Lend Academy Network Forum
Lending Club Discussion => Investors  LC => Topic started by: larrydag on February 10, 2019, 08:57:10 AM

I've built a loan default prediction model with Lending Club about 2 years ago and I've been investing modestly with it since then. I'm getting about 5.5 to 6% adj. return on my loans. So I think its working fairly well. I'm trying to improve the model hopefully one day achieve 10% returns. I'm wondering if anyone else has built similar models and have come up with creative variable transformations on the historical loan data? Here are some that I've come up with
loan_to_income = loan amount / income
payment_to_income = installment / income
time_since_earliest_credit_line = earliest credit line date  issue date
open_acc_ratio = open_acc / total_acc
curr_bal_ratio = tot_cur_bal / total_bal_ex_mort
some of these are more or less predictive. Anyone have any other interesting transforms?
My inspiration for developing a Lending Club model came from LendingRobot http://blog.lendingrobot.com/research/predictingthenumberofpaymentsinpeerlending/

I recommend the book "Credit Scoring, Response Modeling and Insurance Rating" by Steven Finlay.
Also recommend the statistical package R as it's free, open source and very powerful.
Finally, I recommend the following LA thread and particularly the post by brycemason 12/23/2015.
In particularly note the referral to "the four horsemen of the consumer credit scoring apocalypse".
https://forum.lendacademy.com/index.php/topic,3570.msg31594.html#msg31593 (https://forum.lendacademy.com/index.php/topic,3570.msg31594.html#msg31593)
Anyway, test transformations for covariance with your other model factors to see if they statistically add value;
loan_to_income (one of your transformations) is (was) one of the four biggies.
Good luck with that 10%!

Three important transformations are:
 Installment to Income
 Loan Amount to Revolving Balance
 Credit Age (Earliest Credit Date  Loan Issue Date)

Three important transformations are:
 Installment to Income
 Loan Amount to Revolving Balance
 Credit Age (Earliest Credit Date  Loan Issue Date)
This is a bit of a technicality but it's important. Clearly if you want to be able to take your model result and use it to determine whether or not the LC model has "mispriced" a loan (ie find the best loans) then your model may not incorporate any output of the LC model (Grade, Interest Rate, etc). If your model is independent of the LC model and LC changes its model (underwriting standards) then comparisons with your model results (that have not changed) should be immediately evident. Installment is based on LC's assigned interest rate which is the key product of it's model so you can't use Installment in your model. The best you can do is use loan to income. (Credit to Bryce for this insight, a very long time ago).

Installment to income represents capability to pay, what does loan amount to income represent?
Three important transformations are:
 Installment to Income
 Loan Amount to Revolving Balance
 Credit Age (Earliest Credit Date  Loan Issue Date)
This is a bit of a technicality but it's important. Clearly if you want to be able to take your model result and use it to determine whether or not the LC model has "mispriced" a loan (ie find the best loans) then your model may not incorporate any output of the LC model (Grade, Interest Rate, etc). If your model is independent of the LC model and LC changes its model (underwriting standards) then comparisons with your model results (that have not changed) should be immediately evident. Installment is based on LC's assigned interest rate which is the key product of it's model so you can't use Installment in your model. The best you can do is use loan to income. (Credit to Bryce for this insight, a very long time ago).

Installment to income represents capability to pay, what does loan amount to income represent?
Three important transformations are:
 Installment to Income
 Loan Amount to Revolving Balance
 Credit Age (Earliest Credit Date  Loan Issue Date)
This is a bit of a technicality but it's important. Clearly if you want to be able to take your model result and use it to determine whether or not the LC model has "mispriced" a loan (ie find the best loans) then your model may not incorporate any output of the LC model (Grade, Interest Rate, etc). If your model is independent of the LC model and LC changes its model (underwriting standards) then comparisons with your model results (that have not changed) should be immediately evident. Installment is based on LC's assigned interest rate which is the key product of it's model so you can't use Installment in your model. The best you can do is use loan to income. (Credit to Bryce for this insight, a very long time ago).
My answer would simply be "something very important".
In a multivariate logistic regression its statistical significance is quite large; only surpassed by FICO. Using words of the English language to describe a relationship seems sensible enough but our own personal biases attach a significance or lack thereof that may not be accurate. I'll go where the numbers take me and prefer to exclude all LC model results from being inputs my own model, period. That permits my model to be a completely unbiased observer so to speak when evaluating the outputs of the LC model to determine which loans meet my own investment criteria and which do not.
For those that may not already know I no longer invest in LC loans and its been a long time since I participated in any of this.
Judging from the Cumulative ROI's I posted recently I don't plan to resume. But then again I never planned to resume anyway.
https://forum.lendacademy.com/index.php/topic,5076.0.html (https://forum.lendacademy.com/index.php/topic,5076.0.html)
EDIT: Changed "linear regression" to "logistic regression" which was used. Like I said, it's been quite a while.

Our Data Scientist, Guangming Lang, used machine learning to mine the LC historical data. He used a combination of R and XGBoost to train our Liquid P2P loan selection models. I believe these are one click installs on AWS if you're inclined to tackle such a project.
https://liquidp2p.com/ (https://liquidp2p.com/)
https://www.linkedin.com/in/gmlang/ (https://www.linkedin.com/in/gmlang/)
https://www.rproject.org/about.html (https://www.rproject.org/about.html)
https://xgboost.readthedocs.io/en/latest/ (https://xgboost.readthedocs.io/en/latest/)

Thanks for all of the replies. I should have shared a little about myself and my methods. I have experience building predictive credit models in financial institutions. My primary tool of choice to build predictive models is R. I'm very fond of the GLMNET package and my methods resemble Frank Harrells "Regression Modeling Strategies".

Maybe you and Guangming should have a chat... lol. I’m a serial entrepreneur, not a data scientist. I knew what I wanted to build and assembled a team. He obviously was a critical team member. Guangming also authored a book on scoring consumer credit. I would be happy to show and discuss some of his work in detail if you want to pm me.
Sent from my iPhone using Tapatalk

Thanks for all of the replies. I should have shared a little about myself and my methods. I have experience building predictive credit models in financial institutions. My primary tool of choice to build predictive models is R. I'm very fond of the GLMNET package and my methods resemble Frank Harrells "Regression Modeling Strategies".
Very good. Thanks for the tip on the book.
Please share a bit more of your experience if you will. It would be so interesting so see how things are now.
Claim "secret sauce" where appropriate.
1) Is LC offering enough loans that meet your criteria for you to be able to stay fully invested? Would it be too much to ask that $ amount?
2) Presumably you are using the API to access new loans at the four "feeding times". Is there still a race? Do you consider speed important?
3) What's the Term and Grade allocation of your portfolio 36(%A, %B, ...) and 60(%A, %B, ...) where %x is a percent of the total $ principal invested?
TIA

Thanks for all of the replies. I should have shared a little about myself and my methods. I have experience building predictive credit models in financial institutions. My primary tool of choice to build predictive models is R. I'm very fond of the GLMNET package and my methods resemble Frank Harrells "Regression Modeling Strategies".
Very good. Thanks for the tip on the book.
Please share a bit more of your experience if you will. It would be so interesting so see how things are now.
Claim "secret sauce" where appropriate.
1) Is LC offering enough loans that meet your criteria for you to be able to stay fully invested? Would it be too much to ask that $ amount?
2) Presumably you are using the API to access new loans at the four "feeding times". Is there still a race? Do you consider speed important?
3) What's the Term and Grade allocation of your portfolio 36(%A, %B, ...) and 60(%A, %B, ...) where %x is a percent of the total $ principal invested?
TIA
there's literally nothing to pick from at those 4 times a day in the primary market. i made a very picky algo and my algo never buys anything, because of the 1000 loans issue today, i think the majority are bought as whole loans, then the remainder is bought by retail investors with a simple rules mix of grade and duration, then a few loans dribble out to the api  like 2030 a day.

Did you use installment/income term in addition to loan amount/income and FICO in your multivariate logistic regression? Did you also use separate monthly income term in your regression? If not, then your statement is ingenuous as you didn't considered the relative importance of these terms in respect to each other. If you had considered relative merits of these terms together in your regression, you would know that monthly income is a very important "borrower characteristics" datapoint and any transformation containing monthly income will be weighted heavily in a regression. The first step of any regression analysis is to identify important and influential attributes to include in the regression.
The English language explanation for loan amount/income transformation is simple. This transformation represents whether a borrower given certain income can pay back the loan amount or not irrespective of duration. The installment/income transformation represents whether a borrower given certain income can make regular payment of installment amount over certain duration to payback loan amount or not. It is a "borrower indebtedness" datapoint and goes along with DTI.
When you are lending on LC primary market, you are deciding whether to lend on the LC given terms of lending (interest rate, duration installment). If you were deciding the terms of lending yourself (for ex: Prosper 1.0), then your strategy of not considering platform recommended terms of lending in assessing the loan quality will be effective and you will come up with your own acceptable terms of lending at which you will lend.
Sorry to see you discontinue the lending but not surprised.
Installment to income represents capability to pay, what does loan amount to income represent?
My answer would simply be "something very important".
In a multivariate logistic regression its statistical significance is quite large; only surpassed by FICO. Using words of the English language to describe a relationship seems sensible enough but our own personal biases attach a significance or lack thereof that may not be accurate. I'll go where the numbers take me and prefer to exclude all LC model results from being inputs my own model, period. That permits my model to be a completely unbiased observer so to speak when evaluating the outputs of the LC model to determine which loans meet my own investment criteria and which do not.
For those that may not already know I no longer invest in LC loans and its been a long time since I participated in any of this.
Judging from the Cumulative ROI's I posted recently I don't plan to resume. But then again I never planned to resume anyway.
https://forum.lendacademy.com/index.php/topic,5076.0.html (https://forum.lendacademy.com/index.php/topic,5076.0.html)
EDIT: Changed "linear regression" to "logistic regression" which was used. Like I said, it's been quite a while.

Did you use installment/income term in addition to loan amount/income and FICO in your multivariate logistic regression? Did you also use separate monthly income term in your regression? If not, then your statement is ingenuous as you didn't considered the relative importance of these terms in respect to each other. If you had considered relative merits of these terms together in your regression, you would know that monthly income is a very important "borrower characteristics" datapoint and any transformation containing monthly income will be weighted heavily in a regression. The first step of any regression analysis is to identify important and influential attributes to include in the regression.
The English language explanation for loan amount/income transformation is simple. This transformation represents whether a borrower given certain income can pay back the loan amount or not irrespective of duration. The installment/income transformation represents whether a borrower given certain income can make regular payment of installment amount over certain duration to payback loan amount or not. It is a "borrower indebtedness" datapoint and goes along with DTI.
When you are lending on LC primary market, you are deciding whether to lend on the LC given terms of lending (interest rate, duration installment). If you were deciding the terms of lending yourself (for ex: Prosper 1.0), then your strategy of not considering platform recommended terms of lending in assessing the loan quality will be effective and you will come up with your own acceptable terms of lending at which you will lend.
Sorry to see you discontinue the lending but not surprised.
Installment to income represents capability to pay, what does loan amount to income represent?
My answer would simply be "something very important".
In a multivariate logistic regression its statistical significance is quite large; only surpassed by FICO. Using words of the English language to describe a relationship seems sensible enough but our own personal biases attach a significance or lack thereof that may not be accurate. I'll go where the numbers take me and prefer to exclude all LC model results from being inputs my own model, period. That permits my model to be a completely unbiased observer so to speak when evaluating the outputs of the LC model to determine which loans meet my own investment criteria and which do not.
For those that may not already know I no longer invest in LC loans and its been a long time since I participated in any of this.
Judging from the Cumulative ROI's I posted recently I don't plan to resume. But then again I never planned to resume anyway.
https://forum.lendacademy.com/index.php/topic,5076.0.html (https://forum.lendacademy.com/index.php/topic,5076.0.html)
EDIT: Changed "linear regression" to "logistic regression" which was used. Like I said, it's been quite a while.
Installment / Income wasn't used for the reason I mentioned before, loan amount / income was. (Actually Installment / Income was used in early models but somewhere along the way Bryce noted the problem regarding the use of Installment and replaced it with loan amount / income.) IIRC the change didn't have a major effect on the model results.
Yes, income and loan amount were also included separately.
The objective of the model was to produce results very much Prosper 1.0, yielding an independent probability of default (i.e "risk"). In addition to the risk model a measure of "reward" was also computed (using the LC assigned interest rate, etc.). Prosper 1.0 offered no comparative "reward" basis which IMO is why it failed. Using risk and reward it was simple enough to rank a set of loans LC offered from best to worst and purchase only the ones ranked best. Of course this determination is all relative. If all the loans are lousy then selecting the best ones will still be lousy; and vice versa. Back in 2013 and 2014 there were lots of very good loans (as we now know from hindsight). Picking the relatively best ones was a winning bet. As time moved forward risk / reward increased and I had to chose whether to accept less reward for risk or buy fewer loans. Unfortunately I lowered my lending standards and accepted less reward for the risk. I had no idea it would get as bad as it did. Had I not lowered my lending standards my guess is that I would have completely stopped purchasing LC D&E notes in 2015. My bad. Actually the 16Q2 fiasco saved me from myself as it caused me to stop purchasing loans, sell half of my loan portfolio and reassess. I did buy a few more higher risk (D&E) loans in the fall, switched to all B and stopped all purchases in Feb 17. I was ready to leave LC for good. All the credit for the model is Bryce Mason's, not mine, but I have a pretty good handle how it worked and my comments are based on that understanding. We collaborated on quite a number of things back then.
I'm sorry to be leaving LC as it's been both an interesting and profitable hobby.
Guess when it became less profitable it also became less interesting.

Thanks for all of the replies. I should have shared a little about myself and my methods. I have experience building predictive credit models in financial institutions. My primary tool of choice to build predictive models is R. I'm very fond of the GLMNET package and my methods resemble Frank Harrells "Regression Modeling Strategies".
Very good. Thanks for the tip on the book.
Please share a bit more of your experience if you will. It would be so interesting so see how things are now.
Claim "secret sauce" where appropriate.
1) Is LC offering enough loans that meet your criteria for you to be able to stay fully invested? Would it be too much to ask that $ amount?
2) Presumably you are using the API to access new loans at the four "feeding times". Is there still a race? Do you consider speed important?
3) What's the Term and Grade allocation of your portfolio 36(%A, %B, ...) and 60(%A, %B, ...) where %x is a percent of the total $ principal invested?
TIA
My modeling method is using a Cox Prop Hazard multivariate survival model tuned with GLMNET. Nothing really special. I've never put a survival model in production and wanted to give it a go. I've worked in auto finance for the last 7 years and have done applied math and data analysis for most of my career. I've built credit scoring models for large lenders. It is actually quite fun in my opinion.
1) to be honest I do it as a hobby. I've only invested a few thousand in the last couple of years. I'm doing it to keep my chops up and it interests me.
2) yes I'm using the API. I don't invest enough frequency to see if speed is important
3) 36: B 10%, C 33%, D 17%, E 14% 60: B 2%, C 11%, D 4%, E 5%, F/G 2%

So, you had no theoretical basis/reason for excluding "installment/income" in favor of "loan amount/income" from your model. That's all I wanted to highlight as a forum participant reached out to me offline for more clarification on merit of using installment over loan amount. I typically don't get into back and forth on internet forums. Thanks for your time in explaining the reasoning.
Installment / Income wasn't used for the reason I mentioned before, loan amount / income was. (Actually Installment / Income was used in early models but somewhere along the way Bryce noted the problem regarding the use of Installment and replaced it with loan amount / income.) IIRC the change didn't have a major effect on the model results.
Yes, income and loan amount were also included separately.
The objective of the model was to produce results very much Prosper 1.0, yielding an independent probability of default (i.e "risk"). In addition to the risk model a measure of "reward" was also computed (using the LC assigned interest rate, etc.). Prosper 1.0 offered no comparative "reward" basis which IMO is why it failed. Using risk and reward it was simple enough to rank a set of loans LC offered from best to worst and purchase only the ones ranked best. Of course this determination is all relative. If all the loans are lousy then selecting the best ones will still be lousy; and vice versa. Back in 2013 and 2014 there were lots of very good loans (as we now know from hindsight). Picking the relatively best ones was a winning bet. As time moved forward risk / reward increased and I had to chose whether to accept less reward for risk or buy fewer loans. Unfortunately I lowered my lending standards and accepted less reward for the risk. I had no idea it would get as bad as it did. Had I not lowered my lending standards my guess is that I would have completely stopped purchasing LC D&E notes in 2015. My bad. Actually the 16Q2 fiasco saved me from myself as it caused me to stop purchasing loans, sell half of my loan portfolio and reassess. I did buy a few more higher risk (D&E) loans in the fall, switched to all B and stopped all purchases in Feb 17. I was ready to leave LC for good. All the credit for the model is Bryce Mason's, not mine, but I have a pretty good handle how it worked and my comments are based on that understanding. We collaborated on quite a number of things back then.
I'm sorry to be leaving LC as it's been both an interesting and profitable hobby.
Guess when it became less profitable it also became less interesting.

Thanks for all of the replies. I should have shared a little about myself and my methods. I have experience building predictive credit models in financial institutions. My primary tool of choice to build predictive models is R. I'm very fond of the GLMNET package and my methods resemble Frank Harrells "Regression Modeling Strategies".
Very good. Thanks for the tip on the book.
Please share a bit more of your experience if you will. It would be so interesting so see how things are now.
Claim "secret sauce" where appropriate.
1) Is LC offering enough loans that meet your criteria for you to be able to stay fully invested? Would it be too much to ask that $ amount?
2) Presumably you are using the API to access new loans at the four "feeding times". Is there still a race? Do you consider speed important?
3) What's the Term and Grade allocation of your portfolio 36(%A, %B, ...) and 60(%A, %B, ...) where %x is a percent of the total $ principal invested?
TIA
My modeling method is using a Cox Prop Hazard multivariate survival model tuned with GLMNET. Nothing really special. I've never put a survival model in production and wanted to give it a go. I've worked in auto finance for the last 7 years and have done applied math and data analysis for most of my career. I've built credit scoring models for large lenders. It is actually quite fun in my opinion.
1) to be honest I do it as a hobby. I've only invested a few thousand in the last couple of years. I'm doing it to keep my chops up and it interests me.
2) yes I'm using the API. I don't invest enough frequency to see if speed is important
3) 36: B 10%, C 33%, D 17%, E 14% 60: B 2%, C 11%, D 4%, E 5%, F/G 2%
Are there any resources you'd recommend for someone starting in auto finance and predictive modeling?

There are a lot of opportunities to get started in auto finance if you have the right skillsets. The typical skillsets that auto finance companies look for are STEM degrees and business degrees. You can easily look up on a job aggregator to see the job descriptions. Most auto finance companies are like every other company in they want to be able to make data driven decisions about loan applicants ability to repay on loans. If you don't have previous financial or lending experience I believe you can still get in the door at an analyst or IT developer level and build your experience. Even if you can't find an auto finance job you can find an analyst job at a bank and learn about credit and lending in that position. The important things to know in auto finance is credit bureau data and loan portfolio management.
Getting started in predictive modeling is more broad. There is a huge swath of companies and industries looking for that skill. In fact even if you current job doesn't require you could probably take it on as as side project and show how your model would help your current organization. Here is the secret untold story about predictive modeling that most academics do not tell you. Predictive modeling is 80% data acquisition and management and 20% modeling. So make sure you are a data skill hawk meaning that you can download, pull, connect, manipulate, slice, dice, warehouse, store, and distribute data. That means having skills in SQL, Python, R or other data programming tool. Trust me your bosses would like it even if you can just manage multiple data sources and provide meaningful data analysis. Chances are the business decision makers in an organization doesn't know how and doesn't know the data exists.
Getting your chops up in the statistical and applied math of predictive modeling can be done on your own via online learning or in a more structured classroom setting. Do it in baby steps if you have no applied math background. Start with basic statistics 101 and move on to more advanced.

Thanks for all of the replies. I should have shared a little about myself and my methods. I have experience building predictive credit models in financial institutions. My primary tool of choice to build predictive models is R. I'm very fond of the GLMNET package and my methods resemble Frank Harrells "Regression Modeling Strategies".
Very good. Thanks for the tip on the book.
Please share a bit more of your experience if you will. It would be so interesting so see how things are now.
Claim "secret sauce" where appropriate.
1) Is LC offering enough loans that meet your criteria for you to be able to stay fully invested? Would it be too much to ask that $ amount?
2) Presumably you are using the API to access new loans at the four "feeding times". Is there still a race? Do you consider speed important?
3) What's the Term and Grade allocation of your portfolio 36(%A, %B, ...) and 60(%A, %B, ...) where %x is a percent of the total $ principal invested?
TIA
My modeling method is using a Cox Prop Hazard multivariate survival model tuned with GLMNET. Nothing really special. I've never put a survival model in production and wanted to give it a go. I've worked in auto finance for the last 7 years and have done applied math and data analysis for most of my career. I've built credit scoring models for large lenders. It is actually quite fun in my opinion.
1) to be honest I do it as a hobby. I've only invested a few thousand in the last couple of years. I'm doing it to keep my chops up and it interests me.
2) yes I'm using the API. I don't invest enough frequency to see if speed is important
3) 36: B 10%, C 33%, D 17%, E 14% 60: B 2%, C 11%, D 4%, E 5%, F/G 2%
Thanks for the reply. As I mentioned earlier it was a very nice hobby for me as well. If you had been lucky enough to stumble across LC back in 2013 you could have made some serious money with your skills. Plenty of good loans from which to select. From the previous post in this thread by mikedev10 there's just not very many loans offered any more.

There are a lot of opportunities to get started in auto finance if you have the right skillsets. The typical skillsets that auto finance companies look for are STEM degrees and business degrees. You can easily look up on a job aggregator to see the job descriptions. Most auto finance companies are like every other company in they want to be able to make data driven decisions about loan applicants ability to repay on loans. If you don't have previous financial or lending experience I believe you can still get in the door at an analyst or IT developer level and build your experience. Even if you can't find an auto finance job you can find an analyst job at a bank and learn about credit and lending in that position. The important things to know in auto finance is credit bureau data and loan portfolio management.
Getting started in predictive modeling is more broad. There is a huge swath of companies and industries looking for that skill. In fact even if you current job doesn't require you could probably take it on as as side project and show how your model would help your current organization. Here is the secret untold story about predictive modeling that most academics do not tell you. Predictive modeling is 80% data acquisition and management and 20% modeling. So make sure you are a data skill hawk meaning that you can download, pull, connect, manipulate, slice, dice, warehouse, store, and distribute data. That means having skills in SQL, Python, R or other data programming tool. Trust me your bosses would like it even if you can just manage multiple data sources and provide meaningful data analysis. Chances are the business decision makers in an organization doesn't know how and doesn't know the data exists.
Getting your chops up in the statistical and applied math of predictive modeling can be done on your own via online learning or in a more structured classroom setting. Do it in baby steps if you have no applied math background. Start with basic statistics 101 and move on to more advanced.
Thanks. I have broad experience in the lending and data analysis. But I've started to learn python and may be moving into a role that is for an auto lender. Should be fun if it happens

There are a lot of opportunities to get started in auto finance if you have the right skillsets. The typical skillsets that auto finance companies look for are STEM degrees and business degrees. You can easily look up on a job aggregator to see the job descriptions. Most auto finance companies are like every other company in they want to be able to make data driven decisions about loan applicants ability to repay on loans. If you don't have previous financial or lending experience I believe you can still get in the door at an analyst or IT developer level and build your experience. Even if you can't find an auto finance job you can find an analyst job at a bank and learn about credit and lending in that position. The important things to know in auto finance is credit bureau data and loan portfolio management.
Getting started in predictive modeling is more broad. There is a huge swath of companies and industries looking for that skill. In fact even if you current job doesn't require you could probably take it on as as side project and show how your model would help your current organization. Here is the secret untold story about predictive modeling that most academics do not tell you. Predictive modeling is 80% data acquisition and management and 20% modeling. So make sure you are a data skill hawk meaning that you can download, pull, connect, manipulate, slice, dice, warehouse, store, and distribute data. That means having skills in SQL, Python, R or other data programming tool. Trust me your bosses would like it even if you can just manage multiple data sources and provide meaningful data analysis. Chances are the business decision makers in an organization doesn't know how and doesn't know the data exists.
Getting your chops up in the statistical and applied math of predictive modeling can be done on your own via online learning or in a more structured classroom setting. Do it in baby steps if you have no applied math background. Start with basic statistics 101 and move on to more advanced.
Thanks. I have broad experience in the lending and data analysis. But I've started to learn python and may be moving into a role that is for an auto lender. Should be fun if it happens
Does sound like fun. Good luck!

Good luck to you rawraw. You'll find auto finance to be a rewarding and challenging industry

Good luck to you rawraw. You'll find auto finance to be a rewarding and challenging industry
It certainly seems that way. I've had exposure to it via reviewing the models and such, but never created them myself. Thanks for the luck:)