This Week: The Story of ConvertKit - From $0 to $968,000 in Monthly Revenue
- Jan 11, 2019
A collection of 5 things from the week that I found interesting, am enjoying, or am working on.
This week: management is doing things right. Leadership is doing the right things
- Jan 4, 2019
A collection of 5 things from the week that I found interesting, am enjoying, or am working on.
Tony Alexander Presentation Notes - things which affected the NZ housing market over the decades
- Jan 2, 2019
I recently attended a presentation by BNZ’s Chief Economist, Tony Alexander.
One of the topics that really caught my attention was his perspective on the things that influenced the housing market over the decades, from the 1950’s until today.
I wrote up a bunch of notes to summarise his points.
If you’re interested in getting a bit of perspective on the NZ property market, and some of the things that caused it’s near-relentless movement upward over time, then read on.
How to scrape Yahoo Finance and extract fundamental stock market data using Python, LXML, and Pandas
- Apr 3, 2020
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.