁的信用记录您将会看到平坦的山坡上。
The range is 7-8% for AA all the way to ~55% for HR.范围是为7-8 ％机管局所有道路〜 55 ％用于人力资源。
(Click Graph for Larger Version) （点击图放大版）
Prosper Vintage Curves普罗斯珀经典曲线
Brief Explanation: These curves show the entire set of prosper loan broken down by credit grade and lined up along the x axis on their origination date… As a loan goes late (1 month or worse) it is counted (either by amount or by count) as late against the population… The curves stop when the loan population falls below 250 (ie there are 249 or less loans that age or older)…简短的解释：这些曲线显示一整套繁荣细分贷款信用等级和排队沿x轴的起始日期...作为贷款不用后期（ 1个月或更糟糕）这是个（无论数量或计数）晚对人口...曲线停止贷款时，人口低于250个（即有249个或更少的贷款岁或以上） ...
Recently a study最近的一项研究 from the University of Maryland claimed a peak default date around month 10 of a Prosper loan. This would translate into the largest delta in this graph over a month period. Does this graph confirm or deny that statement? Is it conclusive? Please leave a comment.来自马里兰大学的要求高峰预设的日期10个月左右的普罗斯珀贷款。这将转化为最大的三角洲在这图表超过三个月。这个图肯定或否定这种说法？这是决定性的？请发表评论。
Here is the vintage curves by count (click graph for larger version)…这里是老式曲线计数（点击大图版） ...
Here is the vintage curves by amount (larger loan go late at a higher rate and therefore on a percentage basis you would expect an increase), (click graph for larger version)…这里是老式曲线的数额（较大的贷款去年底在一个较高的利率，因此，对一定比例的基础您希望增加） ， （点击大图版） ...
Here is the SQL that I used to pull the underlying data out of the以下是SQL ，我来拉基本数据出 public and private data downloads公共和私人数据下载 … ...
DECLARE @DTD int宣告@ DTD的整数
SET @DTD=30集@ DTD的= 30
SELECT选择
cast(aday-originationdate as int) as 'PIT',演员（ aday - originationdate为整数）作为'坑' ，
l.creditgrade, l.creditgrade ，
sum(PrincipalBalance+NetDefaults) as 'Amount',总结（ PrincipalBalance + NetDefaults ）作为'金额' ，
count(l.[key]) as 'Count',伯爵（湖[关键] ）作为'计数' ，
sum(case WHEN (mld.DPD!=0 and总和（时（ mld.DPD ！ = 0和
(mld.DPD+(aday-observationdate))>@DTD) THEN （ mld.DPD + （ aday - observationdate ） ） “ @ DTD的） ，然后
PrincipalBalance+NetDefaults ELSE 0 END) as 'AmountLate', PrincipalBalance + NetDefaults语句0完）作为' AmountLate ' ，
sum(case WHEN (mld.DPD!=0 and总和（时（ mld.DPD ！ = 0和
(mld.DPD+(aday-observationdate))>@DTD) THEN （ mld.DPD + （ aday - observationdate ） ） “ @ DTD的） ，然后
PrincipalBalance+NetDefaults ELSE 0 END)/ PrincipalBalance + NetDefaults语句0完） /
sum(PrincipalBalance+NetDefaults) as AmountLatePercentage,总结（ PrincipalBalance + NetDefaults ）作为AmountLatePercentage ，
sum(case WHEN (mld.DPD!=0 and总和（时（ mld.DPD ！ = 0和
(mld.DPD+(aday-observationdate))>@DTD) THEN （ mld.DPD + （ aday - observationdate ） ） “ @ DTD的） ，然后
1 ELSE 0 END) as 'CountLate',一日语句0完）作为' CountLate ' ，
sum(case WHEN (mld.DPD!=0 and总和（时（ mld.DPD ！ = 0和
(mld.DPD+(aday-observationdate))>@DTD) THEN （ mld.DPD + （ aday - observationdate ） ） “ @ DTD的） ，然后
1.0 ELSE 0.0 END)/count(l.[key]) as 'CountLatePercentage' 1.0语句0.0完） /伯爵（湖[关键] ）作为' CountLatePercentage ，
FROM从
loan l贷款升
inner join creditprofile cp on cp.listingkey=l.listingkey内加入creditprofile蛋白的cp.listingkey = l.listingkey
inner join LoanPerformance mld on l.[key]=mld.loankey cross join alldays内加入LoanPerformance mld的湖[关键] = mld.loankey两岸加入alldays
where哪里
mld.observationdate = ( select top 1 observationdate mld.observationdate = （选取前1 observationdate
from LoanPerformance sub从LoanPerformance分
where sub.observationdate < aday在sub.observationdate “ aday
and sub.loankey=mld.loankey order by sub.observationdate DESC )和sub.loankey = mld.loankey秩序sub.observationdate降序）
and aday < getDate()和aday “ getDate （ ）
and aday >= '02/01/2006'和aday “ = '02 / 2006分之01 '
and l.creditgrade!='NC'和l.creditgrade ！ = '数控'
group by组
cast(aday-originationdate as int),演员（ aday - originationdate为整数） ，
l.creditgrade
having有
count(l.[key])>250 and计数（湖[关键] ） “ 250
sum(PrincipalBalance+NetDefaults)>0总之（ PrincipalBalance + NetDefaults ） “ 0
order by命令
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