MarketHolidaysLibrary "MarketHolidays"
The MarketHolidays library compiles market holidays (including historical special market closures) into arrays, which can then be utilized in TradingView indicators and strategies to account for non-trading days. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com
getHolidays(_country)
The getHolidays function aggregates holiday data from different time periods to create a single array with market holidays for a specified country.
Parameters:
_country (string) : The country code for which to retrieve market holidays. Accepts syminfo.country or pre-set country code in ISO 3166-1 alpha-2 format.
Returns: An array of timestamps of market holidays \ non-trading days for the given country.
Marketclosure
holidays_2020to2025Library "holidays_2020to2025"
This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com
holidays(_country)
Parameters:
_country (string)
holidays_2015to2020Library "holidays_2015to2020"
This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com
holidays(_country)
Parameters:
_country (string)
holidays_2010to2015Library "holidays_2010to2015"
This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com
holidays(_country)
Parameters:
_country (string)
holidays_2005to2010Library "holidays_2005to2010"
This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com
holidays(_country)
Parameters:
_country (string)
holidays_2000to2005Library "holidays_2000to2005"
This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com
holidays(_country)
Parameters:
_country (string)
holidays_1990to2000Library "holidays_1990to2000"
This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com
holidays(_country)
Parameters:
_country (string)
holidays_1980to1990Library "holidays_1980to1990"
This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com
holidays(_country)
Parameters:
_country (string)
holidays_1970to1980Library "holidays_1970to1980"
This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com
holidays(_country)
Parameters:
_country (string)
holidays_1962to1970Library "holidays_1962to1970"
This dataset is part of my "MarketHolidays" library. The datasets were split into different libraries to overcome compiling limitations, streamline the process of removing specific time frames if not needed, and to enhance code execution speed. The timestamps are generated using a custom Python script that employs the 'pandas_market_calendars' library. To build your own set of arrays, you can find the script and instructions at github.com
holidays(_country)
Parameters:
_country (string)
