Today, the number of platforms that allow users to make transactions with exchange rate data is increasing day by day. These platforms and businesses are often known as trading platforms because they provide instant currency conversions. Recently, many trading platforms have provided their users with foreign exchange historical rates. Historical currency exchange rate data is vital for many traders.
Platforms that provide foreign exchange historical rates usually aim to have more users. They are also ready to serve globally. Today, many users and traders shape their investment strategies according to historical currency exchange rate data. In this article, we will first talk about the importance of historical currency exchange rate data. Then, after introducing the best API that provides this data, we will visualize and analyze the historical currency exchange rate data of this API with the Python programming language.
What is the Importance of Using Historical Currency Exchange Rate?
The importance of using historical exchange rate data is huge for both traders and regular users. They use this data for many purposes and gain advantages. Some of the most popular use cases where they use this data are as follows.
Market Analysis and Trading Strategies
Traders can easily analyze past price movements and identify patterns, trends, and market behavior by examining historical exchange rate data. These analyses give them insight into developing trading strategies and when to buy and sell currencies. By understanding past exchange rate movements, traders can better predict potential future price movements.
Algorithmic Automated Trading Strategies
Algorithmic traders often use historical exchange rate data to backtest and optimize their trading strategies. By testing their strategies with historical data, they can evaluate the effectiveness of their algorithms and make improvements as needed.
Financial Planning and Budgeting
Individuals and businesses dealing with international transactions or travel often use historical exchange rate data to plan their budgets. Understanding historical exchange rate fluctuations with accurate data helps them accurately estimate the true cost of foreign purchases, expenditures, or investments.
Forecasting Models
Economists and financial analysts create forecasting models for the future using historical exchange rate data. They try to predict future exchange rate movements by analyzing past trends and economic indicators.
Performance Comparison
Investors and businesses compare the performance of different assets or currencies over time. Historical exchange rate data helps investors and businesses assess the strength or weakness of one currency over another over the years and make informed investment decisions.
Research and Academic Purposes
Researchers, academics, and students working in economics, finance, and international business use historical exchange rate data for a variety of studies, analyses, and academic articles.
Fixer API: The Most Reliable Historical Currency Exchange Rate Provider
Fixer API is known as the most reliable foreign exchange rate API in the market in 2023. This API is a forex API with more than 6 years of industry experience and serves global companies such as Kraken, Samsung, Bershka, and Microsoft.
The most important aspects that make Fixer API stand out are its reliability and providing accurate data. It supports 170 official currencies around the world and obtains data on these currencies from multiple official and reputable financial institutions such as the European Central Bank. It provides historical exchange rates as well as live data for 170 currencies. Fixer provides all historical data up to January 1, 1999, with a single API call.
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Fixer API works very fast and has a flexible infrastructure. Thus, it integrates easily into all programming languages. Moreover, there are integration codes for more than one programming language in its unique documentation.
What is the Pricing Policy of the Fixer API?
Fixer is an API that offers foreign exchange rate service on the internet with flexible and affordable subscription plans. Firstly, this API has a free subscription plan.
The free subscription plan offered by the Fixer API to its users is limited to 1,000 API calls per month. It does not require any credit card information. Additionally, it updates data hourly.
It has multiple paid subscription plans. Its paid plans offer 10,000 API calls and hourly updates for $14.99 per month, while its most comprehensive plan offers 500,000 API calls and 60-second updates for $99.99.
Finally, you can buy the paid plans offered by Fixer API for 20% more with annual subscriptions.
Visualize and Analyze Historical Currency Exchange Rate (USD/JPY) in Python
In this step, we will visualize the historical exchange rates presented to us by the Fixer API with the Python programming language and make some analysis. We will use the time-series endpoint of this application Fixer API and visualize and analyze USD/JPY currencies.
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Get an API Key
API key acquisition is the first step we need to start before we start developing the application. With the API key provided by Fixer, we will be able to easily access its services and data.
To obtain an API key, we will sign up for one of the paid subscription plans offered by Fixer API. The free subscription plan does not support the time-series endpoint. On the ‘Dashboard’ page that opens after registration, we can view our own API key and even our API usage.
Install the Required Libraries
Now we will download the Python libraries that we will use in the application. For this, let’s first open a terminal in the file path where we intend to develop the application and run the following command:
pip install requests matplotlib numpyWith this code, we have installed the following libraries on our computer:
- requests: It is a fairly common library for sending and receiving HTTP requests in Python. It provides an easy and efficient way to get data from APIs, send requests to websites, or also send data.
- matplotlib: This library is a powerful library for creating visualizations in Python. It can be used to create line charts, histograms, scatter charts, and many more types of charts.
- numpy: This is a basic library for scientific calculations and data manipulation in Python. It provides the opportunity to perform fast and efficient operations on arrays and provides also mathematical functions.
Code
Now we can start developing the application. For this, let’s open a file named ‘historical-exchange-rates-with-fixer.py’ in the file path where we downloaded the necessary libraries and then put the following codes in it:
import requests
import matplotlib.pyplot as plt
import numpy as np
def get_exchange_rates():
url = "https://data.fixer.io/api/timeseries"
params = {
"access_key": "YOUR-ACCESS-KEY",
"start_date": "2012-05-01",
"end_date": "2012-05-25",
"base": "USD",
"symbols": "JPY"
}
response = requests.get(url, params=params)
if response.status_code == 200:
data = response.json()
print(data)
return data["rates"]
else:
print("Error retrieving data::", response.status_code)
return None
exchange_rates = get_exchange_rates()
dates = list(exchange_rates.keys())
rates = [rate["JPY"] for rate in exchange_rates.values()]
plt.figure(figsize=(10, 6))
plt.plot(dates, rates, marker='o', linestyle='-')
plt.xlabel("Date")
plt.ylabel("JPY Value")
plt.title("USD/JPY Exchange Rate")
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()
# Data analysis
rate_values = np.array(rates)
mean_rate = np.mean(rate_values)
max_rate = np.max(rate_values)
min_rate = np.min(rate_values)
std_dev = np.std(rate_values)
print(f"Mean Value: {mean_rate:.4f}")
print(f"Maximum Value: {max_rate:.4f}")
print(f"Minimum Value: {min_rate:.4f}")
print(f"Standard Deviation: {std_dev:.4f}")
With this code, we will obtain historical exchange rates between USD/JPY between 2012-05-01 and 2012-05-25 with Fixer API’s time-series endpoint, and then visualize and analyze these data with Python programming language.
Before starting the application, let’s put our own API key in the ‘YOUR-ACCESS-KEY’ field.
Test
We are now ready to start the application. Let’s open a terminal in the file location of the application and run the application with the following command:
python historical-exchange-rates-with-fixer.pyThe graph we get after running the application is as follows:
Analysis data is as follows:
Mean Value: 79.8039
Maximum Value: 80.3574
Minimum Value: 79.1885
Standard Deviation: 0.3333Conclusion
To summarize, historical currency exchange rates are frequently used for many purposes today. The number of historical currency exchange rate APIs that provide this data is increasing day by day. You can easily visualize and analyze with Python by using one of the APIs that provide this service, which is reliable and offers high-accuracy data.
FAQs
Q: What is the Historical Currency Exchange Rate?
A: The historical exchange rate is the exchange rate that expresses the changes in a particular currency (such as USD, JPY, and EUR) in the past.
Q: How Many API Calls Does the Fixer API’s Free Subscription Plan Offer Per Month?
A: Fixer API’s free subscription plan offers 1,000 API calls per month. Also, it has multiple subscription plans with high limits.
Q: How Many Currencies Does the Fixer API Provide the Historical Currency Exchange Rate?
A: Fixer API provides historical exchange rate data for 170 different official currencies.
Q: How to Visualize and Analyze Historical Currency Exchange Data with Python?
A: To visualize and analyze historical exchange rate data using Python, you can first pull data from the API with the requests library. Then, create line charts with the matplotlib library. Finally, you can perform statistical analysis with the numpy library.
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