Float

To integrate float data from Finviz into QuantConnect, you can consider the following steps:

1. Custom Data Integration: QuantConnect allows for custom data integration, which means you can bring in data from external sources like Finviz. You would need to create a custom data type by extending the PythonData class and implementing the get_source and reader methods. This setup will allow you to fetch and process data from Finviz or any other external source.

2. Using add_data Method: In your algorithm's initialize method, you can use the add_data method to create a subscription for your custom data. This method requires specifying the data type, ticker, resolution, and other optional parameters like time zone and leverage.

3. Data Source and Transport: You can use various data transport mediums such as REST APIs or file downloads. The SubscriptionDataSource class helps define how and where to fetch the data. For live data, you might use a REST API endpoint provided by Finviz.

4. Partnership Proposal: If you believe that a direct partnership between QuantConnect and Finviz would be beneficial, you could draft a proposal highlighting the strategic advantages and user interest. This proposal could be shared with QuantConnect's support or business development team to explore potential collaboration.

5. Testing and Validation: Once you have set up the data integration, test it thoroughly to ensure the data's accuracy and reliability in your trading algorithms.

Here's a basic structure for a custom data class:

class FinvizFloatData(PythonData):
    def get_source(self, config, date, is_live_mode):
        if is_live_mode:
            return SubscriptionDataSource("https://finviz.com/api/float", SubscriptionTransportMedium.REST)
        else:
            return SubscriptionDataSource("path/to/historical/data.csv", SubscriptionTransportMedium.LOCAL_FILE)

    def reader(self, config, line, date, is_live_mode):
        data = FinvizFloatData()
        data.symbol = config.symbol
        # Parse the line or JSON response to extract float data
        # Example: data.value = parsed_data["float"]
        return data

In your algorithm:

class MyAlgorithm(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2020, 1, 1)
        self.set_end_date(2020, 12, 31)
        self.set_cash(100000)
        self.add_data(FinvizFloatData, "AAPL", Resolution.DAILY)

This setup provides a framework for integrating custom float data from Finviz into your QuantConnect algorithms.

Sources: Key Concepts - QuantConnect.com, Custom Data - QuantConnect.com, Requesting Data - QuantConnect.com, Key Concepts - QuantConnect.com, Custom Data Bitcoin Algorithm