I completed the AWS Solutions Architect Associate exam in November 2017, after about eight weeks study, with a score of 85%.
Below are a series links to blog posts containing all my study notes, and finally, another blog post containing my thoughts on the exam, as well as some exam feedback and tips for those who will sit the exam after me.
In this blog post I’ll show you how to scrape Income Statement, Balance Sheet, and Cash Flow data for companies from Yahoo Finance using Python, LXML, and Pandas.
I’ll use data from Mainfreight NZ (MFT.NZ) as an example, but the code will work for any stock symbol on Yahoo Finance.
The screenshot below shows a Pandas DataFrame with MFT.NZ balance sheet data, which you can expect to get by following the steps in this blog post:
After taking you step by step on how to fetch data from the balance sheet, I’ll show you how to generalise the code to also generate a DataFrame containing data from the Income Statement, and Cash Flow statement.
After creating the Pandas DataFrames, I’ll then show you how to scrape data for multiple symbols, and finally, export everything to an Excel file, so you’ll have output that looks something like this:
This post was last updated in April, 2020.
Prior to October, 2019, Yahoo Finance conveniently had all this data in a regular HTML table, which made extracting the data super easy. Since then, they’ve updated the page with a new structure, which was a wee bit tricker to get the data from. Fortunately, it’s still possible. Read on to find out how.
In this blog post, you will learn how to create a Python app using Flask and the Google API which will:
By the time you get to the end of this blog post, you’ll have built a basic Google Drive file browser which looks something like:
This blog post is divided up into sections which progressively build up an app which interacts with the user’s Google Drive. If you only want to find out about how to do user Authentication with Google and Python, feel free to stop there.
In this blog post, you’ll learn how to host a sample Python web app on Elastic Beanstalk, using HTTPS, without a load balancer.
After following the steps in this blog post, you should end up with something that looks like the following image:
This article is written for the intermediate F# audience who has a basic familiarity of stock trading and technical analysis, and is intended to show the basics of implementing a backtesting system in F#.
If you’re an F# beginner it may not take too long for you to get up to speed on the concepts if you check out a few resources. In particular:
The backtesting strategy which you will implement is a simple SMA crossover where the fast line is 10 bars and the slow one 25. When the fast line crosses above the slow line, a buy signal will be triggered, and when the fast line cross below the slow line, a sell signal will be triggered. Only one long position will exist at any time, so the system will not trigger another buy order until after the long position is sold.
A collection of links, with a Wellington focus, which I frequently use to work out if it’s worth going for a dive or not.
Feel free to get in touch if you have any links worth adding.
MetService - 5 day forecast for Wellington
MetService - 10 day forecast for Wellington
Swellmap - Surf Forecast Conditions and Marine Weather
Windy - Interactive wind forecast maps
LINZ - Hydrographic raster chart