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

class ExtractAlphaCrossAssetModelAlgorithm(QCAlgorithm):

    def Initialize(self) -> None:
        self.SetStartDate(2019, 1, 1)
        self.SetEndDate(2020, 1, 1)
        self.SetCash(100000)
        
        self.time = datetime.min
        
        self.AddUniverse(self.MyCoarseFilterFunction)
        self.UniverseSettings.Resolution = Resolution.Minute
        
    def MyCoarseFilterFunction(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
        sorted_by_dollar_volume = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 4], 
                                key=lambda x: x.DollarVolume, reverse=True)
        selected = [x.Symbol for x in sorted_by_dollar_volume[:100]]
        return selected

    def OnData(self, slice: Slice) -> None:
        if self.time > self.Time: return
    
        # Accessing Data
        points = slice.Get(ExtractAlphaCrossAssetModel)
        sorted_by_score = sorted([x for x in points.items() if x[1].Score], key=lambda x: x[1].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]]
        
        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)
        
        long_targets = [PortfolioTarget(symbol, 0.05) for symbol in long_symbols]
        short_targets = [PortfolioTarget(symbol, -0.05) for symbol in short_symbols]
        self.SetHoldings(long_targets + short_targets)
        
        self.time = Expiry.EndOfDay(self.Time)
        
    def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
        for security in changes.AddedSecurities:
            # Requesting Data
            extract_alpha_cross_asset_model_symbol = self.AddData(ExtractAlphaCrossAssetModel, security.Symbol).Symbol

            # Historical Data
            history = self.History(extract_alpha_cross_asset_model_symbol , 60, Resolution.Daily)
            self.Debug(f"We got {len(history)} items from our history request")
        

Example Applications

The Cross Asset Model dataset by ExtractAlpha enables you to utilize Options market information to extract alpha. Examples include the following strategies: