The vast majority of quantitative analysis I do these days is either in Matlab or via the spreadsheets of Google Drive. Less commonly I’ll use Python if I need to distribute heavy web scraping among web servers or C++ if I want to use OpenCL for something very specific.
Recently I’ve been experiment with the spreadsheets on Google Drive (formerly Google Docs) and finding a lot of success. The Google Doc’s spreadhseet is a cloud based Excel analogue. There are several major advantages
- It is far easier for a non-programmer to use than scripting languages like Matlab or Python.
- A simple google finance protocol is integrated.
- Other web resources can be loaded and parsed through google’s servers, which mitigates connection flooding complaints and presumably creates caching opportunities which reduces overhead on the resource.
- It has all of the advantages of being cloud based: auto-saving, backup, easy collaboration, and remote access.
The major downsides are
- The number of calls to google finance or remote resources are limited.
- As spreadsheets go, it is moderately watered down compared to Excel.
“Well that sounds nice, but what can I actually do with it?” - possibilities include, but are not limited to :
- Automatically track the price of stocks and highlight those which reach a “buy” value you set.
- Load any value from recent financial statements, allowing you to create customized valuation metrics.
- Access historical price information about stock price.
- Create charts tracking the performance of a portfolio of stocks over time.
- Automatically load and update stock ratings from third parties (morningstar, motley fools’ CAPS)for a set of tickers.
During a series of forthcoming articles, I will explain how to achieve these effects in your own spreadsheets.