EqualWeightingPortfolioConstructionModel

QuantConnect.Algorithm.Framework.Portfolio.EqualWeightingPortfolioConstructionModel

EqualWeightingPortfolioConstructionModel(
    rebalance: Any, portfolio_bias: PortfolioBias = ...
)

EqualWeightingPortfolioConstructionModel(
    rebalancing_date_rules: IDateRule,
    portfolio_bias: PortfolioBias = ...,
)

EqualWeightingPortfolioConstructionModel(
    rebalancing_func: Callable[
        [datetime], Optional[datetime]
    ],
    portfolio_bias: PortfolioBias = ...,
)

EqualWeightingPortfolioConstructionModel(
    rebalancing_func: Callable[[datetime], datetime],
    portfolio_bias: PortfolioBias = ...,
)

EqualWeightingPortfolioConstructionModel(
    time_span: timedelta,
    portfolio_bias: PortfolioBias = ...,
)

EqualWeightingPortfolioConstructionModel(
    resolution: Resolution = ...,
    portfolio_bias: PortfolioBias = ...,
)

Bases: PortfolioConstructionModel

Provides an implementation of IPortfolioConstructionModel that gives equal weighting to all securities. The target percent holdings of each security is 1/N where N is the number of securities. For insights of direction InsightDirection.UP, long targets are returned and for insights of direction InsightDirection.DOWN, short targets are returned.

Initialize a new instance of EqualWeightingPortfolioConstructionModel

Parameters:

Name	Type	Description	Default
`rebalance`	`Optional[Any]`	Rebalancing func or if a date rule, timedelta will be converted into func.	`None`
`portfolio_bias`	`PortfolioBias`	Specifies the bias of the portfolio (Short, Long/Short, Long)	`...`
`rebalancing_date_rules`	`Optional[IDateRule]`	The date rules used to define the next expected rebalance time	`None`
`rebalancing_func`	`Optional[Callable[[datetime], Optional[datetime]] \| Callable[[datetime], datetime]]`	For a given algorithm UTC DateTime returns the next expected rebalance time	`None`
`time_span`	`Optional[timedelta]`	Rebalancing frequency	`None`
`resolution`	`Optional[Resolution]`	Rebalancing frequency	`...`

rebalance_on_security_changes

rebalance_on_security_changes: bool

True if should rebalance portfolio on security changes. True by default

rebalance_on_insight_changes

rebalance_on_insight_changes: bool

True if should rebalance portfolio on new insights or expiration of insights. True by default

algorithm

algorithm: IAlgorithm

The algorithm instance

This codeEntityType is protected.

python_wrapper

python_wrapper: PortfolioConstructionModelPythonWrapper

This is required due to a limitation in PythonNet to resolved overriden methods. When Python calls a C# method that calls a method that's overriden in python it won't run the python implementation unless the call is performed through python too.

This codeEntityType is protected.

determine_target_percent

determine_target_percent(
    active_insights: List[Insight],
) -> Dictionary[Insight, float]

Will determine the target percent for each insight

This codeEntityType is protected.

Parameters:

Name	Type	Description	Default
`active_insights`	`List[Insight]`	The active insights to generate a target for	required

Returns:

Type	Description
`Dictionary[Insight, float]`	A target percent for each insight.

respect_portfolio_bias

respect_portfolio_bias(insight: Insight) -> bool

Method that will determine if a given insight respects the portfolio bias

This codeEntityType is protected.

Parameters:

Name	Type	Description	Default
`insight`	`Insight`	The insight to create a target for	required

Returns:

Type	Description
`bool`	True if the insight respects the portfolio bias.

on_securities_changed

on_securities_changed(
    algorithm: QCAlgorithm, changes: SecurityChanges
) -> None

Event fired each time the we add/remove securities from the data feed

Parameters:

Name	Type	Description	Default
`algorithm`	`QCAlgorithm`	The algorithm instance that experienced the change in securities	required
`changes`	`SecurityChanges`	The security additions and removals from the algorithm	required

create_targets

create_targets(
    algorithm: QCAlgorithm, insights: List[Insight]
) -> Iterable[IPortfolioTarget]

Create portfolio targets from the specified insights

Parameters:

Name	Type	Description	Default
`algorithm`	`QCAlgorithm`	The algorithm instance	required
`insights`	`List[Insight]`	The insights to create portfolio targets from	required

Returns:

Type	Description
`Iterable[IPortfolioTarget]`	An enumerable of portfolio targets to be sent to the execution model.

filter_invalid_insight_magnitude

filter_invalid_insight_magnitude(
    algorithm: IAlgorithm, insights: List[Insight]
) -> List[Insight]

Helper class that can be used by the different IPortfolioConstructionModel implementations to filter Insight instances with an invalid Insight.magnitude value based on the IAlgorithmSettings

This codeEntityType is protected.

Parameters:

Name	Type	Description	Default
`algorithm`	`IAlgorithm`	The algorithm instance	required
`insights`	`List[Insight]`	The insight collection to filter	required

Returns:

Type	Description
`List[Insight]`	Returns a new array of insights removing invalid ones.

get_target_insights

get_target_insights() -> List[Insight]

Gets the target insights to calculate a portfolio target percent for

This codeEntityType is protected.

Returns:

Type	Description
`List[Insight]`	An enumerable of the target insights.

is_rebalance_due

is_rebalance_due(
    insights: List[Insight],
    algorithm_utc: Union[datetime, date],
) -> bool

Determines if the portfolio should be rebalanced base on the provided rebalancing func, if any security change have been taken place or if an insight has expired or a new insight arrived If the rebalancing function has not been provided will return true.

This codeEntityType is protected.

Parameters:

Name	Type	Description	Default
`insights`	`List[Insight]`	The insights to create portfolio targets from	required
`algorithm_utc`	`Union[datetime, date]`	The current algorithm UTC time	required

Returns:

Type	Description
`bool`	True if should rebalance.

refresh_rebalance

refresh_rebalance(
    algorithm_utc: Union[datetime, date],
) -> None

Refresh the next rebalance time and clears the security changes flag

This codeEntityType is protected.

set_python_wrapper

set_python_wrapper(
    python_wrapper: PortfolioConstructionModelPythonWrapper,
) -> None

Used to set the PortfolioConstructionModelPythonWrapper instance if any

This codeEntityType is protected.

set_rebalancing_func

set_rebalancing_func(rebalance: Any) -> None

Python helper method to set the rebalancing function. This is required due to a python net limitation not being able to use the base type constructor, and also because when python algorithms use C# portfolio construction models, it can't convert python methods into func nor resolve the correct constructor for the date rules, timespan parameter. For performance we prefer python algorithms using the C# implementation

This codeEntityType is protected.

Parameters:

Name	Type	Description	Default
`rebalance`	`Any`	Rebalancing func or if a date rule, timedelta will be converted into func. For a given algorithm UTC DateTime the func returns the next expected rebalance time or null if unknown, in which case the function will be called again in the next loop. Returning current time will trigger rebalance. If null will be ignored	required

should_create_target_for_insight

should_create_target_for_insight(insight: Insight) -> bool

Method that will determine if the portfolio construction model should create a target for this insight

This codeEntityType is protected.

Parameters:

Name	Type	Description	Default
`insight`	`Insight`	The insight to create a target for	required

Returns:

Type	Description
`bool`	True if the portfolio should create a target for the insight.