Quantbook throws exception in live mode

The issue you're experiencing is due to the fact that QuantBook is not supported in live trading. QuantBook is a research environment tool that allows you to access QuantConnect's data sources for research purposes. It is not intended for use in live trading algorithms.

In your algorithm, you're trying to use QuantBook to add and remove equities. Instead, you should use the AddEquity and RemoveSecurity methods of the QCAlgorithm class. Here's how you can modify your code:

from AlgorithmImports import *

class QuantbookTestingAlgorithm(QCAlgorithm):

    def Initialize(self):

        self.SetTimeZone(TimeZones.London)

        self.AddUniverse(self.SelectSymbols)

        self.universe = {}

    def OnData(self, data):
        pass

    def OnSecuritiesChanged(self, changes):

        for s in changes.AddedSecurities:

            if str(s.Symbol) not in self.universe:

                self.universe[str(s.Symbol)] = s
                self.AddEquity(str(s.Symbol).split(' ')[0])

        for s in changes.RemovedSecurities:

            if str(s.Symbol) in self.universe.keys():

                self.universe.pop(str(s.Symbol))
                self.RemoveSecurity(str(s.Symbol).split(' ')[0])

    def SelectSymbols(self, symbols):
        selected = [c for c in symbols if c.HasFundamentalData]
        sorted_by_dollar_volume = sorted(selected, key=lambda c: c.DollarVolume, reverse=True)
        return [ c.Symbol for c in sorted_by_dollar_volume[:50] ]

This should resolve the issue you're experiencing in live trading. For more information on how to use AddEquity and RemoveSecurity, you can refer to the QuantConnect Documentation.

Thank you Mia. I want to use the Quantbook because I need some fundamental data (for example, the market cap and valuations ratios). How can I get the fundamental data in live mode, is that possible?

Yes, it is possible to get fundamental data without using QuantBook. In live trading, you can access fundamental data through the FineFundamental object. This object is available in the FineSelectionFunction of the AddUniverse method. Here's an example of how you can select symbols based on their market cap and valuation ratios:

from AlgorithmImports import *

class FundamentalDataAlgorithm(QCAlgorithm):

    def Initialize(self):

        self.SetTimeZone(TimeZones.London)

        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)

        self.universe = {}

    def CoarseSelectionFunction(self, coarse):
        return [c.Symbol for c in coarse if c.HasFundamentalData]

    def FineSelectionFunction(self, fine):
        sorted_by_market_cap = sorted(fine, key=lambda f: f.ValuationRatios.MarketCap, reverse=True)
        sorted_by_valuation_ratio = sorted(fine, key=lambda f: f.ValuationRatios.PERatio, reverse=True)
        return [f.Symbol for f in sorted_by_market_cap[:50]] + [f.Symbol for f in sorted_by_valuation_ratio[:50]]

    def OnData(self, data):
        pass

    def OnSecuritiesChanged(self, changes):

        for s in changes.AddedSecurities:

            if str(s.Symbol) not in self.universe:

                self.universe[str(s.Symbol)] = s
                self.AddEquity(str(s.Symbol).split(' ')[0])

        for s in changes.RemovedSecurities:

            if str(s.Symbol) in self.universe.keys():

                self.universe.pop(str(s.Symbol))
                self.RemoveSecurity(str(s.Symbol).split(' ')[0])

In this example, the FineSelectionFunction sorts the fine universe by market cap and valuation ratio, and selects the top 50 symbols for each. The selected symbols are then added to the algorithm's universe.

For more information on how to use fundamental data in your algorithms, you can refer to the QuantConnect Documentation.