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)
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]:
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
# Accessing Data
points = slice.Get(ExtractAlphaCrossAssetModel)
if points:
self.points = points
if slice.time.time() < time(10): return
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:]]
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 data
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.
Explore Other Datasets
US Futures
Dataset by AlgoSeek
Coinbase Crypto Price Data
Dataset by CoinAPI
US Futures Security Master
Dataset by QuantConnect
