ETF fundamentals data

QuantConnect provides a wide range of datasets, including US Equity data, options, futures, Forex, CFD, and cryptocurrencies, among others. For specific ETF-related data, such as industry classification, total assets under management (AUM), and average volume, the platform offers the US ETF Constituents dataset. This dataset tracks the constituents and weighting of US Equities in over 2,650 ETF listings, starting from January 2009 and delivered on a daily basis. However, it primarily focuses on the constituents and their weightings within the ETFs rather than direct metrics like industry classification or AUM of the ETFs themselves.

For more detailed ETF information like industry, AUM, and average volume, you might need to look into integrating external data sources or leveraging custom data capabilities on QuantConnect. While QuantConnect does not directly provide such specific ETF metrics in its standard datasets, you can import custom data into your algorithms. This could involve sourcing the data from external providers and then using the custom data importing feature to integrate this information into your QuantConnect algorithms.

Here's a basic example of how you might start to incorporate custom data into your algorithm:

class MyCustomData(PythonData):
    def GetSource(self, config, date, isLiveMode):
        return SubscriptionDataSource("your_data_source_url", SubscriptionTransportMedium.RemoteFile)

    def Reader(self, config, line, date, isLiveMode):
        customData = MyCustomData()
        try:
            # Parse your data line here and assign values to customData properties
            # For example:
            # customData.Value = parsed_value
        except ValueError:
            # Handle parsing errors
            return None

        return customData

def Initialize(self):
    self.AddData(MyCustomData, "your_custom_symbol", Resolution.Daily)

This example is a starting point for how to define and use custom data in your algorithm. You would need to replace "your_data_source_url" with the actual URL of your data source and "your_custom_symbol" with a symbol for your custom data. The parsing logic in the Reader method should be adapted based on the format of your external data.

Remember, when using external data, you should ensure compliance with the data provider's terms of use and any relevant regulations.

thank you, this is for accessing data from URL, right?

What if I have the data available in a local file (e.g. in excel), can I import my local data in quantconnect?

Yes, the previous example was for accessing data from a URL. If you have data available in a local file, such as an Excel file, and you're running algorithms locally using the LEAN engine, you can indeed import your local data into QuantConnect by converting it into a format that LEAN can read (typically CSV) and then using a custom data class to read it.

Here's a simplified process for using local data from a file:

1. Convert Your Excel Data to CSV: LEAN works best with flat files, so you'll want to convert your Excel data into CSV format. Most spreadsheet software, including Microsoft Excel and Google Sheets, allows you to save or export your data as a CSV file.

2. Place Your CSV File in the Correct Directory: Copy your CSV file to the data directory in your LEAN project. The exact location can vary based on how you've set up LEAN, but it's typically under Lean/Data/custom.

3. Create a Custom Data Class: You'll need to create a custom data class in your algorithm that tells LEAN how to read your CSV file. Here's an example of what that might look like:

from QuantConnect.Data import BaseData

class MyCustomData(BaseData):
    def GetSource(self, config, date, isLiveMode):
        # Specify the local file path relative to the data folder
        filePath = f"custom/mydata/{date.strftime('%Y%m%d')}.csv"
        return SubscriptionDataSource(filePath, SubscriptionTransportMedium.LocalFile)

    def Reader(self, config, line, date, isLiveMode):
        if not (line.strip() and line[0].isdigit()): return None

        data = MyCustomData()
        try:
            # Assuming CSV format is: Date,Value
            parts = line.split(',')
            data.Time = datetime.strptime(parts[0], '%Y-%m-%d')
            data.Value = float(parts[1])
            # You can add more custom fields here
        except ValueError:
            return None

        return data

1. Add Your Custom Data to Your Algorithm: In your algorithm's Initialize method, add your custom data using the AddData method:

def Initialize(self):
    self.AddData(MyCustomData, "MyCustomSymbol", Resolution.Daily)

1. Access Your Data in OnData: You can now access your custom data in the OnData method of your algorithm:

def OnData(self, data):
    if data.ContainsKey("MyCustomSymbol"):
        customData = data["MyCustomSymbol"]
        # Do something with customData

This process allows you to use locally stored data within your QuantConnect algorithms when running the LEAN engine locally. Remember, the paths and the data parsing logic in the Reader method should be adapted to match your specific data format and file structure.