About Cross Asset Model
The Cross Asset Model by ExtractAlpha provides stock scoring values based on the trading activity in the Options market. Since the Options market has a higher proportion of institutional traders than the Equities market, the Options market is composed of investors who are more informed and information-driven on average. The data covers a dynamic universe of over 3,000 US Equities, starts in July 2005, and is delivered on a daily frequency. This dataset is created by feature engineering on the Options market put-call spread, volatility skewness, and volume.
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 ExtractAlpha
ExtractAlpha was founded by Vinesh Jha in 2013 with the goal of providing alternative data for investors. ExtractAlpha's rigorously researched data sets and quantitative stock selection models leverage unique sources and analytical techniques, allowing users to gain an investment edge.
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 ExtractAlphaCrossAssetModelAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2019, 1, 1)
self.set_end_date(2019, 12, 31)
self.set_cash(100000)
# A variable to control the rebalance time
self.last_time = datetime.min
self.add_universe(self.my_coarse_filter_function)
self.dataset_symbol_by_symbol = {}
self.points = {}
def my_coarse_filter_function(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
# Select non-penny stocks with highest dollar volume due to better informed information from more market activities
# Only the ones with fundamental data are supported by cross asset model data
sorted_by_dollar_volume = sorted([x for x in coarse if x.has_fundamental_data and x.price > 4],
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 based on corss asset model data
points = slice.Get(ExtractAlphaCrossAssetModel)
if points:
self.points = points
# Avoid too frequent trades
if slice.time.time() < time(10): return
# Long the ones with the highest return estimates based on option trade data
# Short the lowest return ones
sorted_by_score = sorted([x for x in self.points.items() if x[1].score != None],
key=lambda x: x[1].score, reverse=True)
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 ones without a strong trading signal
# Invest equally and dollar-neutral to evenly dissipate capital risk and hedge systematic risk
portfolio_targets = []
for symbol, security_holding in self.portfolio.items():
weight = 0
if symbol in long_symbols:
weight = 0.05
elif symbol in short_symbols:
weight = -0.05
elif not security_holding.invested:
continue
portfolio_targets.append(PortfolioTarget(symbol, weight))
self.set_holdings(portfolio_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 cross asset model data for trading signal generation
self.dataset_symbol_by_symbol[security.symbol] = self.add_data(ExtractAlphaCrossAssetModel, security.symbol).symbol
for security in changes.removed_securities:
dataset_symbol = self.dataset_symbol_by_symbol.pop(security.symbol, None)
if dataset_symbol:
self.remove_security(dataset_symbol)
Example Applications
The Cross Asset Model dataset by ExtractAlpha enables you to utilize Options market information to extract alpha. Examples include the following strategies:
- Predicting price and volatility changes in Equities.
- Signaling arbitrage opportunities between Options and underlying assets.
- Using it as a stock selection indicator.
Pricing
Cloud Access
Using ExtractAlpha Cross Asset Model 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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Data Link
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