ExtractAlpha
Estimize
Introduction
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.
For more information about the Estimize dataset, including CLI commands and pricing, see the dataset listing.
About the Provider
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.
Getting Started
The following snippet demonstrates how to request data from the Estimize dataset:
from QuantConnect.DataSource import *
self.symbol = self.AddEquity("AAPL", Resolution.Daily).Symbol
self.estimize_consensus_symbol = self.AddData(EstimizeConsensus, self.symbol).Symbol
self.estimize_estimate_symbol = self.AddData(EstimizeEstimate, self.symbol).Symbol
self.estimize_release_symbol = self.AddData(EstimizeRelease, self.symbol).Symbol
using QuantConnect.DataSource;
_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_estimizeConsensusSymbol = AddData<EstimizeConsensus>(_symbol).Symbol;
_estimizeEstimateSymbol = AddData<EstimizeEstimate>(_symbol).Symbol;
_estimizeReleaseSymbol = AddData<EstimizeRelease>(_symbol).Symbol;
Data Point Attributes
The Estimize dataset provides EstimizeConsensus, EstimizeEstimate, and EstimizeRelease objects.
EstimizeConsensus Attributes
EstimizeConsensus objects have the following attributes:
EstimizeEstimate Attributes
EstimizeEstimate objects have the following attributes:
EstimizeRelease Attributes
EstimizeRelease objects have the following attributes:
Requesting Data
To add Estimize data to your algorithm, call the AddData method. Save a reference to the dataset Symbol so you can access the data later in your algorithm.
class ExtractAlphaEstimizeDataAlgorithm(QCAlgorithm):
def Initialize(self) -> None:
self.SetStartDate(2019, 1, 1)
self.SetEndDate(2020, 6, 1)
self.SetCash(100000)
self.symbol = self.AddEquity("AAPL", Resolution.Daily).Symbol
self.estimize_consensus_symbol = self.AddData(EstimizeConsensus, self.symbol).Symbol
self.estimize_estimate_symbol = self.AddData(EstimizeEstimate, self.symbol).Symbol
self.estimize_release_symbol = self.AddData(EstimizeRelease, self.symbol).Symbol namespace QuantConnect
{
public class ExtractAlphaEstimizeDataAlgorithm : QCAlgorithm
{
private Symbol _symbol, _estimizeConsensusSymbol, _estimizeEstimateSymbol, _estimizeReleaseSymbol;
public override void Initialize()
{
SetStartDate(2019, 1, 1);
SetEndDate(2020, 6, 1);
SetCash(100000);
_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_estimizeConsensusSymbol = AddData<EstimizeConsensus>(_symbol).Symbol;
_estimizeEstimateSymbol = AddData<EstimizeEstimate>(_symbol).Symbol;
_estimizeReleaseSymbol = AddData<EstimizeRelease>(_symbol).Symbol;
}
}
}
Accessing Data
To get the current Estimize data, index the current Slice with the dataset Symbol. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your dataset at every time step. To avoid issues, check if the Slice contains the data you want before you index it.
def OnData(self, slice: Slice) -> None:
if slice.ContainsKey(self.estimize_consensus_symbol):
data_point = slice[self.estimize_consensus_symbol]
self.Log(f"{self.estimize_consensus_symbol} mean at {slice.Time}: {data_point.Mean}")
if slice.ContainsKey(self.estimize_estimate_symbol):
data_point = slice[self.estimize_estimate_symbol]
self.Log(f"{self.estimize_estimate_symbol} EPS at {slice.Time}: {data_point.Eps}")
if slice.ContainsKey(self.estimize_release_symbol):
data_point = slice[self.estimize_release_symbol]
self.Log(f"{self.estimize_release_symbol} EPS at {slice.Time}: {data_point.Eps}") public override void OnData(Slice slice)
{
if (slice.ContainsKey(_estimizeConsensusSymbol))
{
var dataPoint = slice[_estimizeConsensusSymbol];
Log($"{_estimizeConsensusSymbol} mean at {slice.Time}: {dataPoint.Mean}");
}
if (slice.ContainsKey(_estimizeEstimateSymbol))
{
var dataPoint = slice[_estimizeEstimateSymbol];
Log($"{_estimizeEstimateSymbol} EPS at {slice.Time}: {dataPoint.Eps}");
}
if (slice.ContainsKey(_estimizeReleaseSymbol))
{
var dataPoint = slice[_estimizeReleaseSymbol];
Log($"{_estimizeReleaseSymbol} EPS at {slice.Time}: {dataPoint.Eps}");
}
}
To iterate through all of the dataset objects in the current Slice, call the Get method.
def OnData(self, slice: Slice) -> None:
for dataset_symbol, data_point in slice.Get(EstimizeConsensus).items():
self.Log(f"{dataset_symbol} mean at {slice.Time}: {data_point.Mentions}")
for dataset_symbol, data_point in slice.Get(EstimizeEstimate).items():
self.Log(f"{dataset_symbol} EPS at {slice.Time}: {data_point.Eps}")
for dataset_symbol, data_point in slice.Get(EstimizeRelease).items():
self.Log(f"{dataset_symbol} EPS at {slice.Time}: {data_point.Eps}")
public override void OnData(Slice slice)
{
foreach (var kvp in slice.Get<EstimizeConsensus>())
{
var datasetSymbol = kvp.Key;
var dataPoint = kvp.Value;
Log($"{datasetSymbol} mean at {slice.Time}: {dataPoint.Mentions}");
}
foreach (var kvp in slice.Get<EstimizeEstimate>())
{
var datasetSymbol = kvp.Key;
var dataPoint = kvp.Value;
Log($"{datasetSymbol} EPS at {slice.Time}: {dataPoint.Eps}");
}
foreach (var kvp in slice.Get<EstimizeRelease>())
{
var datasetSymbol = kvp.Key;
var dataPoint = kvp.Value;
Log($"{datasetSymbol} EPS at {slice.Time}: {dataPoint.Eps}");
}
}
Historical Data
To get historical Estimize data, call the History method with the dataset Symbol. If there is no data in the period you request, the history result is empty.
# DataFrames
consensus_history_df = self.History(self.estimize_consensus_symbol, 100, Resolution.Daily)
estimate_history_df = self.History(self.estimize_estimate_symbol, 100, Resolution.Daily)
release_history_df = self.History(self.estimize_release_symbol, 100, Resolution.Daily)
history_df = self.History([
self.estimize_consensus_symbol,
self.estimize_estimate_symbol,
self.estimize_release_symbol], 100, Resolution.Daily)
# Dataset objects
consensus_history_bars = self.History[EstimizeConsensus](self.estimize_consensus_symbol, 100, Resolution.Daily)
estimate_history_bars = self.History[EstimizeEstimate](self.estimize_estimate_symbol, 100, Resolution.Daily)
release_history_bars = self.History[EstimizeRelease](self.estimize_release_symbol, 100, Resolution.Daily) // Dataset objects
var concensusHistory = History<EstimizeConsensus>(_estimizeConsensusSymbol, 100, Resolution.Daily);
var estimateHistory = History<EstimizeEstimate>(_estimizeEstimateSymbol, 100, Resolution.Daily);
var releaseHistory = History<EstimizeRelease>(_estimizeReleaseSymbol, 100, Resolution.Daily);
// Slice objects
var history = History(new[]{_estimizeConsensusSymbol,
_estimizeEstimateSymbol,
_estimizeReleaseSymbol}, 10, Resolution.Daily);
For more information about historical data, see History Requests.
Remove Subscriptions
To remove a subscription, call the RemoveSecurity method.
self.RemoveSecurity(self.estimize_consensus_symbol) self.RemoveSecurity(self.estimize_estimate_symbol) self.RemoveSecurity(self.estimize_release_symbol)
RemoveSecurity(_estimizeConsensusSymbol); RemoveSecurity(_estimizeEstimateSymbol); RemoveSecurity(_estimizeReleaseSymbol);
If you subscribe to Estimize data for assets in a dynamic universe, remove the dataset subscription when the asset leaves your universe. To view a common design pattern, see Track Security Changes.
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:
- Fundamental estimates for ML regression/classification models
- Arbitrage/Sentiment trading on market “surprise” from ordinary expectations based on the better expectation by the dataset
- Using industry-specific KPIs to predict the returns of individual sectors
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