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    Beginners Resources

    Learning Python – My Personal Journey

    by Stuart Jamieson 21 December 2018
    written by Stuart Jamieson

    I have been asked multiple times lately, by various different people, as to whether I could write a blog post about how I got from where I started (someone who had never coded before at all), to where I am now (someone who’s still firmly in the “learning” camp, but able to create a range of semi-useful code snippets).

    Those asking pointed out that although it was relatively interesting and fun reading my posts while following along, replicating my code line for line and then running the script to get their output, my posts weren’t great at actually helping people develop the necessary skills and knowledge to be able to branch out and create their own mini-programs and projects. Nor did I tend to point readers in the right direction of the actual resources that I myself had used and found useful to develop as a Python programmer.

    I totally take this on board, and appreciate there is a fundamental difference between just presenting my own mini-projects in blog posts and inspiring/helping others to be able to identify and create their own mini-project“wish-list”.

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    21 December 2018 6 comments
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  • Basic Data Analysis

    Seaborn Module and Python – Categorical Plots

    by Stuart Jamieson 21 September 2018
    by Stuart Jamieson 21 September 2018

    Carrying on from the last blog post, I am now going to shift attention to plotting categorical data with Seaborn. So let’s write our first few lines of code that deals with the import of various packages and loads our excel file into a DataFrame. The excel file we are using can be downloaded by clicking the download link below.

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  • Basic Data Analysis

    Seaborn Module and Python – Distribution Plots

    by Stuart Jamieson 22 July 2018
    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…

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  • Basic Data AnalysisBeginners Resources

    Native Excel Functionality with Python and openpyxl Module

    by Stuart Jamieson 9 July 2018
    by Stuart Jamieson 9 July 2018

    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…

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  • Trading Strategy Backtest

    Mean Reversion Pairs Trading With Inclusion of a Kalman Filter

    by Stuart Jamieson 4 July 2018
    by Stuart Jamieson 4 July 2018

    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…

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  • Basic Data Analysis

    Replicating Excel Functionality in Pandas

    by Stuart Jamieson 30 June 2018
    by Stuart Jamieson 30 June 2018

    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…

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  • Basic Data Analysis

    Trading Strategy Analysis using Python and the FFN Package – Part 2

    by Stuart Jamieson 15 March 2018
    by Stuart Jamieson 15 March 2018

    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,…

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  • Basic Data Analysis

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

    by Stuart Jamieson 27 February 2018
    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…

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  • Basic Data Analysis

    Stock Clusters Using K-Means Algorithm in Python

    by Stuart Jamieson 8 February 2018
    by Stuart Jamieson 8 February 2018

    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…

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  • Uncategorized

    Stock Return Heatmap using Seaborn

    by Stuart Jamieson 7 February 2018
    by Stuart Jamieson 7 February 2018

    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…

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About Me

About Me

I am a current PhD Computer Science candidate, a CFA Charterholder (CFAI) and Certified Financial Risk Manager (GARP) with over 16 years experience as a financial derivatives trader in London. I also hold an MSc in Data Science and a BA in Economics Finance / Machine Learning / Data Visualization / Data Science Consultant I am mostly interested in projects related to data science, data visualization, data engineering and machine learning, especially those related to finance.

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