After some investigation on how to get started on algo trading, I learned that the important thing is to get started with the bare minimum as soon as possible. A lot of posts on reddit suggest that many spend tons of time reading books, watching videos, but never get their hands dirty with trading. That is the biggest mistake, as you learn best from doing.
In the part 1 of the series, we created a docker-compose file for easier spin up of our web app. In this post, we continue on to the part where we deploy the web app on cloud so anyone can access it online. Exciting isn’t it? Let’s jump right in.
骑车这个爱好是我最近两年发展起来的。
在漫长的5年博士和3年工作里,奉行“能坐着就不站着,能躺着就不坐着的”的节能主义者的我,终于在看到镜中自己圆润的肚子后顿悟了生命在于运动。为了保证既要运动,又不能累的养生方式,我盯上了骑车这个运动。我自认为我的骑车底子还是过硬的。且不说我的高中三年都是骑车上下学,而我家离高中大约是40分钟的自行车程。这约等于我每天被动骑车运动一个半小时。这之上还有课间操,体育课和放学打球等其他活动。满打满算怎么也是翻身上车骑着就走蹬个半小时大气不带喘的水平。既然初步评估十分满意,我遂去车行入手单车一辆。
Recently I have been following this awesome Flask tutorial to build a microblog. The author does a great job breaking things down, and I was able to follow step by step from hello world to containerizing my web app. However, one cruicial step is left as an exercise for the reader to figure out, which is to organize everything with docker-compose, as well as actually deploying the web app on AWS. I took a stab at it and got it working, checkout my microblog here. Looking back, it was easy but not very straight forward. So I thought I will write down what I did and what I learned for future references. Here we go!
Every once in a while I dream about creating something by coding a side project, but never really follow through. As years pass by I think maybe it will be a better idea if I start by tracking down all the interesting project ideas for future references. So here it goes.
Linear regression is usually the first model one will learn in a machine learning course due to its simplicity and popularity. While the idea of linear regression seems straight forward and easily understandable, there are many details around linear regression that are often overlooked but are important to know for ML practitioners. This article aims to provide couple of different perspectives on linear regression, what are its assumptions and why are those assumptions necessary, and go over some topics including multicolinearity and confidence intervals.