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.

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;

The following table describes the dataset properties:

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.

For more example algorithms, see Examples.

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

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

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);
            SetCash(100000);

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

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}");
    }
}

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.

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.

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.

For more example algorithms, see Examples.