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Kavout

Composite Factor Bundle

Introduction

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.

For more information about the Composite Factor Bundle dataset, including CLI commands and pricing, see the dataset listing.

About the Provider

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.

Getting Started

The following snippet demonstrates how to request data from the Composite Factor Bundle dataset:

from QuantConnect.DataSource import *

self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_symbol = self.add_data(KavoutCompositeFactorBundle, self.aapl).symbol
using QuantConnect.DataSource;

_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_datasetSymbol = AddData<KavoutCompositeFactorBundle>(_symbol).Symbol;

Data Summary

The following table describes the dataset properties:

PropertyValue
Start DateJanuary 2003
Asset Coverage8,000 US Equities
Data DensityRegular
ResolutionDaily
TimezoneUTC

Requesting Data

To add Composite Factor Bundle data to your algorithm, call the AddDataadd_data method. Save a reference to the dataset Symbol so you can access the data later in your algorithm.

class KavoutCompositeFactorBundleAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)

        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
        self.dataset_symbol = self.add_data(KavoutCompositeFactorBundle, self.aapl).symbol
public class KavoutCompositeFactorBundleAlgorithm : QCAlgorithm
{
    private Symbol _symbol, _datasetSymbol;

    public override void Initialize()
    {
        SetStartDate(2019, 1, 1);
        SetEndDate(2020, 6, 1);
        SetCash(100000);

        _symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
        _datasetSymbol = AddData & lt; KavoutCompositeFactorBundle & gt; (_symbol).Symbol;
    }
}

Accessing Data

To get the current Composite Factor Bundle data, index the current Slice with the dataset Symbol. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your dataset at every time step. To avoid issues, check if the Slice contains the data you want before you index it.

def on_data(self, slice: Slice) -> None:
    if slice.contains_key(self.dataset_symbol):
        data_point = slice[self.dataset_symbol]
        self.log(f"{self.dataset_symbol} momentum at {slice.time}: {data_point.momentum}")
public override void OnData(Slice slice)
{
    if (slice.ContainsKey(_datasetSymbol))
    {
        var dataPoint = slice[_datasetSymbol];
        Log($"{_datasetSymbol} momentum at {slice.Time}: {dataPoint.Momentum}");
    }
}

To iterate through all of the dataset objects in the current Slice, call the Getget method.

def on_data(self, slice: Slice) -> None:
    for dataset_symbol, data_point in slice.get(KavoutCompositeFactorBundle).items():
        self.log(f"{dataset_symbol} momentum at {slice.time}: {data_point.momentum}")
public override void OnData(Slice slice)
{
    foreach (var kvp in slice.Get<KavoutCompositeFactorBundle>())
    {
        var datasetSymbol = kvp.Key;
        var dataPoint = kvp.Value;
        Log($"{datasetSymbol} momentum at {slice.Time}: {dataPoint.Momentum}");
    }
}

Historical Data

To get historical Composite Factor Bundle data, call the Historyhistory method with the dataset Symbol. If there is no data in the period you request, the history result is empty.

# DataFrame
history_df = self.history(self.dataset_symbol, 100, Resolution.DAILY)

# Dataset objects
history_bars = self.history[KavoutCompositeFactorBundle](self.dataset_symbol, 100, Resolution.DAILY)
var history = History<KavoutCompositeFactorBundle>(_datasetSymbol, 100, Resolution.Daily);

For more information about historical data, see History Requests.

Remove Subscriptions

To remove a subscription, call the RemoveSecurityremove_security method.

self.remove_security(self.dataset_symbol)
RemoveSecurity(_datasetSymbol);

If you subscribe to Composite Factor Bundle data for assets in a dynamic universe, remove the dataset subscription when the asset leaves your universe. To view a common design pattern, see Track Security Changes.

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:

  • Performing return-risk optimization based on performance and volatility scoring.
  • Weighing stocks based on regression analysis in factor-vector space.

Classic Algorithm Example

The following example algorithm creates a dynamic universe of the 100 most liquid US Equities. Each day, it then forms a equal-weighted dollar-neutral portfolio of the 10 companies most likely to outperform and the 10 companies most likely to underperform.

from AlgorithmImports import *
from QuantConnect.DataSource import *

class KavoutCompositeFactorBundleAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)
        # A variable that control the time of rebalancing
        self.last_time = datetime.min
        
        self.add_universe(self.my_coarse_filter_function)
        self.universe_settings.resolution = Resolution.MINUTE
        
    def my_coarse_filter_function(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
        # Filter for the highly traded stocks for more informed data from frequent market activities, which may translate to more accurate prediction
        # Factors scores are only available for the ones with fundamentals
        sorted_by_dollar_volume = sorted([x for x in coarse if x.has_fundamental_data], 
                                key=lambda x: x.dollar_volume, reverse=True)
        selected = [x.symbol for x in sorted_by_dollar_volume[:100]]
        return selected

    def on_data(self, slice: Slice) -> None:
        if self.last_time > self.time: return
    
        # Trade only on the factor score data
        points = slice.Get(KavoutCompositeFactorBundle)

        # Long the stocks with highest factor scores, which indicate higher return from various factors
        # Short the ones with lowest factor scores for lower return estimates
        sorted_by_score = sorted(points.items(), key=self.total_score)
        long_symbols = [x[0].underlying for x in sorted_by_score[-10:]]
        short_symbols = [x[0].underlying for x in sorted_by_score[:10]]
        
        # Liquidate the stocks with less significant return estimation for better PnL
        for symbol in [x.symbol for x in self.portfolio.Values if x.invested]:
            if symbol not in long_symbols + short_symbols:
                self.liquidate(symbol)
        
        # Invest in equal-size and dollar-neutral to evenly dissipate individual capital risk, avoid non-systematic risk, and better margin
        long_targets = [PortfolioTarget(symbol, 0.05) for symbol in long_symbols]
        short_targets = [PortfolioTarget(symbol, -0.05) for symbol in short_symbols]
        self.set_holdings(long_targets + short_targets)
        
        self.last_time = Expiry.END_OF_DAY(self.time)
        
    def on_securities_changed(self, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            # Requesting factor bundle data for trade signal generation
            kavout_composite_factor_bundle_symbol= self.add_data(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 total_score(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.low_volatility + value.momentum + value.quality + value.value_factor
public class KavoutCompositeFactorBundleAlgorithm : QCAlgorithm
{
      // A variable that control the time of rebalancing
    private DateTime _time = DateTime.MinValue;
    
    public override void Initialize()
    {
        SetStartDate(2003, 1, 10);
        SetEndDate(2003, 1, 15);
        SetCash(100000);
        
        AddUniverse(MyCoarseFilterFunction);
        UniverseSettings.Resolution = Resolution.Minute;
    }
    
    private IEnumerable<Symbol> MyCoarseFilterFunction(IEnumerable<CoarseFundamental> coarse)
    {
                    // Filter for the highly traded stocks for more informed data from frequent market activities, which may translate to more accurate prediction
                    // Factors scores are only available for the ones with fundamentals
        return (from c in coarse
                where c.HasFundamentalData
                orderby c.DollarVolume descending
                select c.Symbol).Take(100);
    }
    
    public override void OnData(Slice slice)
    {
        if (_time > Time) return;
        
        // Trade only on the factor score data
        var points = slice.Get<KavoutCompositeFactorBundle>();

        // Long the stocks with highest factor scores, which indicate higher return from various factors
                    // Short the ones with lowest factor scores for lower return estimates
        var sortedByScore = from s in points.Values
                        orderby TotalScore(s) descending
                        select s.Symbol.Underlying;
        var longSymbols = sortedByScore.Take(10).ToList();
        var shortSymbols = sortedByScore.TakeLast(10).ToList();

        // Liquidate the stocks with less significant return estimation for better PnL
        foreach (var kvp in Portfolio)
        {
            var symbol = kvp.Key;
            if (kvp.Value.Invested && 
            !longSymbols.Contains(symbol) && 
            !shortSymbols.Contains(symbol))
            {
                Liquidate(symbol);
            }
        }

        // Invest in equal-size and dollar-neutral to evenly dissipate individual capital risk, avoid non-systematic risk, and better margin
        var targets = new List<PortfolioTarget>();
        targets.AddRange(longSymbols.Select(symbol => new PortfolioTarget(symbol, 0.05m)));
        targets.AddRange(shortSymbols.Select(symbol => new PortfolioTarget(symbol, -0.05m)));
        
        SetHoldings(targets);
        
        _time = Expiry.EndOfDay(Time);
    }
    
    public override void OnSecuritiesChanged(SecurityChanges changes)
    {
        foreach(var security in changes.AddedSecurities)
        {
            // Requesting factor bundle data for trade signal generation
            var kavoutCompositeFactorBundleSymbol = AddData<KavoutCompositeFactorBundle>(security.Symbol).Symbol;
            
            // Historical Data
            var history = History(new[]{kavoutCompositeFactorBundleSymbol}, 60, Resolution.Daily);
            Debug($"We got {history.Count()} items from our history request");
        }
    }
    
    private decimal TotalScore(KavoutCompositeFactorBundle value)
    {
        // Return the total score to integrate overall likelihood to outcompete, take equal weighting for each factor
        return value.Growth + value.ValueFactor + value.Quality + value.Momentum + value.LowVolatility;
    }
}

Framework Algorithm Example

The following example algorithm creates a dynamic universe of the 100 most liquid US Equities. Each day, it then forms a equal-weighted dollar-neutral portfolio of the 10 companies most likely to outperform and the 10 companies most likely to underperform.

from AlgorithmImports import *
from QuantConnect.DataSource import *

class KavoutCompositeFactorBundleAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)
        
        self.add_universe(self.my_coarse_filter_function)
        self.universe_settings.resolution = Resolution.MINUTE
        
        # Custom alpha model to emit insights based on factor bundle data
        self.add_alpha(KavoutCompositeFactorBundleAlphaModel())

        # Invest in equal-size and dollar-neutral to evenly dissipate individual capital risk, avoid non-systematic risk, and better margin
        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
        
        self.set_execution(ImmediateExecutionModel())
        
    def my_coarse_filter_function(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
        # Filter for the highly traded stocks for more informed data from frequent market activities, which may translate to more accurate prediction
        # Factors scores are only available for the ones with fundamentals
        sorted_by_dollar_volume = sorted([x for x in coarse if x.has_fundamental_data], 
                                key=lambda x: x.dollar_volume, reverse=True)
        selected = [x.symbol for x in sorted_by_dollar_volume[:100]]
        return selected
        
class KavoutCompositeFactorBundleAlphaModel(AlphaModel):
    
    def __init__(self) -> None:
        # A variable that control the time of rebalancing
        self.last_time = datetime.min
        
    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
        if self.last_time > algorithm.time: return []
        
        # Trade only on the factor score data
        points = slice.Get(KavoutCompositeFactorBundle)
        for kvp in points:
            algorithm.log(f"Symbol: {kvp.Key} - Growth:{kvp.Value.growth} - Low Volatility: {kvp.Value.low_volatility} - Momentum: {kvp.Value.momentum}"
                        f" - Quality: {kvp.Value.quality} - Value Factor: {kvp.Value.value_factor}")
        
        # Long the stocks with highest factor scores, which indicate higher return from various factors
        # Short the ones with lowest factor scores for lower return estimates
        sorted_by_score = sorted(points.items(), key=self.total_score)
        long_symbols = [x[0].underlying for x in sorted_by_score[-10:]]
        short_symbols = [x[0].underlying for x in sorted_by_score[:10]]
        
        insights = []
        for symbol in long_symbols:
            insights.append(Insight.price(symbol, Expiry.END_OF_DAY, InsightDirection.UP))
        for symbol in short_symbols:
            insights.append(Insight.price(symbol, Expiry.END_OF_DAY, InsightDirection.DOWN))
        
        self.last_time = Expiry.END_OF_DAY(algorithm.time)
        
        return insights
        
    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            # Requesting factor bundle data for trade signal generation
            kavout_composite_factor_bundle_symbol = algorithm.add_data(KavoutCompositeFactorBundle, security.symbol).symbol
            
            # Historical Data
            history = algorithm.history(kavout_composite_factor_bundle_symbol, 2, Resolution.DAILY)
            algorithm.debug(f"We got {len(history)} items from our history request")
            
    def total_score(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.low_volatility + value.momentum + value.quality + value.value_factor
public class KavoutCompositeFactorBundleAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2003, 1, 10);
        SetEndDate(2003, 1, 15);
        SetCash(100000);
        
        AddUniverse(MyCoarseFilterFunction);
        UniverseSettings.Resolution = Resolution.Minute;

        // Custom alpha model to emit insights based on factor bundle data
        AddAlpha(new KavoutCompositeFactorBundleAlphaModel());

        // Invest in equal-size and dollar-neutral to evenly dissipate individual capital risk, avoid non-systematic risk, and better margin
        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
        
        SetExecution(new ImmediateExecutionModel());
    }
    
    private IEnumerable<Symbol> MyCoarseFilterFunction(IEnumerable<CoarseFundamental> coarse)
    {
                    // Filter for the highly traded stocks for more informed data from frequent market activities, which may translate to more accurate prediction
                    // Factors scores are only available for the ones with fundamentals
        return (from c in coarse
                where c.HasFundamentalData
                orderby c.DollarVolume descending
                select c.Symbol).Take(100);
    }
}

public class KavoutCompositeFactorBundleAlphaModel: AlphaModel
{
            // A variable that control the time of rebalancing
    public DateTime _time;
    
    public KavoutCompositeFactorBundleAlphaModel()
    {
        _time = DateTime.MinValue;
    }
    
    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
    {
        if (_time > algorithm.Time) return new List<Insight>();
        
        // Trade only on the factor score data
        var points = slice.Get<KavoutCompositeFactorBundle>();
        foreach(var kvp in points)
        {
            algorithm.Log(@"Symbol: {kvp.Key} - Growth:{kvp.Value.Growth} - Low Volatility: {kvp.Value.LowVolatility} - Momentum: {kvp.Value.Momentum}
                - Quality: {kvp.Value.Quality} - Value Factor: {kvp.Value.ValueFactor}");
        }

        // Long the stocks with highest factor scores, which indicate higher return from various factors
                    // Short the ones with lowest factor scores for lower return estimates
        var sortedByScore = from s in points.Values
                        orderby TotalScore(s) descending
                        select s.Symbol.Underlying;
        var longSymbols = sortedByScore.Take(10).ToList();
        var shortSymbols = sortedByScore.TakeLast(10).ToList();
        
        var insights = new List<Insight>();
        insights.AddRange(longSymbols.Select(symbol => new Insight(symbol, Expiry.EndOfDay, InsightType.Price, InsightDirection.Up)));
        insights.AddRange(shortSymbols.Select(symbol => new Insight(symbol, Expiry.EndOfDay, InsightType.Price, InsightDirection.Down)));
        
        _time = Expiry.EndOfDay(algorithm.Time);
        
        return insights;
    }
    
    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
    {
        foreach(var security in changes.AddedSecurities)
        {
            // Requesting factor bundle data for trade signal generation
            var kavoutCompositeFactorBundleSymbol = algorithm.AddData<KavoutCompositeFactorBundle>(security.Symbol).Symbol;
            
            // Historical Data
            var history = algorithm.History(new[]{kavoutCompositeFactorBundleSymbol}, 60, Resolution.Daily);
            algorithm.Debug($"We got {history.Count()} items from our history request");
        }
    }
    
    private decimal TotalScore(KavoutCompositeFactorBundle value)
    {
        // Return the total score to integrate overall likelihood to outcompete, take equal weighting for each factor
        return value.Growth + value.ValueFactor + value.Quality + value.Momentum + value.LowVolatility;
    }
}

Data Point Attributes

The Composite Factor Bundle dataset provides KavoutCompositeFactorBundle objects, which have the following attributes:

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