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…

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…

This article is aimed at showing how to replicate some common Excel tasks using Python and the Pandas library. The point and click interface of Excel means the learning curve is somewhat lesssteep than it is for using Pandas – however once a certain level of proficiency is met when using Pandas, the possibilities presented are far greater than you…

Hi all, this is the second part to the “Trading Strategy Analysis using Python and the FFN Package” post (the first part can be found here). Last time we went over the use of the “PerformanceStats” object in ffn, whereas this time I want to concentrate on the “GroupStats” object. The former is for use with single series of data,…

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…

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…

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…

I thought for this post I would just continue on with the theme of testing trading strategies based on signals from some of the classic “technical indicators” that many traders incorporate into their decision making; the last post dealt with Bollinger Bands and for this one I thought I’d go for a Stochastic Oscillator Trading Strategy Backtest in Python. Let’s…

So, after a long time without posting (been super busy), I thought I’d write a quick Bollinger Band Trading Strategy Backtest in Python and then run some optimisations and analysis much like we have done in the past. It’s pretty easy and can be written in just a few lines of code, which is why I love Python so much…