Monthly Archives

April 2016

Python Backtesting – ETF Mean Reversion – creating the ticker pairs

by Stuart Jamieson 28 April 2016

Right, welcome back and sorry for the slight delay between posts…I’ve been way more busy than I had hoped. Onto our python backtesting!

So this, I guess, could be considered the first proper post regarding the ETF mean reversion backtest script we’re trying to come up with. In the last post we went over creating our SQLite database and populating it with the ETF data scraped from www.etf.com. So we should now have over 1000 ETF tickers at our disposal to pull down and use in conjunction with the Pandas DataReader to pull daily pricing data from the web and use in our backtest.

28 April 2016 4 comments

Trading Strategy Backtest

Scraping ETF data from the web and sticking it in an SQLite3 database

by Stuart Jamieson 11 April 2016

written by Stuart Jamieson

SQLite database python web scrape…

As promised, here is the first part of my “ETF mean reversion strategy backtest” series. This article deals with scraping ETF data from the web and sticking it in an SQLite3 database.

I’m using SQLite as it’s very easy to set up, and requires nothing complex to get up and running. Firstly you need to visit SQLite download page and download the pre compiled binaries from the Windows section.

11 April 2016 18 comments

Trading Strategy Backtest

Python Mean Reversion Backtest for ETFs

by Stuart Jamieson 10 April 2016

written by Stuart Jamieson

Python Mean Reversion Backtest for ETFs…

I have been looking into using Python to create a backtesting script to test mean reversion strategies based on cointegrated ETF pairs. I have broken down the process in my head into several stages, each of which will form the basis of my next few blog posts. I’m not sure how many I will have to split the whole “shebang” across, but here’s the basic outline.

10 April 2016 0 comment

Basic Data Analysis

What are ‘skew’ lookin’ at? – calculating skew and kurtosis with Python…

by Stuart Jamieson 4 April 2016

written by Stuart Jamieson

Python…skew…kurtosis…

So you have a series of returns you wish to analyse….mean and variance are easy to calculate…how easy does Python make it to calculate skew and kurtosis?

When we look at a series of investment returns, we tend to concentrate on the first 2 ‘moments’ of the distribution; that is the mean and the variance of the returns. The mean gives us a representation of the average expected return, and the variance gives us a measure of the dispersion of returns around the mean. But those two measures don’t give us the full picture. There are ‘higher order’ moments to be aware of…

4 April 2016 4 comments