{"id":18887,"date":"2026-09-27T04:12:59","date_gmt":"2026-09-27T04:12:59","guid":{"rendered":"https:\/\/www.simtrade.fr\/blog_simtrade\/?p=18887"},"modified":"2026-09-27T04:13:02","modified_gmt":"2026-09-27T04:13:02","slug":"how-to-install-and-run-python-on-your-computer-a-step-by-step-guide","status":"publish","type":"post","link":"https:\/\/www.simtrade.fr\/blog_simtrade\/how-to-install-and-run-python-on-your-computer-a-step-by-step-guide\/","title":{"rendered":"How to Install and Run Python on Your Computer (A Step-by-Step Guide)"},"content":{"rendered":"\n<p><a href=\"https:\/\/www.linkedin.com\/in\/hadrienpuche\" target=\"_blank\"><img decoding=\"async\" style=\"padding: 5px\" title=\"\" src=\"https:\/\/www.simtrade.fr\/blog_simtrade\/wp-content\/uploads\/2026\/06\/img_Hadrien_Puche_bis-scaled-e1781527786395.jpeg\" alt=\"Hadrien Puche\" width=\"133\" align=\"right\"><\/a><\/p>\n\n<p>In finance, the ability to rapidly acquire, clean, and manipulate data is a key skill that can help you get an edge over other students and job applicants. While Excel (with VBA) remains widely used and is sufficient for most basic modeling like a DCF, Python offers far more scalability, automation, and mathematical power than Excel.<\/p>\n\n<p>In this article, <a href=\"https:\/\/www.linkedin.com\/in\/hadrienpuche\" target=\"_blank\">Hadrien Puche<\/a> (ESSEC Business School, <i>Grande \u00c9cole<\/i> Program, Master in Management, 2023-2027) will help you understand how to:<\/p>\n<ul>\n  <li>Understand the core components of a Python environment for finance<\/li>\n  <li>Choose the most secure and efficient development setup for financial data<\/li>\n  <li>Install Miniconda and manage isolated virtual environments<\/li>\n  <li>Set up Visual Studio Code (VS Code) as your primary coding workspace<\/li>\n  <li>Run a test script to download and visualize real stock market data (AAPL)<\/li>\n<\/ul>\n\n<p>Lucky for you, there is no need to have a computer science background to start using Python.<\/p>\n\n<div style=\"background-color: #f8f9fa;border-left: 4px solid #0056b3;padding: 15px;margin: 20px 0;border-radius: 4px\">\n  <h4 style=\"margin-top:0;color:#0056b3\">Quick vocabulary for beginners<\/h4>\n  <p style=\"font-size:14px;margin-bottom:8px\">Before we dive in, let\u2019s demystify a few technical terms you will encounter frequently:<\/p>\n  <ul style=\"font-size:13px;margin-bottom:0;padding-left:20px\">\n    <li><b>Python:<\/b> A popular, high-level programming language created in 1991 by Guido van Rossum (and named after the BBC comedy series <i>Monty Python\u2019s Flying Circus<\/i>). Today, it is the gold standard in quantitative finance and data science because of its simple syntax and vast ecosystem of financial tools.<\/li>\n    <li><b>Library \/ Package:<\/b> A collection of pre-written code created by other developers so you don&#8217;t have to reinvent the wheel (e.g., <code>pandas<\/code> for data tables, <code>yfinance<\/code> for downloading stock market prices).<\/li>\n    <li><b>Dependency:<\/b> A package that another package needs in order to work properly.<\/li>\n    <li><b>Environment:<\/b> An isolated &#8220;sandbox&#8221; on your computer containing a specific version of Python and specific libraries, preventing projects from interfering with one another.<\/li>\n    <li><b>IDE (Integrated Development Environment):<\/b> The visual software app where you write, edit, and test your code (e.g., Visual Studio Code).<\/li>\n    <li><b>Extension:<\/b> An add-on (like an app from an App Store) that adds extra features to your IDE.<\/li>\n  <\/ul>\n<\/div>\n\n<p>In this first article, we will focus on helping you set up Python on your computer so that you can start learning how to use it. We will walk hand-in-hand through setting up a professional local workspace and testing that everything is working properly. Once that is done, you will find a list of follow-up articles at the end to explore real-world financial use cases.<\/p>\n\n\n<h2>Choosing your development environment<\/h2>\n\n<p>A <b>development environment<\/b> is simply the ecosystem of software tools you use to write, manage, and execute your code. When selecting a workspace for Python in finance, you have three main choices:<\/p>\n\n<ul>\n    <li><b>Local workspaces (like Visual Studio Code):<\/b> The standard choice for finance. Running your code locally (in your own computer) gives you full control over your local file systems, execution speed, and (most importantly) <b>data privacy<\/b>. In finance, working with proprietary trading algorithms or confidential client data means you cannot upload sensitive information to unvetted third-party servers.<\/li>\n    <li><b>Cloud notebooks (like Google Colab):<\/b> Cloud platforms are convenient for quick experiments because they require zero installation. However, they are generally unsuitable for professional financial workflows. You do not have full control over code execution or environment stability, and uploading confidential financial datasets or proprietary logic to public cloud infrastructure poses significant security and compliance risks.<\/li>\n    <li><b>AI-native code editors (like Cursor or Windsurf):<\/b> These editors heavily integrate AI to generate code automatically. While powerful for experienced developers, relying on AI tools too early prevents beginners from learning core programming logic, syntax, and debugging skills. It is far better to understand the core mechanics manually first.<\/li>\n<\/ul>\n\n<p>In this article, we will focus exclusively on establishing a local workspace using <b>Visual Studio Code (VS Code)<\/b>, which is the best tool to get comfortable with professional Python coding.<\/p>\n\n\n<p>We will also use <b>Jupyter Notebooks<\/b> (files ending in <code>.ipynb<\/code>). Unlike traditional Python scripts (files ending in <code>.py<\/code>) that execute the entire code at once, Jupyter Notebooks allow you to write and run code in individual &#8220;cells.&#8221; This block-by-block structure is especially powerful in finance for several reasons:<\/p>\n<ul>\n    <li><b>Isolating code:<\/b> You can work on and execute specific parts of your code independently (e.g., downloading data once, then tweaking the math in a separate cell without re-downloading).<\/li>\n    <li><b>Immediate feedback:<\/b> Data tables, charts, and outputs are displayed directly below the specific cell you just ran, and you do not have to execute the entire code each time.<\/li>\n    <li><b>Easier debugging:<\/b> By testing your logic piece-by-piece, identifying and fixing errors becomes significantly faster.<\/li>\n    <li><b>Better examples, tutorials, or exercises:<\/b> You can mix executable code with explanatory text and financial formulas, making it the perfect format for case studies and tutorials.<\/li>\n<\/ul>\n\n<p>As you code grows, using a Jupyter Notebook will be more and more useful.<\/p>\n\n\n\n<h2>What you need to install (and why)<\/h2>\n\n<p>Before installing anything, let\u2019s understand how the different components of your workspace fit together:<\/p>\n\n<ul>\n    <li><b>Miniconda (which includes Python &amp; Conda):<\/b> Standard Python comes with very limited built-in capabilities (basic mathematical functions). To perform financial analysis, you need external <b>packages\/libraries<\/b> like <code>pandas<\/code> or <code>yfinance<\/code>. <b>Conda<\/b> is a tool that manages these packages and isolates them into dedicated <b>virtual environments<\/b>.\n        <br><i>Note on Anaconda vs. Miniconda:<\/i> <b>Anaconda<\/b> is a big download that comes bundled with hundreds of packages you may never use. I suggest to use <b>Miniconda<\/b> because it is a lightweight version, containing only Conda and Python, allowing us to keep our setup clean and fast.<\/li>\n    <li><b>Virtual Environments:<\/b> Why do we need them? If you install every package into one single base Python installation, different projects will eventually require conflicting versions of the same library (a &#8220;dependency collision&#8221;), causing your scripts to crash. Virtual environments keep each project&#8217;s tools safely separated.<\/li>\n    <li><b>Visual Studio Code (VS Code):<\/b> A clean user interface where you write, edit, and debug your code. VS Code connects seamlessly to your Conda virtual environment to execute your scripts.<\/li>\n<\/ul>\n\n\n<h3>How the architecture works<\/h3>\n\n<p>The diagram below illustrates how your development setup functions:<\/p>\n \n<p style=\"text-align:center;margin:20px 0\">\n  <img decoding=\"async\" src=\"https:\/\/www.simtrade.fr\/blog_simtrade\/wp-content\/uploads\/2026\/08\/img_chart_python_setup.png\" alt=\"A graph showing the links between the user, VS Code, Miniconda, and Python.\" width=\"500\" style=\"margin-top:8px;border-radius:8px;max-width: 100%\">\n  <br>\n  <small style=\"color:#555\">Figure 1: How the User, VS Code, Miniconda Environment, and Python Engine interact.<\/small>\n<\/p>\n\n<ul>\n  <li><b>You (the User)<\/b> interact directly with <b>VS Code<\/b> to write commands and inspect results.<\/li>\n  <li><b>VS Code<\/b> sends your code to your isolated <b>Miniconda Virtual Environment (e.g., <code>my_environment<\/code> that you can create to store the packages that you will use in your own code)<\/b>.<\/li>\n  <li>Inside this environment, the <b>Python Engine<\/b> processes the math and logic, drawing upon the installed financial libraries (like <code>yfinance<\/code> and <code>pandas<\/code>).<\/li>\n  <li>The execution results (tables, charts, output logs) are sent back to <b>VS Code<\/b> for you to view.<\/li>\n<\/ul>\n\n<p><i>As a fun side note: you can technically write code in almost any text editor ! For a fun take on how far you could take this, check out <a href=\"https:\/\/www.youtube.com\/watch?v=gJXZ9KTufvA\" target=\"_blank\">this video<\/a>.<\/i><\/p>\n\n\n<h2>Step-by-step installation guide<\/h2>\n\n<h3>Step 1: Install Miniconda (Python + Conda)<\/h3>\n\n<p>Conveniently, downloading and installing <b>Miniconda<\/b> automatically installs Python, so this will be our first step.<\/p>\n\n<p>Head to the official <a href=\"https:\/\/docs.anaconda.com\/free\/miniconda\/\" target=\"_blank\">Miniconda Download Page<\/a>, select the installer for your operating system (Windows or macOS), and complete the installation using the recommended default settings.<\/p>\n\n\n<h3>Step 2: Install Visual Studio Code and Extensions<\/h3>\n\n<p>Visual Studio Code (VS Code) will serve as your Integrated Development Environment (IDE). As a quick reminder, an IDE is the main visual software application, where you will actually write, edit, test, and debug your code. You can think of it as the central command dashboard for all your financial programming projects.<\/p>\n\n<ol>\n    <li><b>Download &amp; Install:<\/b> Go to the official <a href=\"https:\/\/code.visualstudio.com\/Download\" target=\"_blank\">VS Code website<\/a>, download the installer for Windows or macOS, and follow the standard installation instructions.<\/li>\n    <li><b>Install Essential Extensions:<\/b> Launch VS Code. Click on the <b>Extensions<\/b> icon on the left-hand Activity Bar (or press <code>Ctrl+Shift+X<\/code> on Windows \/ <code>Cmd+Shift+X<\/code> on Mac). Think of extensions as add-ons from an app store that give VS Code superpowers. Search for and install:\n        <ul>\n            <li><b>Python<\/b> (by Microsoft) &#8211; Provides syntax highlighting, code completion, and interpreter selection.<\/li>\n            <li><b>Jupyter<\/b> (by Microsoft) &#8211; Enables interactive execution of code cells inside <code>.ipynb<\/code> notebook files.<\/li>\n        <\/ul>\n    <\/li>\n<\/ol>\n \n<p style=\"text-align:center;margin:20px 0\">\n  <img decoding=\"async\" src=\"https:\/\/www.simtrade.fr\/blog_simtrade\/wp-content\/uploads\/2026\/08\/img_VSC_marketplace_Python.png\" alt=\"VS Code Extensions Marketplace showing Python extension by Microsoft\" width=\"500\" style=\"margin-top:8px;border-radius:8px;max-width: 100%\">\n  <br>\n  <small style=\"color:#555\">Make sure to install the official Python and Jupyter extensions in VS Code.<\/small>\n<\/p>\n\n\n<h3>Step 3: Create your virtual environment via Terminal<\/h3>\n\n<p>Now, we will create a clean, isolated Conda environment named <code>my_environment <\/code> where our financial packages will live.<\/p>\n\n<ol>\n    <li><b>Open your command line interface:<\/b>\n        <ul>\n            <li><b>Windows 11 \/ 10:<\/b> Open the Start menu, search for <b>Anaconda Prompt<\/b>, and click to open it. <i>(Alternatively, you can open Windows Terminal \/ PowerShell, but Anaconda Prompt automatically initializes Conda for you).<\/i><\/li>\n            <li><b>macOS:<\/b> Open the <b>Terminal<\/b> app (press <code>Cmd + Space<\/code>, type &#8220;Terminal&#8221;, and press Enter).<\/li>\n        <\/ul>\n    <\/li>\n    <li><b>Run the following Conda &amp; Pip commands one by one:<\/b><\/li>\n<\/ol>\n\n<div style=\"background-color: #f6f8fa;border: 1px solid #d0d7de;border-radius: 6px;padding: 16px;font-family: monospace;white-space: pre;font-size: 13px;line-height: 1.45\">\n<span style=\"color: #6a737d\"># 1. Create an isolated environment named \u2018my_environment&#8217; with Python 3.11<\/span>\nconda create &#8211;name my_environment python=3.11 -y\n\n<span style=\"color: #6a737d\"># 2. Activate your new environment<\/span>\nconda activate my_environment\n\n<span style=\"color: #6a737d\"># 3. Upgrade pip and install core financial analysis libraries<\/span>\npip install &#8211;upgrade yfinance pandas numpy matplotlib notebook &#8211;no-cache-dir\n<\/div>\n\n<p><i>Pro-tip: Whenever you need to install additional packages in the future, open your terminal, activate your environment (<code>conda activate my_environment<\/code>), and run <code>pip install [package_name]<\/code>.<\/i><\/p>\n\n\n<h3>Step 4: Connect VS Code to your environment<\/h3>\n\n<p>Now that your environment and libraries are ready, you need to tell VS Code to use <code> my_environment<\/code> to run your code.<\/p>\n\n<ol>\n    <li><b>Open a workspace folder:<\/b> In VS Code, go to <b>File &gt; Open Folder&#8230;<\/b> and select or create a dedicated folder on your computer (e.g., <code>finance_python<\/code>). It does not matter where it is, you simply need somewhere to store your code files.<\/li>\n    <li><b>Create your files:<\/b> Click the <b>New File<\/b> icon in the Explorer sidebar to create two files:\n        <ul>\n            <li><code>test.py<\/code> (.py is to store python code)<\/li>\n            <li><code>notebook.ipynb<\/code> (.ipynb is the extension name for jupyter notebooks)<\/li>\n        <\/ul>\n    <\/li>\n    <li><b>Select the python interpreter:<\/b> Open <code>test.py<\/code>. Press <code>Ctrl+Shift+P<\/code> (Windows) or <code>Cmd+Shift+P<\/code> (macOS) to open the Command Palette, type <b><code>Python: Select Interpreter<\/code><\/b>, and press <b>Enter<\/b>.\n        <ul>\n            <li>VS Code should automatically list <b><code> my_environment<\/code><\/b>. Click on it.<\/li>\n            <li><i>If it doesn&#8217;t appear automatically:<\/i> Click <b>Enter interpreter path&#8230;<\/b> &gt; <b>Find&#8230;<\/b> and navigate directly to the executable file:\n                <ul>\n                    <li><b>Windows:<\/b> <code>C:\\Users\\YourUsername\\miniconda3\\envs\\my_environment\\python.exe<\/code><\/li>\n                    <li><b>macOS:<\/b> <code>\/Users\/YourUsername\/miniconda3\/envs\/my_environment\/bin\/python3<\/code><\/li>\n                <\/ul>\n            <\/li>\n        <\/ul>\n    <\/li>\n    <li><b>Select Jupyter Kernel:<\/b> Open <code>notebook.ipynb<\/code>. Click <b>Select Kernel<\/b> in the top-right corner of the window, choose <b>Python Environments&#8230;<\/b>, and select your <b><code>my_environment<\/code><\/b> path.<\/li>\n<\/ol>\n \n<p style=\"text-align:center;margin:20px 0\">\n  <img decoding=\"async\" src=\"https:\/\/www.simtrade.fr\/blog_simtrade\/wp-content\/uploads\/2026\/09\/img_VSC_create_testing_files.png\" alt=\"An image of the VS Code menu with notebook.ipynb and test.py created\" width=\"700\" style=\"margin-top:8px;border-radius:8px;max-width: 100%\">\n  <br>\n  <small style=\"color:#555\">Once this is done, your IDE should look juste like this.<\/small>\n<\/p>\n\n<div style=\"background-color: #fff8c5;border: 1px solid #d4a72c;border-radius: 6px;padding: 12px;margin: 15px 0;font-size: 13px\">\n  <b>Quick Troubleshooting Tips:<\/b>\n  <ul style=\"margin-bottom: 0;padding-left: 20px\">\n    <li><b>&#8220;No matching commands&#8221; error:<\/b> If typing <code>Python: Select Interpreter<\/code> gives no results, click inside the <code>test.py<\/code> editor window first to wake up the Python extension, or click <b>Select Python Interpreter<\/b> in the bottom-right status bar.<\/li>\n    <li><b>Environment missing from the list:<\/b> Make sure you activated the environment in terminal at least once, or use the direct path navigation detailed above.<\/li>\n  <\/ul>\n<\/div>\n\n\n<h2>Testing your installation<\/h2>\n\n<p>Now that VS Code is connected to <code>my_environment<\/code>, let&#8217;s run a simple test script to confirm that our setup can successfully fetch market data and display a stock chart. Open <code>test.py<\/code> or <code>notebook.ipynb<\/code>, paste the code below, and execute it:<\/p>\n\n<pre style=\"background-color: #f6f8fa;border: 1px solid #d0d7de;border-radius: 6px;padding: 16px;font-family: monospace;font-size: 13px;line-height: 1.45;margin: 0\"><code><span style=\"color: #6a737d\"># Import yfinance to download stock market data directly from Yahoo Finance<\/span>\nimport yfinance as yf\n\n<span style=\"color: #6a737d\"># Import matplotlib.pyplot (aliased as 'plt') to create financial charts and plots<\/span>\nimport matplotlib.pyplot as plt\n\n<span style=\"color: #6a737d\"># Download historical stock data for Apple Inc. (AAPL)<\/span>\nprint(\"Fetching financial data via Yahoo Finance API...\")\ndf = yf.download('AAPL', start='2023-01-01', end='2024-01-01')\n\n<span style=\"color: #6a737d\"># Display the first 5 rows of the downloaded data in the terminal \/ output window<\/span>\nprint(\"\\nFirst 5 rows of AAPL market data:\")\nprint(df.head())\n\n<span style=\"color: #6a737d\"># Plot historical closing prices<\/span>\nplt.figure(figsize=(10, 5))\nplt.plot(df['Close'], label='AAPL Close Price', color='#1d4ed8', linewidth=1.5)\nplt.title('Apple Inc. (AAPL) Historical Close Prices - 2023')\nplt.xlabel('Date')\nplt.ylabel('Price ($)')\nplt.grid(True, linestyle='--', alpha=0.5)\nplt.legend()\nplt.show()<\/code><\/pre>\n\n<p>If the market dataset downloads and a clean line chart of Apple&#8217;s stock price appears, congratulations! You have successfully configured a professional, local Python environment for financial engineering.<\/p>\n\n<p>This is how the output should look like if everything is working well :<\/p>\n \n<p style=\"text-align:center;margin:20px 0\">\n  <img decoding=\"async\" src=\"https:\/\/www.simtrade.fr\/blog_simtrade\/wp-content\/uploads\/2026\/09\/img_python_installation_testing_output.png\" alt=\"An image of the VS Code menu with notebook.ipynb and test.py created\" width=\"700\" style=\"margin-top:8px;border-radius:8px;max-width: 100%\">\n<\/p>\n\n<p>Congratulations ! You have successfully configured a professional, local Python environment, ready for financial engineering<\/p>\n\n<h2>A small tip about how to install more dependencies<\/h2>\n\n<p>As you now understand, dependencies such as Yfinance, Matplotlib or Pandas are extremely useful to write python code. If you need to install a new one, instead of having to launch conda on a terminal, you can simply run a !pip command inside of a jupyter notebook cell, with you environment enabled. This how it how it should look like, with the example of seaborn (an upgraded version of matplotlib for data visualization) :<\/p>\n \n<p style=\"text-align:center;margin:20px 0\">\n  <img decoding=\"async\" src=\"https:\/\/www.simtrade.fr\/blog_simtrade\/wp-content\/uploads\/2026\/09\/img_pip_install_example.png\" alt=\"An image of the VS Code menu with notebook.ipynb and test.py created\" width=\"700\" style=\"margin-top:8px;border-radius:8px;max-width: 100%\">\n<\/p>\n\n\n\n\n<h2>About the Author<\/h2> \n\n<p>This article was written in September 2026 by <a href=\"https:\/\/www.linkedin.com\/in\/hadrienpuche\" target=\"_blank\">Hadrien PUCHE<\/a> (ESSEC Business School, <i>Grande \u00c9cole<\/i> Program, Master in Management, 2023-2027).<\/p>\n\n<p>&nbsp;&nbsp;&nbsp;\u25b6 Discover all articles by <a href=\"https:\/\/www.simtrade.fr\/blog_simtrade\/author\/hpuche\/\" target=\"_parent\">Hadrien PUCHE<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In finance, the ability to rapidly acquire, clean, and manipulate data is a key skill that can help you get an edge over other students and job applicants. While Excel (with VBA) remains widely used and is sufficient for most basic modeling like a DCF, Python offers far more scalability, automation, and mathematical power than &#8230; <a title=\"How to Install and Run Python on Your Computer (A Step-by-Step Guide)\" class=\"read-more\" href=\"https:\/\/www.simtrade.fr\/blog_simtrade\/how-to-install-and-run-python-on-your-computer-a-step-by-step-guide\/\" aria-label=\"Read more about How to Install and Run Python on Your Computer (A Step-by-Step Guide)\">Read more<\/a><\/p>\n","protected":false},"author":163,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[5],"tags":[],"class_list":["post-18887","post","type-post","status-publish","format-standard","hentry","category-contributors"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.3 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How to Install and Run Python on Your Computer (A Step-by-Step Guide) - SimTrade blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.simtrade.fr\/blog_simtrade\/how-to-install-and-run-python-on-your-computer-a-step-by-step-guide\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Install and Run Python on Your Computer (A Step-by-Step Guide)\" \/>\n<meta property=\"og:description\" content=\"In finance, the ability to rapidly acquire, clean, and manipulate data is a key skill that can help you get an edge over other students and job applicants. 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