Portfolio Construction

The NullPortfolioConstructionModel is the default Portfolio Construction model. It doesn't return any PortfolioTarget objects. It's useful if you need to analyze an Alpha model in isolation.

SetPortfolioConstruction(new NullPortfolioConstructionModel()); 

self.set_portfolio_construction(NullPortfolioConstructionModel()) 

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.

The EqualWeightingPortfolioConstructionModel assigns an equal share of the portfolio to the securities with active insights. This weighting scheme is useful for universe rotation based on simple portfolio strategies.

SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());

self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())

The following table describes the arguments the model accepts:

This model supports other data types for the rebalancing frequency argument. For more information about the supported types, see Rebalance Frequency.

This model removes expired insights from the Insight Manager during each rebalance. It also removes all insights for a security when the security is removed from the universe.

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.

The ConfidenceWeightedPortfolioConstructionModel generates target portfolio weights based on the Insight.Confidence for the last Insight of each Symbol. If the Insight has a direction of InsightDirection.Up, the model generates long targets. If the Insight has a direction of InsightDirection.Down, the model generates short targets. If the sum of all the last active Insight per Symbol is greater than 1, the model factors down each target percent holdings proportionally so the sum is 1. The model ignores Insight objects that have no Confidence value.

SetPortfolioConstruction(new ConfidenceWeightedPortfolioConstructionModel());

self.set_portfolio_construction(ConfidenceWeightedPortfolioConstructionModel())

The following table describes the arguments the model accepts:

This model supports other data types for the rebalancing frequency argument. For more information about the supported types, see Rebalance Frequency.

This model removes expired insights from the Insight Manager during each rebalance. It also removes all insights for a security when the security is removed from the universe.

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.

The InsightWeightingPortfolioConstructionModel generates target portfolio weights based on the Insight.Weight for the last Insight of each Symbol. If the Insight has a direction of InsightDirection.Up, the model generates long targets. If the Insight has a direction of InsightDirection.Down, the model generates short targets. If the sum of all the last active Insight per Symbol is greater than 1, the model factors down each target percent holdings proportionally so the sum is 1. The model takes the absolute value of the Weight of each Insight object and ignores Insight objects that have no Weight value.

SetPortfolioConstruction(new InsightWeightingPortfolioConstructionModel());

self.set_portfolio_construction(InsightWeightingPortfolioConstructionModel())

The following table describes the arguments the model accepts:

This model supports other data types for the rebalancing frequency argument. For more information about the supported types, see Rebalance Frequency.

This model removes expired insights from the Insight Manager during each rebalance. It also removes all insights for a security when the security is removed from the universe.

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.

The SectorWeightingPortfolioConstructionModel generates target portfolio weights based on the CompanyReference.IndustryTemplateCode provided by the US Fundamental dataset. The target percent holdings of each sector is 1/S where S is the number of sectors and the target percent holdings of each security is 1/N where N is the number of securities of each sector. If the insight has a direction of InsightDirection.Up, the model generates long targets. If the insight has a direction of InsightDirection.Down, the model generates short targets. The model ignores Insight objects for Symbols that have no CompanyReference.IndustryTemplateCode.

SetPortfolioConstruction(new SectorWeightingPortfolioConstructionModel());

self.set_portfolio_construction(SectorWeightingPortfolioConstructionModel())

The following table describes the arguments the model accepts:

This model supports other data types for the rebalancing frequency argument. For more information about the supported types, see Rebalance Frequency.

This model removes expired insights from the Insight Manager during each rebalance. It also removes all insights for a security when the security is removed from the universe.

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.

The AccumulativeInsightPortfolioConstructionModel generates target portfolio weights based on all the active insights of a security. For each active Insight of direction InsightDirection.Up, it increases the position size by a fixed percent. For each active Insight of direction InsightDirection.Down, it decreases the position size by a fixed percent. For each active Insight of direction InsightDirection.Flat, it moves the position size towards 0 by a fixed percent.

SetPortfolioConstruction(new AccumulativeInsightPortfolioConstructionModel());

self.set_portfolio_construction(AccumulativeInsightPortfolioConstructionModel())

The following table describes the arguments the model accepts:

This model supports other data types for the rebalancing frequency argument. For more information about the supported types, see Rebalance Frequency.

This model removes expired insights from the Insight Manager during each rebalance. It also removes all insights for a security when the security is removed from the universe.

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.

The MeanVarianceOptimizationPortfolioConstructionModel seeks to build a portfolio with the least volatility possible and achieve a target return.

SetPortfolioConstruction(new MeanVarianceOptimizationPortfolioConstructionModel());

self.set_portfolio_construction(MeanVarianceOptimizationPortfolioConstructionModel())

The following table describes the arguments the model accepts:

This model supports other data types for the rebalancing frequency argument. For more information about the supported types, see Rebalance Frequency. If you don't provide an optimizer argument, the default one is the MinimumVariancePortfolioOptimizer with upper and lower weights that respect the portfolioBias.

This model removes expired insights from the Insight Manager during each rebalance. It also removes all insights for a security when the security is removed from the universe.

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.

The BlackLittermanOptimizationPortfolioConstructionModel receives Insight objects from multiple Alphas and combines them into a single portfolio. These multiple sources of Alpha models can be seen as the "investor views" required in the classical model.

SetPortfolioConstruction(new BlackLittermanOptimizationPortfolioConstructionModel());

self.set_portfolio_construction(BlackLittermanOptimizationPortfolioConstructionModel())

The following table describes the arguments the model accepts:

This model supports other data types for the rebalancing frequency argument. For more information about the supported types, see Rebalance Frequency. If you don't provide an optimizer argument, the default one is the MinimumVariancePortfolioOptimizer with upper and lower weights that respect the portfolioBias.

This model removes expired insights from the Insight Manager during each rebalance. It also removes all insights for a security when the security is removed from the universe.

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.

The MeanReversionPortfolioConstructionModel implements an on-line portfolio selection technique, named "On-Line Moving Average Reversion" (OLMAR). The basic idea is to represent multi-period mean reversion as "Moving Average Reversion" (MAR), which explicitly predicts next price relatives using moving averages and then forms portfolios with online learning techniques.

SetPortfolioConstruction(new MeanReversionPortfolioConstructionModel());

self.set_portfolio_construction(MeanReversionPortfolioConstructionModel())

The following table describes the arguments the model accepts:

This model supports other data types for the rebalancing frequency argument. For more information about the supported types, see Rebalance Frequency.

This model removes expired insights from the Insight Manager during each rebalance. It also removes all insights for a security when the security is removed from the universe.

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.

Reference:

• Li, B., Hoi, S. C. (2012). On-line portfolio selection with moving average reversion. arXiv preprint arXiv:1206.4626.

The RiskParityPortfolioConstructionModel seeks to build a portfolio with the equal contribution of risk to the total portfolio risk from all assets.

SetPortfolioConstruction(new RiskParityPortfolioConstructionModel());

self.set_portfolio_construction(RiskParityPortfolioConstructionModel())

The following table describes the arguments the model accepts:

This model supports other data types for the rebalancing frequency argument. For more information about the supported types, see Rebalance Frequency. If you don't provide an optimizer argument, the default one is the RiskParityPortfolioOptimizer.

This model removes expired insights from the Insight Manager during each rebalance. It also removes all insights for a security when the security is removed from the universe.

To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.