QuantRocket VS python xlrd

• Comprehensive Data Sources
  QuantRocket integrates with various data providers, offering access to a wide range of historical and fundamental data, which is crucial for quantitative research and backtesting strategies.
• Multi-Asset Support
  The platform supports multiple asset classes including equities, futures, options, and forex, providing flexibility for users to design diverse trading strategies.
• Easy Deployment
  QuantRocket's integration with Docker allows for easy deployment and management of the trading infrastructure, making it accessible even for users with limited technical expertise.
• Backtesting Capabilities
  It provides powerful backtesting tools using Moonshot and Zipline, enabling users to evaluate the effectiveness of their trading strategies efficiently.
• Interactive Brokers Integration
  The platform seamlessly connects with Interactive Brokers, allowing users to execute their strategies in a live trading environment with a reliable brokerage.

Possible disadvantages

• Complexity
  The platform can be complex for beginners due to its comprehensive features and the requirement to understand Docker, which might pose a steep learning curve for some users.
• Cost
  QuantRocket is a paid platform, and the subscription fees might be a barrier for hobbyist traders or those with a limited budget.
• Limited Community Support
  While there is documentation available, the community around QuantRocket is relatively small compared to more popular platforms, which might mean fewer resources and shared strategies.
• Dependence on Third-Party Data Providers
  Users may incur additional costs if they choose to subscribe to premium data feeds from third-party providers integrated with QuantRocket.
• System Requirements
  Running QuantRocket effectively requires robust hardware and system resources, which may not be feasible for all users, especially those using personal computers.

• Simplicity
  xlrd provides a straightforward and easy-to-use API for reading Excel files, making it accessible for beginners and quick implementations.
• Widely Used
  xlrd has been a popular choice for handling Excel files in Python, which means there is a lot of available documentation and community support.
• Efficient Reading
  It is optimized for reading data from Excel files without loading entire data into memory, which is beneficial for handling large files.

Possible disadvantages

• No Write Support
  xlrd is designed solely for reading, and it does not support writing or modifying Excel files.
• Limited to Older Excel Formats
  With version 2.0 and above, xlrd only supports the older .xls Excel file format and does not support .xlsx files.
• Deprecated Features
  Due to changes in dependencies and updates to Excel formats, some features in xlrd have become deprecated or removed, which can limit its functionality.

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Tracking QuantRocket since Oct 2021.

• I need to read multiple excel files, extract a column from each and compose a new file

  So to get this out of the way first, xlrd has less features than openpyxl and in addition only works with the old '.xls' format, not the newer '.xlsx' format. Even on the xlrd's Github repo it says: 'Please use openpyxl where you can... '. Source: over 4 years ago

• Sending Bulk SMS using Africas Talking, Python and Excel

  There are few alternative libraries for reading and writing excel files: Pandas, Xlrd , openpyxl among others. In the end I settled for openpyxl as I had the most experience Using it and it had support for .xlsx files. - Source: dev.to / over 5 years ago