# Day Count Conventions

The day count conventions are used to determine how the interest calculations are performed for different fixed-income securities.

It is commonly expressed as a fraction. The numerator is usually 30 or actual. The numerator is usually, 360, 365 or actual.

The day count conventions vary based on the market, location and currency. For example, euro denominated bonds follow actual/actual day-count convention. The London inter-bank market follows actual/360 convention (except in a few cases).

It’s a good idea to have a clear understanding of these conventions and how to apply them to securities and markets.

Here is a summary of these conventions.

Actual/actual | For both numerator and denominator, the actual number of days is used. So, in case of a leap year, the denominator will be 366. Commonly used for sterling bonds, Euro-denominated bonds, and US Treasury bonds. |

Actual/365 | The numerator is the actual number of days, while the denominator is always 365. Commonly used for sterling bonds, Euro-denominated bonds, and US Treasury bonds. |

Actual/360 | The numerator is the actual number of days, while the denominator is always 360. Here, each month is assumed to be of 30 days. Commonly used for Eurocurrency Libor rates (except sterling) |

30/360 | Each month is assumed to have 30 days with an exception that if the last day is the 31st and the first day is not 30th or 31st then that month has 31 days. The denominator is always 360. This convention is common for calculating accrued interest on domestic US bonds such as Yankee bonds, corporate bonds, and municipal bonds. |

30E/360 | E refers to Eurobond basis. This assumes that all months have 30 days, including February. The denominator is always 360. |

## Data Science in Finance: 9-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 includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $39 (Regular $57)## Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.