Monthly Archives

February 2018

Trading Strategy Analysis using Python and the FFN Package – Part 1

by Stuart Jamieson 27 February 2018

In this post I will be reviewing and running through examples of using the brilliant python module, “ffn – Financial Functions for Python“, which has been created by Philippe Morissette and released on the MIT license. The github page can be found here (http://pmorissette.github.io/ffn/index.html)

The module helps quickly carry out analysis of trading strategies and financial asset price series/history. It can deal with single series using the “PerformanceStats” class, or multiple combined assets simultaneously using the “GroupStats” class.

Let’s begin with the simple, one asset/strategy case and run through some examples.

We begin by importing the relevant modules (and if working in a Jupyter notebook, the call to allow matplotlib objects to be rendered in the browser).

27 February 2018 28 comments

Basic Data Analysis

Stock Clusters Using K-Means Algorithm in Python

by Stuart Jamieson 8 February 2018

written by Stuart Jamieson

For this post, I will be creating a script to download pricing data for the S&P 500 stocks, calculate their historic returns and volatility and then proceed to use the K-Means clustering algorithm to divide the stocks into distinct groups based upon said returns and volatilities.

So why would we want to do this you ask? Well dividing stocks into groups with “similar characteristics” can help in portfolio construction to ensure we choose a universe of stocks with sufficient diversification between them.

The concept behind K-Means clustering is explained here far more succinctly than I ever could, so please visit that link for more details on the concept and algorithm

I’ll deal instead with the actual Python code needed to carry out the necessary data collection, manipulation and analysis.

First things first, we need to collect the data – lets run our imports and create a simple data download script that scrapes the web to collect the tickers for all the individual stocks within the S&P 500.

8 February 2018 12 comments

Uncategorized

Stock Return Heatmap using Seaborn

by Stuart Jamieson 7 February 2018

written by Stuart Jamieson

Hi all, this post is going to be a relatively short and to the point run through of creating an annotated heatmap for the Dow 30 stock returns using the Python Seaborn package.

Let’s start with what is a heatmap actually is; it’s defined as “a representation of data in the form of a map or diagram in which data values are represented as colours.”

This makes it a great tool to quickly visualise the magnitude of stock returns over time in a matrix/grid format, using a colour map/scale to represent the size and direction of each stock’s percentage change over that period of time.

Creating a heatmap without stock ticker labels annotated, i.e. a heatmap annotated with just the numerical value of the relevant cell is a very easy process, thanks to the power and ease of use of Seaborn.

7 February 2018 6 comments