CIBOR 3-Month Historical Data API: Timeseries, Charts & Downloads
In the fast-paced world of finance, accurate and timely interest rate data is crucial for developers, economists, and financial analysts. The CIBOR 3-Month (Copenhagen Interbank Offered Rate) is a key interbank rate in Denmark, reflecting the average interest rate at which banks lend to one another. This blog post will explore how to effectively utilize the Interest Rates API from interestratesapi.com to access CIBOR 3-Month historical data, enabling users to perform time series analysis, generate charts, and download data for further analysis.
Understanding the Importance of CIBOR 3-Month Data
The CIBOR 3-Month rate is significant for various financial applications, including loan pricing, risk management, and economic forecasting. By leveraging the Interest Rates API, developers can access historical data, analyze trends, and make informed decisions based on the latest market conditions. This API provides a comprehensive suite of endpoints that allow users to retrieve current rates, historical data, and perform time series analysis.
Key API Endpoints for CIBOR 3-Month Data
The Interest Rates API offers several endpoints that are particularly useful for working with CIBOR 3-Month data:
- /api/v1/latest: Retrieve the latest CIBOR 3-Month rate.
- /api/v1/historical: Access historical rates for a specific date.
- /api/v1/timeseries: Fetch a series of rates between two dates.
- /api/v1/ohlc: Obtain OHLC (Open, High, Low, Close) data for candlestick charting.
- /api/v1/fluctuation: Analyze changes in rates over a specified period.
Fetching Latest CIBOR 3-Month Rate
To get the most recent CIBOR 3-Month rate, you can use the /api/v1/latest endpoint. This endpoint returns the latest value for specified symbols, including CIBOR 3-Month.
cURL Example:
curl "https://interestratesapi.com/api/v1/latest?symbols=CIBOR_3M&api_key=YOUR_KEY"JSON Response Example:
{
"success": true,
"date": "2026-05-23",
"base": "MIXED",
"rates": {
"CIBOR_3M": 5.33
},
"dates": {
"CIBOR_3M": "2026-05-23"
},
"currencies": {
"CIBOR_3M": "DKK"
}
}This response indicates that the latest CIBOR 3-Month rate is 5.33 DKK as of May 23, 2026.
Accessing Historical CIBOR 3-Month Data
For point-in-time lookups, the /api/v1/historical endpoint allows users to retrieve the CIBOR 3-Month rate for a specific date. This is particularly useful for analyzing historical trends or for compliance purposes.
cURL Example:
curl "https://interestratesapi.com/api/v1/historical?date=2025-06-15&symbols=CIBOR_3M&api_key=YOUR_KEY"JSON Response Example:
{
"success": true,
"date": "2025-06-15",
"base": "DKK",
"rates": {
"CIBOR_3M": 5.33
},
"currencies": {
"CIBOR_3M": "DKK"
}
}This response shows that on June 15, 2025, the CIBOR 3-Month rate was 5.33 DKK.
Time Series Analysis with CIBOR 3-Month Data
The /api/v1/timeseries endpoint is essential for developers looking to analyze trends over a specified period. This endpoint allows users to fetch a series of rates between two dates, which is invaluable for time series analysis.
cURL Example:
curl "https://interestratesapi.com/api/v1/timeseries?start=2025-05-23&end=2026-05-23&symbols=CIBOR_3M&api_key=YOUR_KEY"JSON Response Example:
{
"success": true,
"base": "DKK",
"start_date": "2025-05-23",
"end_date": "2026-05-23",
"rates": {
"CIBOR_3M": {
"2025-01-02": 5.33,
"2025-01-03": 5.33,
"2025-01-06": 5.33
}
},
"frequencies": {
"CIBOR_3M": "daily"
},
"currencies": {
"CIBOR_3M": "DKK"
}
}This response provides daily rates for CIBOR 3-Month between the specified dates, allowing for detailed analysis of trends and fluctuations.
Creating Candlestick Charts with OHLC Data
For visualizing the CIBOR 3-Month data, the /api/v1/ohlc endpoint provides OHLC data, which is essential for creating candlestick charts. This is particularly useful for financial analysts who need to visualize trends over time.
cURL Example:
curl "https://interestratesapi.com/api/v1/ohlc?symbols=CIBOR_3M&period=monthly&start=2025-05-23&end=2026-05-23&api_key=YOUR_KEY"JSON Response Example:
{
"success": true,
"period": "monthly",
"start_date": "2025-05-23",
"end_date": "2026-05-23",
"rates": {
"CIBOR_3M": [
{
"period": "2025-01",
"open": 5.50,
"high": 5.50,
"low": 5.33,
"close": 5.33,
"data_points": 23
}
]
}
}This response provides the OHLC data for CIBOR 3-Month, which can be used to create candlestick charts using libraries like Chart.js or Plotly.
Analyzing Rate Fluctuations
The /api/v1/fluctuation endpoint allows users to analyze the change statistics of CIBOR 3-Month over a specified date range. This is useful for understanding the volatility of the rate and making informed decisions based on historical performance.
cURL Example:
curl "https://interestratesapi.com/api/v1/fluctuation?start=2025-05-23&end=2026-05-23&symbols=CIBOR_3M&api_key=YOUR_KEY"JSON Response Example:
{
"success": true,
"rates": {
"CIBOR_3M": {
"start_date": "2025-05-23",
"end_date": "2026-05-23",
"start_value": 5.50,
"end_value": 5.33,
"change": -0.17,
"change_pct": -3.09,
"high": 5.50,
"low": 5.25
}
}
}This response provides insights into the fluctuations of the CIBOR 3-Month rate, including the percentage change and the highest and lowest values during the specified period.
Building a Python Data Pipeline
For developers looking to automate the retrieval and analysis of CIBOR 3-Month data, building a Python data pipeline can be an effective solution. Below is an example of how to fetch data, convert it into a pandas DataFrame, and export it to CSV or Parquet format.
Python Code Example:
import requests
import pandas as pd
# Fetch timeseries data
response = requests.get(
'https://interestratesapi.com/api/v1/timeseries',
params=dict(start='2025-05-23', end='2026-05-23', symbols='CIBOR_3M', api_key='YOUR_KEY')
)
data = response.json()
# Convert to DataFrame
df = pd.DataFrame(data['rates']['CIBOR_3M']).T
df.index = pd.to_datetime(df.index)
# Export to CSV
df.to_csv('cibor_3m_data.csv')
# Export to Parquet
df.to_parquet('cibor_3m_data.parquet')
This code snippet demonstrates how to retrieve CIBOR 3-Month data, convert it into a DataFrame, and export it for further analysis.
Common Pitfalls in Time Series Analysis
When working with time series data, developers should be aware of common pitfalls, such as:
- Missing Dates: Ensure that the data covers all required dates, especially when dealing with monthly symbols.
- Frequency Considerations: Understand the frequency of the data (daily vs. monthly) and how it impacts analysis.
- Data Points Interpretation: Be cautious when interpreting the number of data points, as it may affect statistical calculations.
Conclusion
The Interest Rates API from interestratesapi.com provides a powerful toolset for accessing and analyzing CIBOR 3-Month data. By leveraging the various endpoints, developers can retrieve the latest rates, historical data, and perform comprehensive time series analysis. This capability is essential for building robust fintech applications, conducting economic research, and making informed financial decisions.
To get started with the Interest Rates API, visit Get started with Interest Rates API and explore the features available to enhance your financial applications.
