How to Get Historical Exchange Rates?

If you are running or managing a global business with sales and operations in many different countries, then you will need the historical exchange rate data for various reasons such as consolidating and reconciling the accounts, identifying forex trends, hedging, and so on. For example, revenue earned in the month of March needs to be converted to the parent company’s currency using the March exchange rate.

The foreign exchange historical data is easily available on the Internet. You can access this data for the period you want for your desired currency pair. Some of the popular sources are XE.com, Oanda.com, x-rates.com, and FRB H.10 release. You can easily get the exchange rates for the past 20+ years.

The typical method to get the historical rates will be as follows:

  1. First, select the currency pair for which you want the historical data. You can use the 3-letter ISO currency symbols such as USD for US dollar, and EUR for Euro.
  2. Second, specify the period (from date and to date) for which you want the data. Follow any other instructions and you will be presented with historical data in a tabular format.

The rates you get will usually be average exchange rates. Some websites like oanda.com provide you the choice of price data you want such as Bid, Ask, and Mid-point. You can even specify the data frequency such as daily, or weekly.

The historical data is available for most standard currency pairs. However, for some currencies there are no direct quotes available for which you will have to calculate cross rates using data for two related currency pairs.

If you want to store this data, you can copy it in an excel sheet or you can automatically download these historical foreign exchange rates in an excel sheet. This excel sheet retrieves the historical rates from oanda.com.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.