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.

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

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

Data Summary

The following table describes the dataset properties:

Start DateJanuary 2003
Asset Coverage8,000 US Equities
Data DensityRegular

Data Point Attributes

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

Requesting Data

To add Composite Factor Bundle data to your algorithm, call the AddData 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.symbol = self.add_equity("AAPL", Resolution.DAILY).symbol
        self.dataset_symbol = self.add_data(KavoutCompositeFactorBundle, self.symbol).symbol
namespace QuantConnect.Algorithm.CSharp.AltData
    public class KavoutCompositeFactorBundleAlgorithm: QCAlgorithm
        private Symbol _symbol, _datasetSymbol;

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

            _symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
            _datasetSymbol = AddData<KavoutCompositeFactorBundle>(_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 Get 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 History 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 RemoveSecurity method.


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.

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