Monthly Archives

July 2018

Seaborn Module and Python – Distribution Plots

by Stuart Jamieson 22 July 2018

I thought for this post I would look into the Seaborn library – Seaborn is a statistical plotting library and is built on top of Matplotlib. It has really nice looking default plotting styles and also works really well with Pandas DataFrames – so we can leverage the work we have done with Pandas in previous blog posts and hopefully create some great plots.

Seaborn can be installed just like any other Python package by using “pip”. Go to your command line and run:

pip install seaborn

The official documentation page for Seaborn can be found here and a lovely looking gallery page showing examples of what is possible with Seabon can be found here. You can click on any of the images on the gallery page and it will present you with example code on how to produce that particular plot. Another important page is the API page, which references the various available plot types – this can be found here.

I am going to try to break the Seaborn capabilities down into various categories – and begin with the plots that allow us to visualise the distribition of a data set

22 July 2018 2 comments

Native Excel Functionality with Python and openpyxl Module

by Stuart Jamieson 9 July 2018

written by Stuart Jamieson

In this article I shall be looking into using the Python module “openpyxl” to manipulate data within the Python ecosystem, while also being able to tap into excel functionality directly. I believe Python is a much better ecosystem within which to do any kind of data munging/analysis, however Excel is a much used platform, favoured by many as the means of final presentation once the munging/analysis has been completed.

Also, while using Python within a business office environment, users can often come across the situation whereby they need to present their findings/results to their colleagues who often times don’t even have Python or the necessary modules installed.

So how about this – being able to use Python to do the data analysis grunt work, AND being able to use Python to call native Excel methodology including conditional formatting, chart creation/insertion among other things, rather than having to save tabular data to excel/csv and THEN opening Excel and creating charts and including formatting that way.

Sounds good to me!

9 July 2018 3 comments

Trading Strategy Backtest

Mean Reversion Pairs Trading With Inclusion of a Kalman Filter

by Stuart Jamieson 4 July 2018

written by Stuart Jamieson

In this article we are going to revisit the concept of building a trading strategy backtest based on mean reverting, co-integrated pairs of stocks. So to restate the theory, stocks that are statistically co-integrated move in a way that means when their prices start to diverge by a certain amount (i.e. the spread between the 2 stocks prices increases), we would expect that divergence to
eventually revert back to the mean. In this instance we would look to sell the outperforming stock,and buy the under performing stock in our expectance that the under performing stock would eventually “catch up” with the overpeforming stock and rise in price, or vice versa the overperforming stock would in time suffer from the same downward pressure of the underperforming stock and fall in relative value.

Hence, pairs trading is a market neutral trading strategy enabling traders to profit from virtually any market conditions: uptrend, downtrend, or sideways movement.

4 July 2018 68 comments