About True Beats
The True Beats dataset by ExtractAlpha provides quantitative predictions of EPS and Revenues for US Equities. The data covers a dynamic universe of around 4,000-5,000 US-listed Equities on a daily average. The data starts in January 2000 and is delivered on a daily frequency. This dataset is created by incorporating the opinions of expert analysts, historical earnings, revenue trends for the company and its peers, and metrics on company earnings management.
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 ExtractAlphaTrueBeatsDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2019, 1, 1)
self.set_end_date(2020, 1, 1)
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],
key=lambda x: x.dollar_volume, reverse=True)
selected = [x.symbol for x in sorted_by_dollar_volume[:100]]
return selected
def on_data(self, slice: Slice) -> None:
if self.time > self.time: return
# Retrieve Data
points = slice.Get(ExtractAlphaTrueBeats)
if not points: return
true_beats = {point.Key: trueBeat for point in points for trueBeat in point.Value}
sorted_by_true_beat = sorted(true_beats.items(), key=lambda x: x[1].true_beat)
long_symbols = [x[0].underlying for x in sorted_by_true_beat[-10:]]
short_symbols = [x[0].underlying for x in sorted_by_true_beat[:10]]
for symbol in self.portfolio.Keys:
if self.portfolio[symbol].invested \
and symbol not in long_symbols \
and symbol not in 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_DAY(self.time)
def on_securities_changed(self, changes: SecurityChanges) -> None:
for security in changes.added_securities:
# Requesting data
extract_alpha_true_beats_symbol = self.add_data(ExtractAlphaTrueBeats, security.symbol).symbol
# Historical Data
history = self.history(extract_alpha_true_beats_symbol, 10, Resolution.DAILY)
self.log(f"We got {len(history)} items from our history request for {security.symbol} ExtractAlpha True Beats data")
Example Applications
The True Beats dataset enables you to predict EPS and revenue of US-listed Equities for trading. Examples include the following strategies:
- Finding surprise in EPS or revenue for sentiment/arbitrage trading
- Stock or sector selections based on EPS or revenue predictions
- Calculate expected return by valuation models based on EPS or revenue predictions (e.g. Black-Litterman)
Pricing
Cloud Access
Using ExtractAlpha Tactical data in the QuantConnect Cloud for your backtesting and live trading purposes.
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