​Simplifying NSE Trading Day Checks with the nse-trading-calendar Python Library​

For traders and developers working with the Indian stock market, accurately identifying trading days on the National Stock Exchange (NSE) is crucial. The nse-trading-calendar Python library offers a straightforward solution to determine whether a specific date is a trading day and its type.​

What is nse-trading-calendar?

The nse-trading-calendar is a Python package designed to check if a given date is a trading day on the NSE and to identify the type of trading day. It categorizes days as:​

  • Normal Trading Day: Regular days when the market operates.​
  • Special Trading Day: Occasional days when the market is open due to specific reasons, such as compensating for previous closures.​
  • Unknown: Dates that don’t fall into the above categories or lack sufficient data.​

This functionality is particularly useful for applications that require precise market schedules, such as backtesting trading strategies, scheduling automated trades, or analyzing market patterns.​

Note that the information on whether it’s a normal or special trading day is available from the year 2016.

Key Features

  • Simple Installation: Easily install the package using pip:​
    pip install nse-trading-calendar==0.1.1
  • Easy to Use: With minimal code, determine the trading status of any date.​
  • Lightweight: The package is concise, ensuring quick integration without unnecessary bloat.​
  • Open Source: Licensed under the MIT License, allowing for flexibility in usage and contributions.​

How to Use

Here’s a simple example to get started:​

from nse_calendar.calendar import NSETradingCalendar

calendar = NSETradingCalendar()
status = calendar.get_trading_day_info("2023-10-02")
print(status)

In this example, the function get_trading_day_info will return True or False along with:​

  • 0 for a normal trading day
  • 1 for a special trading day​
  • None if the date is not a trading day or data is unavailable​

The output for the above example is (False, None) as it is not a trading day. For a normal trading day, output will be (True, 0).

This straightforward approach allows developers to programmatically assess trading days, facilitating more accurate financial analyses and operations.​

Ideal Use Cases

  • Backtesting Trading Strategies: Ensure that historical data aligns with actual trading days to improve the accuracy of backtests.​
  • Automated Trading Systems: Schedule trades only on valid trading days, avoiding errors on holidays or weekends.​
  • Market Analysis Tools: Filter data to include only trading days, providing clearer insights into market trends.​
  • Financial Reporting: Generate reports that accurately reflect trading activity, excluding non-trading days.​

Conclusion

The nse-trading-calendar Python library serves as a valuable tool for anyone involved in the Indian financial markets. By providing an easy method to identify trading days, it aids in the development of more reliable and efficient trading applications.​

For more information or to contribute to the project, visit the PyPI page.