I have explored multiple cloud providers in China, including QingCloud, over the past two years. QingCloud focuses on private enterprises in China but also caters to many startups too. I wanted to find a way to automate a Virtual Private Cloud (VPC) setup and thought I would share my script to build out a bastion host in a secure VPC. Automating their infrastructure is incredibly simple given their powerful API partnered with their private network capabilities. Having worked with clouds in Amazon, Google, IBM, Microsoft, VMWare, Aliyun… I am impressed with how easy QingCloud makes establishing a VPC either from their web portal or automated script. Here is my automated deployment script.
After reading Dr. Yves Hilpisch’s article, “Algorithmic trading using 100 lines of python code,” I was inspired to give it a shot. I wanted to apply his guide on how to use a time series momentum algorithm because I have been interested in forex trading with cryptocurrencies. I set up a free forex trial account on OANDA, jumped into a jupyter notebook, and got to work. I hit an issue. OANDA changed their API from “v1” to “v20” and all new accounts default to the new API. I ended up rewriting his sample code to work with the new OANDA v20 API using a third party python library.
When I first got started I always found myself using R’s “plot” capability because, well, it is easy! Unfortunately, it lacks some advanced features — and the plots it produces are really ugly looking (subjective, but I bet you will agree with me). Luckily, there is a better tool for the job – ggplot. With only a few tricks you will find it just as easy to use.
If your smoke alarm or, in my case, water alarm goes off you want to know right away – even if you are currently half way across the world traveling in China. I run a fish tank. I take many precautions but you really can’t be too safe. I bought a set of Honeywell water sensors which I highly recommend. Sadly, this particular alarm is not IoT enabled. In fact, last I checked all the IoT alarm systems were terribly reviewed and overpriced. Hopefully that gets fixed soon. Until then, I needed to make do with what I had.
Buying a ring is a big decision. You have the whole “are they the one” decision. I can’t help you with that. Then you have the reality that this could likely be the first major financial decision that will impact both of you. Wouldn’t it be nice if you could save hundreds or even thousands of dollars?
I am not here to convince you to avoid buying a diamond (thanks, De Beers). Instead, I am going to show you a basic statistical programming technique with python and R known as a “linear regression model.” I will use a jupyter notebook to execute data analysis so you can see step by step how it works.
You might be able to use this to shop smartly by allowing you to compare an actual cost in store to a predicted price. My wife and I built and used this code in 2013 while engagement ring shopping together. Hope it helps others!
Let’s get started!