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ExtractAlpha

Tactical

Introduction

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.

For more information about the Tactical 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 Tactical dataset:

Select Language: 
from QuantConnect.DataSource import *

self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_symbol = self.add_data(ExtractAlphaTacticalModel, self.aapl).symbol

Data Summary

The following table describes the dataset properties:

PropertyValue
Start DateJanuary 2000
Asset Coverage5,000 US Equities
Data DensitySparse
ResolutionDaily
TimezoneUTC

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.

For more example algorithms, see Examples.

Data Point Attributes

The Tactical dataset provides ExtractAlphaTacticalModel objects, which have the following attributes:

ExtractAlphaTacticalModel Select Language:
    The stock's likelihood to revert to the mean. This value is the percentile rank bounded between [1, 100]. The closer the value is to the upper bound, the more likely the stock will outperform according to this component.
  • reversal: Int32
    • Cross-asset momentum percentile rank based on one of the stock's factors (e.g. industry, value/growth stock classification, etc.) This value is the percentile rank bounded between [1, 100]. The closer the value is to the upper bound, the more likely the stock will outperform according to this component.
  • factor_momentum: Int32
    • Short-term stock liquidity shock score. This value is the percentile rank bounded between [1, 100]. The closer this value is to the upper bound, the more likely the stock will outperform according to this component.
  • liquidity_shock: Int32
    • Seasonality score of cross-section stock returns. This value is the percentile rank bounded between [1, 100]. The closer this value is to the upper bound, the more likely the stock will outperform according to this component.
  • seasonality: Int32
    • Composite model score of all defined scores: Reversal, FactorMomentum, LiquidityShock, and Seasonality. This value is the percentile rank bounded between [1, 100]. The closer this value is to the upper bound, the more likely the stock will outperform according to this component.
  • score: Int32
    • The time that the data became available to the algorithm
  • end_time: DateTime
    • Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.
  • data_type: MarketDataType
    • True if this is a fill forward piece of data
  • is_fill_forward: bool
    • Current time marker of this data packet.
  • time: DateTime
    • Symbol representation for underlying Security
  • symbol: Symbol
    • Value representation of this data packet. All data requires a representative value for this moment in time. For streams of data this is the price now, for OHLC packets this is the closing price.
  • value: decimal
    • As this is a backtesting platform we'll provide an alias of value as price.
  • price: decimal

Requesting Data

To add Tactical data to your algorithm, call the add_data method. Save a reference to the dataset Symbol so you can access the data later in your algorithm.

Select Language: 
class ExtractAlphaTacticalModelDataAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)

        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
        self.dataset_symbol = self.add_data(ExtractAlphaTacticalModel, self.aapl).symbol

Accessing Data

To get the current Tactical 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.

Select Language: 
def on_data(self, slice: Slice) -> None:
    if slice.contains_key(self.dataset_symbol):
        data_point = slice[self.dataset_symbol]
        self.log(f"{self.dataset_symbol} score at {slice.time}: {data_point.score}")

To iterate through all of the dataset objects in the current Slice, call the get method.

Select Language: 
def on_data(self, slice: Slice) -> None:
    for dataset_symbol, data_point in slice.get(ExtractAlphaTacticalModel).items():
        self.log(f"{dataset_symbol} score at {slice.time}: {data_point.score}")

Historical Data

To get historical Tactical data, call the history method with the dataset Symbol. If there is no data in the period you request, the history result is empty.

Select Language: 
# DataFrame
history_df = self.history(self.dataset_symbol, 100, Resolution.DAILY)

# Dataset objects
history_df = self.history[ExtractAlphaTacticalModel](self.dataset_symbol, 100, Resolution.DAILY)

For more information about historical data, see History Requests.

Remove Subscriptions

To remove a subscription, call the remove_security method.

Select Language: 
self.remove_security(self.dataset_symbol)

If you subscribe to Tactical 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 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.

For more example algorithms, see Examples.

You can also see our Videos. You can also get in touch with us via Discord.

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