Introduction

As a developer, fintech startup, SaaS team, or financial analyst, understanding the historical foreign exchange (FX) trends is crucial when it comes to strategizing around business transactions, forecasting market trends, and pricing products more accurately in the global market. This article will guide you on how to use the OpenExchangeAPI to fetch historical FX data, particularly focusing on USD to EUR trends.

We will utilize Python, a popular language for data analysis, and will implement data computation using pandas, and visualization using matplotlib. This tutorial will demonstrate how to fetch multi-year USD to EUR data, calculate rolling averages, plot historical volatility, and draw insights.

Fetching Historical Forex Data

Firstly, we need to fetch the historical data. OpenExchangeAPI provides a simple way to accomplish this task with its historical FX endpoint. Below is a Python code snippet that demonstrates this:

import requests
import pandas as pd

def fetch_historical_data(start_date, end_date):
    base_url = 'https://api.openexchangeapi.com/v1/historical'
    api_key = 'your_api_key'
    base_currency = 'USD'
    target_currency = 'EUR'

    date_range = pd.date_range(start=start_date, end=end_date)
    data = []

    for date in date_range:
        url = f"{base_url}/{date.strftime('%Y-%m-%d')}?base={base_currency}"
        headers = {"Authorization": f"Bearer {api_key}"}
        response = requests.get(url, headers=headers)
        if response.status_code == 200:
            data.append({'date': date, 'rate': response.json()['rates'][target_currency]})

    return pd.DataFrame(data)

df = fetch_historical_data('2020-01-01', '2025-01-01')

This function fetches historical USD to EUR exchange rates from 2020 to 2025. The data is saved in a pandas DataFrame for further analysis.

Data Computation with Pandas

With the data now in a pandas DataFrame, we can calculate the rolling averages. Pandas provides a simple and efficient way to calculate rolling averages with its rolling() function:

df['rolling_avg'] = df['rate'].rolling(window=30).mean()

This code calculates the 30-day rolling average of the exchange rates.

Plotting Historical Volatility with Matplotlib

For visualizing the volatility of the exchange rates, we will utilize matplotlib. Here’s a simple code snippet to plot the historical volatility:

import matplotlib.pyplot as plt

plt.plot(df['date'], df['rate'], label='USD to EUR')
plt.plot(df['date'], df['rolling_avg'], label='30-day Rolling Average')

plt.legend(loc='upper left')

plt.title('USD to EUR Trends (2020 - 2025)')
plt.xlabel('Year')
plt.ylabel('Exchange Rate')

plt.show()

This code plots the original exchange rates and their 30-day rolling average, offering a clear view of the volatility and the overall trend of the USD to EUR exchange rate.

Drawing Insights from the Analysis

By analyzing historical FX data, businesses can draw insights to aid in decision-making processes. For example, if a business notes an increasing trend in the USD to EUR exchange rate, they might decide to postpone purchases from Europe until the exchange rate stabilizes, or hedge against the risk by purchasing currency forwards.

Conclusion

In this article, we’ve seen how to fetch historical FX data using OpenExchangeAPI, perform data computation using pandas, and visualize the results using matplotlib. By analyzing historical FX data, businesses can better understand market trends, enabling them to price products more accurately and potentially save costs in foreign transactions.

Remember, the key to effective FX data analysis is to continually monitor the trends and adjust your strategies accordingly. With OpenExchangeAPI, this process is made simpler and more efficient, providing you with the necessary tools to make informed decisions.