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.
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
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
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.
Pricing
Cloud Access
Using Kavout Composite Factor Bundle data in the QuantConnect Cloud for your backtesting and live trading purposes.
Download On Premise
Download Composite Factor Bundle historical records for your LEAN backtesting and live trading on premise with the LEAN CLI.
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