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Messages - jz451

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1
Investors - LC / Re: Need Help Writing Automation Script
« on: July 17, 2019, 10:32:36 AM »
That was my assumption. Not a big deal but makes the list of datafame column names a little long to write out in a specific order. Seems like I answered my own questions for the most part after some trial and error. Last step is to figure out how to pickle the final model into the script file and setup the AWS modules.

2
Investors - LC / Re: Need Help Writing Automation Script
« on: July 15, 2019, 12:35:27 PM »
Yes I did, tried the different flavors of converting to dataframe and got the same output as using pd.Dataframe()

Edit: After only selecting the "loans" column it correctly put it into a dataframe.

Also, is there a way to retrieve specific variables instead of all 119 variables when retrieving data for new loans?

3
Investors - LC / Need Help Writing Automation Script
« on: July 14, 2019, 02:59:59 PM »
Hi All,

Been a while since last posting as I haven't been as active investing, but have lurked from time to time.

For those of you have have written scripts for automating loan selection using the API I need some of your assistance. My script will select loans using a model I have backtested to classify which new loans will "Pay in Full". So far I am able to get the cash balance and have written the code for transforming the data, but need help with how to retrieve the data and for placing an order. Here's some questions I have:

1. What platform did you use to run the script? I'm planning to use AWS but wanted to see what others have used.

2. I'm new to web scraping using Python, so running Get/Post calls using Requests isn't intuitive to me when there's no examples in the LC API documentation. For those willing may I see your script sans selection filters/models to get a better idea of the structure for writing a script that functions properly?

3. For retrieving the data, is there a way to get a subset of the variables or am I only able to retrieve all variables? Also, I noticed when trying to convert the JSON to a dataframe, it's formatted as an overall dictionary with a list of loans and each loan is a dictionary of the data as you can see in the attached image. What's the best way to properly turn it into a dataframe so each loan is a separate row with the variables as columns?

Thanks for the help.



4
General P2P Lending Discussion / Re: Roofstock
« on: August 06, 2017, 05:46:33 PM »
From what you have been saying it sounds like you would not like real estate investing. You have to treat it like a business and not like a mutual fund or ETF where you set it and forget it. It involves a lot of work that some people don't want to do, which is fine, might not be your cup of tea. The properties on Roofstock are meant to generate cash flow just like LC and not increase value through appreciation. Properties listed are going to be cheaper than in higher value areas like on the west coast, and typically older and may or may not need some TLC. But as you probably saw there are people who buy the proeprties listed on Roofstock.



I just don't see it. Who desires the geographic diversity of North Carolina or Florida?  If they offered like high end condos in high rent areas -- that probably would need minimum upkeep, maybe that would be worth it.  I can just see lots of maintenance issues in low end Florida and not the best tenants ever.

I also just inherently do not trust "property managers" that I can't keep an eye on. Your putting your entire investment in stranger's hands and as far as i know.. Roofstock offers zero protection the day the sale closes. They are, conveniently, just a way to match people up.

5
General P2P Lending Discussion / Re: Roofstock
« on: August 06, 2017, 02:39:17 PM »
Over 90% of the people use one of the property management companies through the website.


I just don't know why you would do that. You could just do that locally - right? I don't see what Roofstock brings to it?

It's for people looking to invest out of state. Roofstock is headquartered in Oakland, CA so naturally it's difficult to invest in the Bay Area let alone elsewhere in California. Renting out a house in expensive areas is impossible unless the house is paid in all cash.

6
General P2P Lending Discussion / Re: Roofstock
« on: August 06, 2017, 02:17:21 PM »
I looked into it when I saw it on TV. It's basically a marketplace to buy/sell rental proeprties. It's not worth it since most of the properties are trash. You're better off looking on the MLS the traditional way and hiring a proeprty management company.

7
Been a rough month had a surge for my small account in notes being delinquent.

Combined ANAR:  9.64%
Weighted Average Interest Rate:   20.4%
Weighted Average Age of Portfolio: 26.47 mos
Number of Notes: 214

A (0.0%) B (0.0%) C (2.9%) D (39.3%) E (43.4%) F (9.3%) G (5.1%)
36 (0.2%) 60 (99.8%)

vs All Accounts: 94.69
vs Similar Age Accounts: 99.52
vs Similar Age and WAIR Accounts: 98.73

8
Adjusted Net Annualized Return:   10.82%
Weighted Average Interest Rate:   20.43%
Weighted Average Age of Portfolio:   25.51 mos
Number of Notes: 218

Grade A(0%) B (0%) C(2.6%) D(37%) E(44.9%) F(8.9%) G(6.5%)

Term  36(0.2%) 60(99.8%)


9
Combined Adjusted Net Annualized Return:  10.94%
Weighted Average Interest Rate: 20.6%
Weighted Average Age of Portfolio: 24.46 mos
Number of Notes: 211

Grade
A (0.0%) B (0.0%) C (2.3%)
D (36.5%) E (43.0%) F (10.7%) G (7.4%)

Term
36 (0.3%) 60 (99.7%)

vs All Accounts: 96.91
vs Similar Aged Accounts: 99.83
vs Similar Aged and WAIR Accounts: 100

10
Investors - LC / Re: LC Announces Q1 2017 Earnings
« on: May 05, 2017, 03:20:34 PM »
After looking at the filing I find it striking that the engineering/product development costs have risen and that G&A have gone up as well. If they stayed flat year-over-year they could have saved $17 million.Makes you wonder what they are spending money to develop/engineer and where the administrative costs are going.

11
Adjusted Net Annualized Return:    11.47%
Weighted Average Interest Rate:    20.93%
Weighted Average Age of Portfolio:    23.79 mos
Number of Notes:    195

Grade
A (0%) B (0%) C (2.5%) D (34.4%) E (41.2%) F (11.4%) G (10.0%)

Term
Payments  36 (0.4%) 60 (99.6%)

vs All Accounts: 97.76
vs Similar Age Accounts: 99.83
vs Similar Age and WAIR Accounts: 100.00

12
I started the process of investing $2500 this month into my account which is going to result in a big change to my results over the next couple of months as I slowly churn through the cash.

Combined ANAR:  11.50%
Weighted Average Interest Rate:  21.24%
Weighted Average Age of Portfolio:   23.31 mos
Number of Notes:   169

A(0.0%) B (0.0%) C(0.0%) D(31.4%) E(42.1%) F(13.5%) G(13.0%)
36(0.5%) 60(99.5%)

vs All Accounts: 97.79
vs Similar Age Accounts: 99.81
vs Similar Age and WAIR Accounts: 99.02

13
Woops how did I miss that. Also good thing I'm reposting as the ANAR didn't get updated last night.  :P

Adjusted Net Annualized Return :   10.93%
Weighted Average Interest Rate:   22.08%
Weighted Average Age of Portfolio:   22.98 mos
Number of Notes:   134

Grade
A (0%) B (0%) C (0%)
D (19.9%) E (43.7%) F (18.9%) G (17.5%)

Term
36 (0.8%) 60 (99.2%)


vs All Accounts: 97.37
vs Similar Age Accounts: 99.75
vs Similar Age and WAIR Accounts: 100.00

Adjusted Net Annualized Return :   10.87%
Weighted Average Interest Rate:   22.08%
Weighted Average Age of Portfolio:   22.98 mos
Number of Notes:   134

vs All Accounts: 97.37
vs Similar Age Accounts: 99.75
vs Similar Age and WAIR Accounts: 100.00
A-G? 36/60?

14
Adjusted Net Annualized Return :   10.87%
Weighted Average Interest Rate:   22.08%
Weighted Average Age of Portfolio:   22.98 mos
Number of Notes:   134

vs All Accounts: 97.37
vs Similar Age Accounts: 99.75
vs Similar Age and WAIR Accounts: 100.00


15
Investors - LC / Re: Back Testing
« on: February 26, 2017, 12:26:30 PM »
All you have to do is provide your credit card info and it will let you look at more than three filters. I had the same problem months ago when they changed that.

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