About Tactical

The Tactical dataset by ExtractAlpha is a stock scoring algorithm that captures the technical dynamics of individual US Equities over one to ten trading day horizons. It can assist a longer-horizon investor in timing their entry or exit points or be used in combination with existing systematic or qualitative strategies with similar holding periods.

The data covers a dynamic universe of around 4,700 US Equities per day on average, starts in January 2000, and is delivered on a daily frequency. The Tactical dataset expands upon simple reversal, liquidity, and seasonality factors to identify stocks that are likely to trend or reverse.

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 ExtractAlphaTacticalModelAlgorithm(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(ExtractAlphaTacticalModel)
        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_tactical_model_symbol = self.AddData(ExtractAlphaTacticalModel, security.Symbol).Symbol

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

Example Applications

The Tactical dataset enables you to gain insight into short-term stock dynamics for trading. Examples include the following strategies: