About Estimize

The Estimize dataset by ExtractAlpha estimates the financials of companies, including EPS, revenues, industry-specific KPIs, macroeconomic indicators, and more. The data covers over 2,800 US-listed Equities’ EPS/Revenue, over 200 company KPIs, 27 US and 55 international macroeconomic indicator datasets, and more. The data starts in January 2011 and is delivered on a daily frequency. This dataset is crowdsourced from a community of 100,000+ contributors via the data provider’s web platform.

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 ExtractAlphaEstimizeAlgorithm(QCAlgorithm):

    def Initialize(self) -> None:
        self.SetStartDate(2019, 1, 1)
        self.SetEndDate(2020, 12, 31)
        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[:500]]
        return selected

    def OnData(self, slice: Slice) -> None:
        if self.time > self.Time: return
    
        # Accessing Data
        consensus = slice.Get(EstimizeConsensus)
        estimate = slice.Get(EstimizeEstimate)
        release = slice.Get(EstimizeRelease)
        
        if not estimate: return
        
        sorted_by_eps_estimate = sorted([x for x in estimate.items() if x[1].Eps], key=lambda x: x[1].Eps)
        long_symbols = [x[0].Underlying for x in sorted_by_eps_estimate[-10:]]
        short_symbols = [x[0].Underlying for x in sorted_by_eps_estimate[: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.EndOfMonth(self.Time)
        
    def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
        for security in changes.AddedSecurities:
            # Requesting Data
            estimize_consensus_symbol = self.AddData(EstimizeConsensus, security.Symbol).Symbol
            estimize_estimate_symbol = self.AddData(EstimizeEstimate, security.Symbol).Symbol
            estimize_release_symbol = self.AddData(EstimizeRelease, security.Symbol).Symbol

            # Historical Data
            history = self.History([estimize_consensus_symbol,
                                    estimize_estimate_symbol,
                                    estimize_release_symbol
                                    ], 10, Resolution.Daily)
            self.Debug(f"We got {len(history)} items from our history request")

Example Applications

The Estimize dataset enables you to estimate the financial data of a company more accurately for alpha. Examples include the following use cases: