Scraping Historical Currency Exchange Rates for Financial Analysis

Historical exchange rate data is essential for forex analysis, international business planning, and financial research. This guide shows you how to build a scraper that collects and analyzes currency exchange rates over time. Why Scrape Exchange Rates? Commercial forex data APIs charge hundreds per month. Many free sources limit historical access. Web scraping lets you build comprehensive datasets going back years from multiple sources. Setup pip install requests beautifulsoup4 pandas matplotlib Scraping Exchange Rate Data Here's a scraper for historical exchange rates: import requests from bs4 import BeautifulSoup import pandas as pd from datetime import datetime , timedelta def scrape_exchange_rates ( base = " USD " , target = " EUR " , days = 365 ): rates = [] for i in range ( 0 , days , 30 ): # Monthly snapshots date = ( datetime . now () - timedelta ( days = i )). strftime ( " %Y-%m-%d " ) params = { " api_key " : " YOUR_SCRAPERAPI_KEY " , " url " : f " https://www.x-rates.com/his