About Composite Factor Bundle

The Composite Factor Bundle dataset by Kavout provides ensemble scores for popular market factors. Kavout signals are machine-learning enhanced scores that capture the returns of systematic factors such as quality, value, momentum, growth, and low volatility. There are many different anomalies discovered by researchers and practitioners across these factor categories and there is no good common definition of each style across the literature. Kavout creates an ensemble score for each style that gauges the different factors considered in the literature and industry practice.

In this data set, you will find Kavout's proprietary signals for quality, value, momentum, growth, and low volatility, which have been adopted by some of the multi-billion dollar quant funds in New York and London. Each signal is generated by an ensemble model consisting of inputs from hundreds of anomalies. The data is generated on a daily basis and covers all the stocks traded in US major markets such as NYSE and Nasdaq since 2003. You could leverage this abundant set of signals to construct and backtest your strategies.

This dataset depends on the US Equity Security Master dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.


About Kavout

Kavout was created by ex-Googlers and the founding team used to work at Google, Microsoft, Baidu, and financial firms with a proven track record of building many mission-critical machine learning systems where billions of data points were processed in real-time to predict the best outcome for core search ranking, ads monetization, recommendations, and trading platforms.

Their mission is to build machine investing solutions to find alpha with adaptive learning algorithms and to create an edge by assimilating vast quantities of complex data through the latest AI and Machine Learning methods to generate signals to uncover hidden, dynamic, and nonlinear patterns in the financial markets.

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About QuantConnect

QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.


Algorithm Example

class KavoutCompositeFactorBundleAlgorithm(QCAlgorithm):

    def Initialize(self) -> None:
        self.SetStartDate(2019, 1, 1)
        self.SetEndDate(2020, 6, 1)
        self.SetCash(100000)
        
        self.time = datetime.min
        
        self.AddUniverse(self.MyCoarseFilterFunction)
        self.UniverseSettings.Resolution = Resolution.Minute
        
    def MyCoarseFilterFunction(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
        sorted_by_dollar_volume = sorted([x for x in coarse if x.HasFundamentalData], 
                                key=lambda x: x.DollarVolume, reverse=True)
        selected = [x.Symbol for x in sorted_by_dollar_volume[:100]]
        return selected

    def OnData(self, slice: Slice) -> None:
        if self.time > self.Time: return
    
        # Accessing Data
        points = slice.Get(KavoutCompositeFactorBundle)            
        sorted_by_score = sorted(points.items(), key=self.TotalScore)
        long_symbols = [x[0].Underlying for x in sorted_by_score[-10:]]
        short_symbols = [x[0].Underlying for x in sorted_by_score[:10]]
        
        for symbol in [x.Symbol for x in self.Portfolio.Values if x.Invested]:
            if symbol not in long_symbols + shor_symbols:
                self.Liquidate(symbol)
        
        long_targets = [PortfolioTarget(symbol, 0.05) for symbol in long_symbols]
        short_targets = [PortfolioTarget(symbol, -0.05) for symbol in short_symbols]
        self.SetHoldings(long_targets + short_targets)
        
        self.time = Expiry.EndOfDay(self.Time)
        
    def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
        for security in changes.AddedSecurities:
            # Requesting Data
            kavout_composite_factor_bundle_symbol= self.AddData(KavoutCompositeFactorBundle, security.Symbol).Symbol
            
            # Historical Data
            history = self.History(kavout_composite_factor_bundle_symbol, 2, Resolution.Daily)
            self.Debug(f"We got {len(history)} items from our history request")
            
    def TotalScore(self, value: Tuple[Symbol, KavoutCompositeFactorBundle]) -> float:
        '''Return the total score to integrate overall likelihood to outcompete, take equal weighting for each factor'''
        value = value[1]
        return value.Growth + value.LowVolatility + value.Momentum + value.Quality + value.ValueFactor

Example Applications

The Composite Factor Bundle dataset enables you to access the performance of 5 different factors in order to engineer strategies. Examples include the following strategies: