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;
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 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.
Classic Algorithm Example
The following example algorithm creates a dynamic universe of the 100 most liquid US Equities. Each day, it then forms an equal-weighted dollar-neutral portfolio of the 10 companies that are most likely to outperform and the 10 that are most likely to underperform.
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")
public class ExtractAlphaTacticalModelAlgorithm : QCAlgorithm
{
// A variable to control the time of rebalance
private DateTime _time = DateTime.MinValue;
public override void Initialize()
{
SetStartDate(2021, 10, 10);
SetEndDate(2023, 10, 10);
SetCash(100000);
AddUniverse(MyCoarseFilterFunction);
UniverseSettings.Resolution = Resolution.Minute;
}
private IEnumerable<Symbol> MyCoarseFilterFunction(IEnumerable<CoarseFundamental> coarse)
{
// 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
return (from c in coarse
where c.HasFundamentalData && c.Price > 4
orderby c.DollarVolume descending
select c.Symbol).Take(100);
}
public override void OnData(Slice slice)
{
if (_time > Time) return;
// Trade only based on the updated tactical data
var 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
var sortedByScore = from s in points.Values
where (s.Score != None)
orderby s.Score descending
select s.Symbol.Underlying;
var longSymbols = sortedByScore.Take(10).ToList();
var shortSymbols = sortedByScore.TakeLast(10).ToList();
// Liquidate the ones without a strong tactical support
foreach (var kvp in Portfolio)
{
var symbol = kvp.Key;
if (kvp.Value.Invested &&
!longSymbols.Contains(symbol) &&
!shortSymbols.Contains(symbol))
{
Liquidate(symbol);
}
}
// Invest equally and dollar-neutral to evenly dissipate capital risk and hedge systematic risk
var targets = new List<PortfolioTarget>();
targets.AddRange(longSymbols.Select(symbol => new PortfolioTarget(symbol, 0.05m)));
targets.AddRange(shortSymbols.Select(symbol => new PortfolioTarget(symbol, -0.05m)));
SetHoldings(targets);
_time = Expiry.EndOfDay(Time);
}
public override void OnSecuritiesChanged(SecurityChanges changes)
{
foreach(var security in changes.AddedSecurities)
{
// Requesting tactical data for trading signal generation
var extractAlphaTacticalModelSymbol = AddData<ExtractAlphaTacticalModel>(security.Symbol).Symbol;
// Historical Data
var history = History(new[]{extractAlphaTacticalModelSymbol}, 60, Resolution.Daily);
Debug($"We got {history.Count()} items from our history request");
}
}
}
Framework Algorithm Example
The following example algorithm creates a dynamic universe of the 100 most liquid US Equities. Each day, it then forms an equal-weighted dollar-neutral portfolio of the 10 companies that are most likely to outperform and the 10 that are most likely to underperform.
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)
self.add_universe(self.my_coarse_filter_function)
self.universe_settings.resolution = Resolution.MINUTE
# Custom alpha model to generate trading signal based on tactical data
self.add_alpha(ExtractAlphaTacticalModelAlphaModel())
# Invest equally and dollar-neutral to evenly dissipate capital risk and hedge systematic risk
self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
self.set_execution(ImmediateExecutionModel())
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
class ExtractAlphaTacticalModelAlphaModel(AlphaModel):
def __init__(self) -> None:
# A variable to control the time of rebalance
self.day = -1
def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
if self.day == algorithm.time.day: return []
self.day = algorithm.time.day
# 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]]
insights = []
for symbol in long_symbols:
insights.append(Insight.price(symbol, Expiry.END_OF_DAY, InsightDirection.UP))
for symbol in short_symbols:
insights.append(Insight.price(symbol, Expiry.END_OF_DAY, InsightDirection.DOWN))
return insights
def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
for security in changes.added_securities:
# Requesting tactical data for trading signal generation
extract_alpha_tactical_model_symbol = algorithm.add_data(ExtractAlphaTacticalModel, security.symbol).symbol
# Historical Data
history = algorithm.history(extract_alpha_tactical_model_symbol, 60, Resolution.DAILY)
algorithm.debug(f"We got {len(history)} items from our history request")
public class ExtractAlphaTacticalModelAlgorithm : QCAlgorithm
{
public override void Initialize()
{
SetStartDate(2021, 10, 10);
SetEndDate(2023, 10, 10);
SetCash(100000);
AddUniverse(MyCoarseFilterFunction);
UniverseSettings.Resolution = Resolution.Minute;
// Custom alpha model to generate trading signal based on tactical data
AddAlpha(new ExtractAlphaTacticalModelAlphaModel());
// Invest equally and dollar-neutral to evenly dissipate capital risk and hedge systematic risk
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
SetExecution(new ImmediateExecutionModel());
}
private IEnumerable<Symbol> MyCoarseFilterFunction(IEnumerable<CoarseFundamental> coarse)
{
// 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
return (from c in coarse
where c.HasFundamentalData && c.Price > 4
orderby c.DollarVolume descending
select c.Symbol).Take(100);
}
}
public class ExtractAlphaTacticalModelAlphaModel: AlphaModel
{
// A variable to control the time of rebalance
public DateTime _time;
public ExtractAlphaTacticalModelAlphaModel()
{
_time = DateTime.MinValue;
}
public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
{
if (_time > algorithm.Time) return new List<Insight>();
// Trade only based on the updated tactical data
var 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
var sortedByScore = from s in points.Values
where (s.Score != None)
orderby s.Score descending
select s.Symbol.Underlying;
var longSymbols = sortedByScore.Take(10).ToList();
var shortSymbols = sortedByScore.TakeLast(10).ToList();
var insights = new List<Insight>();
insights.AddRange(longSymbols.Select(symbol => new Insight(symbol, Expiry.EndOfDay, InsightType.Price, InsightDirection.Up)));
insights.AddRange(shortSymbols.Select(symbol => new Insight(symbol, Expiry.EndOfDay, InsightType.Price, InsightDirection.Down)));
_time = Expiry.EndOfDay(algorithm.Time);
return insights;
}
public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
{
foreach(var security in changes.AddedSecurities)
{
// Requesting tactical data for trading signal generation
var extractAlphaTacticalModelSymbol = algorithm.AddData<ExtractAlphaTacticalModel>(security.Symbol).Symbol;
// Historical Data
var history = algorithm.History(new[]{extractAlphaTacticalModelSymbol}, 60, Resolution.Daily);
algorithm.Debug($"We got {history.Count()} items from our history request");
}
}
}
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