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Musings of a dad with too much time on his hands and not enough to do. Wait. Reverse that.

Building resiliency in your children

Boy, that kid is resilient

Some time ago, I posted some thoughts on why a person should pursue genealogy as a hobby. Well, according to researchers, here’s another good reason: it helps build resiliency in your children!

Here’s a salient quote from the article:

Researchers speculated that children who know about their own family history have a stronger feeling of being part of something bigger than themselves. Children who knew family history felt part of a larger family.

Daily Herald, 23 February 2019

I wholeheartedly concur. If not you, then some recent or distant ancestor undoubtedly struggled against insurmountable odds and prevailed to give you and your family a fighting shot at success. I think knowing that struggle and sacrifice does build hope and promise in your children and bonds your family even more tightly.

So where do you start? The article suggested a few questions you can pose to your children:

  • Do you know where your grandparents grew up?
  • Do you know where your parents met?

Seems like a good start to me!

(Thanks to Dick Eastman for linking to the article.)

How to make more money in 2019

LifeHacker published an article recently called, How to make more money in 2019. Basically, the article surveyed five people, collected some of their financial particulars, and asked what their plans were for earning more money in 2019. Here’s the short list of money-making strategies I gleaned from the article:

  • Work a second job
  • Reduce frivolous spending
  • Establish and stick to a budget
  • Acquire more skills (programming, negotiating, etc.)
  • Acquire certifications, graduate degree, etc.
  • Take on more work responsibilities in hopes a raise will follow
  • Find a new job that pays more
  • Increase your financial literacy through reading and research
  • Set meaningful financial goals

The salaries, jobs, and ages of the interviewees lined up like so:

The age/salary progression seems reasonable: the older interviewees tended to earn more than the younger ones.

The Applications Engineer resides in Michigan, the Data Specialist in Portland, Oregon, and the rest in California. Given that California and Oregon have some of the highest costs of living in the country, I just don’t see how the four that live in those states can fare on those salaries, particularly the Data Specialist:

The interviewees also estimated their expenses versus savings. Here’s what I gathered from the article (I couldn’t get a good estimate on the expenses of the Applications Engineer, so I left her out):

First, it seems to me that we’re not hearing the full story from the Data Specialist or the second Writer. You’d have to be pretty extraordinary to be saving 40% of your income let alone 75%!

In September 2018, the Bureau of Labor Statistics released a Consumer Expenditures report where they listed average expenditures at a somewhat higher percentage:

More interestingly, the average income they listed in the report was pre-tax. As taxes tend to constitute the biggest expense of households, I would expect that savings slice to shrink even further.

A few of the interviewees mentioned that they’re still paying off student loan debt, which, sadly, seems all too common these days. In particular, the Data Specialist, who’s working for a non-profit in Oregon, is working off some $60k in student loan debt. That combination of factors makes my head hurt.

All said, as a parent, I must do what I can to a) beef up my own financial literacy and b) pass what I know on to my kids so they’re as financially prepared as possible.

Two convenient techniques to collect financial data for analysis

As I stare college bills in the face and know that retirement awaits in the not-too-distant future, I’m working hard to improve my financial literacy. One way I’m trying to do this and work on my programming and data analysis techniques at the same time is to download financial data directly and do some direct analysis with tools like pandas. Right from the start, I’ve found two convenient ways to download the financial data you wish to examine.

Option 1: quandl

Quandl is a great source for datasets and they make accessing their data even easier with their API. One big drawback I’ve encountered with the API is that I have yet to get it to work behind my company’s firewall. The only other point to note is that if you intend on making over 50 calls in one day, you’ll need to get a free API key.

import quandl

df_amzn1 = quandl.get("WIKI/AMZN", start_date="2018-01-01", end_date="2019-01-01")
df_amzn1.head()
The quandl result set

Option 2: pandas-datareader

Pandas-datareader wraps a lot of interesting APIs and hands the results back to you in the form of a pandas dataframe. In my example, I’m using pandas-datareader to call the Yahoo finance API to get Amazon stock price information. Apparently, the Yahoo API has changed too much/too frequently to the point where the pandas-datareader folks have said “enough, already” and deprecated their support of the API. Not content to let go just yet, others have offered up the aptly named fix-yahoo-finance package that can be used to plug the Yahoo hole in pandas-datareader. One other note: unlike quandl, I have successfully used pandas-datareader behind my company’s firewall. If you find yourself with SSL and timeout exceptions at work, you may want to give pandas-datareader a try.

from pandas_datareader import data as pdr
import fix_yahoo_finance as yf

yf.pdr_override()
df_amzn2 = pdr.get_data_yahoo("AMZN", start="2018-01-01", end="2019-01-01")
df_amzn2.head()
The pandas-datareader result set
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