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
from AlgorithmImports import *
from QuantConnect.DataSource import *
class ExtractAlphaTacticalModelAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2021, 10, 10)
self.set_end_date(2023, 10, 10)
self.set_cash(100000)
# A variable to control the time of rebalance
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]:
# Select non-penny stocks with highest dollar volume due to better informed information from more market activities
# Only the ones with fundamental data are supported by tactical data
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[:100]]
return selected
def on_data(self, slice: Slice) -> None:
if self.last_time > self.time: return
# Trade only based on the updated tactical data
points = slice.Get(ExtractAlphaTacticalModel)
# Long the ones with the highest return estimates riding from tactical strategies
# Short the lowest that predicted stock price goes down
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]]
# Liquidate the ones without a strong tactical support
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)
# Invest equally and dollar-neutral to evenly dissipate capital risk and hedge systematic risk
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 tactical data for trading signal generation
extract_alpha_tactical_model_symbol = self.add_data(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:
- Optimizing entry and exit times in a portfolio construction model.
- Using the raw factor values as technical indicators.
- Inputting the data into machine learning classifier models as trend/reversal labels.
Pricing
Cloud Access
Using ExtractAlpha Tactical data in the QuantConnect Cloud for your backtesting and live trading purposes.
Download On Premise
Download Composite Factor Bundle historical records for your LEAN backtesting and live trading on premise with the LEAN CLI.
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