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:
from QuantConnect.DataSource import *
self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_symbol = self.add_data(ExtractAlphaTacticalModel, self.aapl).symbol using QuantConnect.DataSource;
_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_datasetSymbol = AddData<ExtractAlphaTacticalModel>(_symbol).Symbol;
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.
Requesting Data
To add Tactical data to your algorithm, call the AddDataadd_data method. Save a reference to the dataset Symbol so you can access the data later in your algorithm.
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 namespace QuantConnect
{
public class ExtractAlphaTacticalModelDataAlgorithm : QCAlgorithm
{
private Symbol _symbol, _datasetSymbol;
public override void Initialize()
{
SetStartDate(2019, 1, 1);
SetEndDate(2020, 6, 1);
SetCash(100000);
_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_datasetSymbol = AddData<ExtractAlphaTacticalModel>(_symbol).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.
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}") public override void OnData(Slice slice)
{
if (slice.ContainsKey(_datasetSymbol))
{
var dataPoint = slice[_datasetSymbol];
Log($"{_datasetSymbol} score at {slice.Time}: {dataPoint.Score}");
}
}
To iterate through all of the dataset objects in the current Slice, call the Getget method.
def on_data(self, slice: Slice) -> None:
for dataset_symbol, data_point in slice.get(ExtractAlphaTacticalModel).items():
self.log(f"{dataset_symbol} score at {slice.time}: {data_point.score}") public override void OnData(Slice slice)
{
foreach (var kvp in slice.Get<ExtractAlphaTacticalModel>())
{
var datasetSymbol = kvp.Key;
var dataPoint = kvp.Value;
Log($"{datasetSymbol} score at {slice.Time}: {dataPoint.Score}");
}
}
Historical Data
To get historical Tactical data, call the Historyhistory method with the dataset Symbol. If there is no data in the period you request, the history result is empty.
# DataFrame history_df = self.history(self.dataset_symbol, 100, Resolution.DAILY) # Dataset objects history_df = self.history[ExtractAlphaTacticalModel](self.dataset_symbol, 100, Resolution.DAILY)
var history = History<ExtractAlphaTacticalModel>(_datasetSymbol, 100, Resolution.Daily);
For more information about historical data, see History Requests.
Remove Subscriptions
To remove a subscription, call the RemoveSecurityremove_security method.
self.remove_security(self.dataset_symbol)
RemoveSecurity(_datasetSymbol);
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.
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