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Algorithm Framework

Insight Manager

Introduction

The InsightManager tracks all of the Insight objects in your algorithm. It's an InsightCollection, which stores all of your insights in an internal dictionary that has the security Symbol as the key and a list of Insight objects as the value. You can access the manager anywhere in your algorithm where you have reference to the algorithm class. If you want to use an InsightCollection in your algorithm that's seperate from the InsightManager, create a new InsightCollection.

Check Membership

To check if an insight exists in the InsightManager, call the contains method.

Select Language: 
# Check the algorithms central insight collection (InsightManager) to avoid emitting duplicates, reducing the processing work. 
if algorithm.insights.contains(insight):
    pass

To check if the InsightManager has an insight for a Symbol, call the contains_key method.

Select Language: 
# Check the algorithms central insight collection (InsightManager) if there's already been an insight for this symbol across the other alphas.
if algorithm.insights.contains_key(symbol):
    pass

To check if the InsightManager has active insights for a Symbol at a specific Coordinated Universal Time (UTC), call the has_active_insights method.

Select Language: 
# Check if there are active insights for a specific symbol at the given time to determine if alpha should emit more insights
if algorithm.insights.has_active_insights(symbol, utc_time):
    pass

Get Insights

To get the insights for a Symbol, index the InsightManager with the Symbol.

Select Language: 
if algorithm.insights.contains_key(symbol):
    insights = algorithm.insights[symbol]

To get the insights that pass a filter, call the get_insights method.

Select Language: 
insights = algorithm.insights.get_insights(lambda insight: insight.direction == Insightdirection.up)

To iterate through the InsightManager, call the get_enumerator method.

Select Language: 
enumerator = algorithm.insights.get_enumerator()

To get all of the insights that will be active at a certain UTC time, call the get_active_insights method.

Select Language: 
active_insights = algorithm.insights.get_active_insights(utc_time)

Get the Next Expiry Time

To get the next insight expiry time in UTC, call the get_next_expiry_time method.

Select Language: 
next_expiry = algorithm.insights.get_next_expiry_time()

Remove Insights

Only the Portfolio Construction model should remove insights from the InsightManager. It should remove insights when the insights expire and when the corresponding security leaves the universe.

To remove an insight from the InsightManager, call the remove method.

Select Language: 
remove_successful = algorithm.insights.remove(insight)

To remove all the insights for a set of Symbol objects, pass a list of Symbol objects the clear method.

Select Language: 
algorithm.insights.clear(symbols)

To remove all the insights that will be expired at a certain UTC time, call the remove_expired_insights method.

Select Language: 
expired_insights = algorithm.insights.remove_expired_insights(utc_time)

Cancel Insights

In some cases, you may want to cancel an Insight. For example, if a Risk Management model in your algorithm liquidates a security, it should also cancel all of the active insights for the security. If you don't cancel the insights, the Portfolio Construction model might create a new PortfolioTarget to re-enter the position.

Another example of a situtation where you would want to cancel an Insight is when an Alpha model in your algorithm determines the trading opportunity has pre-maturely ended. For instance, say you want your algorithm to enter a long position in a security when its Relative Strength Index (RSI) moves below 20 and then liquidate the position when the security's RSI moves above 30. If you emit an Insight that has a duration of 30 days when the RSI moves below 20 but the RSI moves above 30 in 10 days, you should cancel the Insight when the RSI moves above 30.

To cancel insights, call the cancel/Expire method with a list of Insight objects.

Select Language: 
algorithm.insights.cancel(insights)

To cancel all the insights for some securities, call the cancel/Expire method with a list of Symbol objects.

Select Language: 
algorithm.insights.cancel(symbols)

When you cancel an active insight, it's close_time_utc property is set to one second into the past.

Add Insights to Self-Managed Insight Collections

To add insights to the InsightManager, return a list of Insight objects from the update method of your Alpha model or call the emit_insights method. If you manage an InsightCollection that's seperate from the InsightManager, there are some methods to add Insight objects to it.

To add an insight to an InsightCollection, call the add method.

Select Language: 
# You can manually add an Insight to an InsightCollection object to organize, fetch, or cancel insights an alpha has generated. 
self.insight_collection.add(insight)

To add a list of insights, call the add_range method.

Select Language: 
# You can manually add a list of Insights to an InsightCollection object to organize, fetch, or cancel insights an alpha has generated. 
self.insight_collection.add_range(insights)

Preserve Insights Between Deployments

Follow these steps to use the Object Store to preserve the algorithm state across live deployments:

  1. Create an algorithm that defines a storage key and adds insights to the Insight Manager.
    2. Select Language: 
    class ObjectStoreChartingAlgorithm(QCAlgorithm):
    def initialize(self):
        self.insight_key = f"{self.project_id}/insights"
        self.set_universe_selection(ManualUniverseSelectionModel([ Symbol.create("SPY", SecurityType.EQUITY, Market.USA) ]))
        self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(5), 0.025, None))
  2. At the top of the algorithm file, add the following imports:
    3. from Newtonsoft.Json import JsonConvert
from System.Collections.Generic import List

    Insight objects are a C# objects, so you need the preceding C# libraries to serialize and deserialize them.

  3. In the on_end_of_algorithm event handler of the algorithm, get the Insight objects and save them in the Object Store as a JSON object.
    4. Select Language: 
    def on_end_of_algorithm(self):
    insights = self.insights.get_insights(lambda x: x.is_active(self.utc_time))
    content = ','.join([JsonConvert.SerializeObject(x) for x in insights])
    self.object_store.save(self.insight_key, f'[{content}]')
  4. At the bottom of the initialize method, read the Insight objects from the Object Store and add them to the Insight Manager.
    5. Select Language: 
    if self.object_store.contains_key(self.insight_key):
    insights = self.object_store.read_json[list[Insight]](self.insight_key)
    self.insights.add_range(insights)

The following algorithm provides a full example of preserving the Insight state between deployments:

Select Language: 
class ObjectStoreInsightsAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.universe_settings.resolution = Resolution.DAILY

        self.set_start_date(2023,4,1)
        self.set_end_date(2023,4,11)
        self.set_cash(100000)

        self.insights_key = f"{self.project_id}/insights"

        # Read the file with the insights
        if self.object_store.contains_key(self.insights_key):
            # Load cached JSON key value pair from object store, then add to algorithm insights.
            insights = self.object_store.read_json[list[Insight]](self.insights_key)
            self.log(f"Read {len(insights)} insight(s) from the Object Store")
            self.insights.add_range(insights)

            # Delete the key to reuse it
            self.object_store.delete(self.insights_key)

        self.set_universe_selection(ManualUniverseSelectionModel([ Symbol.create("SPY", SecurityType.EQUITY, Market.USA) ]))
        self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(5), 0.025, None))
        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(Resolution.DAILY))

    def on_end_of_algorithm(self) -> None:
        # Get all active insights.
        insights = self.insights.get_insights(lambda x: x.is_active(self.utc_time))
        # If we want to save all insights (expired and active), we can use
        # insights = self.insights.get_insights(lambda x: True)
        self.log(f"Save {len(insights)} insight(s) to the Object Store.")
        content = ','.join([JsonConvert.SerializeObject(x) for x in insights])
        self.object_store.save(self.insights_key, f'[{content}]')

Properties

The InsightManager has the following properties:

InsightManager Select Language:

Examples

The following examples demonstrate some common practices for using insight manager.

Example 1: Cancel Insight

If the risk management model only submits the PortfolioTarget to liquidate the security, it will be re-ordered immediately due to the active insight. Hence, we also need to add logic to cancel the insight in the manage_risk method of the risk management model. The below example only permits 1-day holding time per each insight through a custom risk management model.

Select Language: 
class FrameworkInsightManagerAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2024, 8, 12)
        self.set_end_date(2024, 9, 1)
        self.set_cash(1000000)

        # Add a universe of the most liquid stocks since their trend is more capital-supported.
        self.add_universe_selection(QC500UniverseSelectionModel())
        # Emit insights on EMA cross, indicating the trend changes. We use short-term versus medium-term for more trade opportunities.
        self.add_alpha(EmaCrossAlphaModel(20, 60, Resolution.DAILY))
        # Equal weighting on each insight to dissipate capital risk evenly.
        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())

        # Add a risk management model that controls maximum holding time.
        self.add_risk_management(MaximumHoldingTimeRiskManagementModel(timedelta(5)))

class MaximumHoldingTimeRiskManagementModel(RiskManagementModel):
    def __init__(self, maximum_holding_time: timedelta) -> None:
        self.maximum_holding_time = maximum_holding_time
        # A dictionary to hold the holding time of each position.
        self.holding_time = {}

    def manage_risk(self, algorithm: QCAlgorithm, targets: list[PortfolioTarget]) -> list[PortfolioTarget]:
        targets = []

        for symbol, holding in algorithm.portfolio.items():
            if holding.invested:
                if symbol not in self.holding_time:
                    # For newly open positions, save the entry time.
                    self.holding_time[symbol] = algorithm.time
                elif algorithm.time - self.holding_time[symbol] > self.maximum_holding_time:
                    # Liquidate the position if the holding time exceeds the permitted level.
                    targets.append(PortfolioTarget(symbol, 0))
                    # Cancel the insight to avoid repeated re-ordering.
                    algorithm.insights.cancel([symbol])
                    # Remove from the dictionary.
                    self.holding_time.pop(symbol)
        
        return targets

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