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Automated web scraper for extracting information about Exchange-Traded Funds (ETFs) eligible for investment in the French PEA account from the Boursorama website.

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ETF Scrapper

Table of Contents

Introduction

The ETF Scrapper is a Python script designed to scrape information about Exchange-Traded Funds (ETFs) from the French bank website "Boursorama". In France, investors can use a Plan d'Épargne en Actions (PEA) to invest in ETFs, but finding a comprehensive list of eligible ETFs can be challenging. This script aims to address this issue by extracting data on ETFs, including those eligible for PEA investment, and saving it to an output Excel file for ease of use.

Technologies

The script is written in Python and utilizes the BeautifulSoup library for web scraping.

Features

  • Scrapes information about ETFs from the Boursorama website.
  • Identifies ETFs eligible for investment in a PEA.
  • Saves the extracted data to an Excel file for easy access and analysis.

Attributes Scraped for Each ETF

  • ISIN: International Securities Identification Number.
  • Name: Name of the ETF.
  • Price: Current price of the ETF.
  • Fees: Fees associated with investing in the ETF.
  • Company: Company managing the ETF.
  • Category: Category or sector of the ETF.
  • Reference Index: Index tracked by the ETF.
  • Link: Link to the ETF's page on the Boursorama website.
  • Is Leveraged: Indicates whether the ETF is leveraged.

Installation

  1. Clone the repository.
  2. Install the required dependencies using the requirements file requirements.txt.

Usage

  1. Run the script main.py.
  2. Once the scraping is complete, the script will generate an Excel file containing the extracted data.

Next Steps

  • Implement a PowerBI interface for visualizing and analyzing the scraped data.
  • Enhance the code to improve robustness, error handling, and performance.

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Automated web scraper for extracting information about Exchange-Traded Funds (ETFs) eligible for investment in the French PEA account from the Boursorama website.

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