About Estimize
The Estimize dataset by ExtractAlpha estimates the financials of companies, including EPS, and revenues. The data covers over 2,800 US-listed Equities’ EPS/Revenue. The data starts in January 2011 and is updated on a daily frequency. The data is sparse, and it doesn't have new updates every day. 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
from AlgorithmImports import *
from QuantConnect.DataSource import *
class ExtractAlphaEstimizeAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2019, 1, 1)
self.set_end_date(2020, 12, 31)
self.set_cash(100000)
self.last_time = datetime.min
self.add_universe(self.my_coarse_filter_function)
self.universe_settings.resolution = Resolution.MINUTE
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[:500]]
return selected
def on_data(self, slice: Slice) -> None:
if self.last_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.set_holdings(long_targets + short_targets)
self.last_time = Expiry.END_OF_MONTH(self.time)
def on_securities_changed(self, changes: SecurityChanges) -> None:
for security in changes.added_securities:
# Requesting Data
estimize_consensus_symbol = self.add_data(EstimizeConsensus, security.symbol).symbol
estimize_estimate_symbol = self.add_data(EstimizeEstimate, security.symbol).symbol
estimize_release_symbol = self.add_data(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:
- 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
Pricing
Cloud Access
Using ExtractAlpha Estimize data in the QuantConnect Cloud for your backtesting and live trading purposes.
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