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Framework Question MeanVarianceOptimizationPortfolioConstructionModel

Hi,

I have a questions regarding the MeanVarianceOptimizationPortfolioConstructionModel.

See atteched backtest: Why does the MeanVarianceOptimizationPortfolioConstructionModel gives the same results as the EqualWeightingPortfolioConstructionModel?

I tried to vary the parameters but without any effect in the results.

Thx.

Eugene

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Hi Eugene ,

Sorry about the wait.

Since the alpha model only emits insights once, the mean-variance portfolio is solely based on the initial 63-day daily historical data that is used by the portfolio optimizer to found the minimum variance. The initial state of the optimization process is the equally-weighted portfolio and, if the solver doesn't find a solution, it returns the initial state. It's probably the case since the solver needs to find portfolio with a 2% return that might not be possible with the selected universe.

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Hello Alexandre,

yes, it seems that the returns are calculated just OnSecurityChange. 

If that is the case, how to get a minimal variance portfolio for a manual universe selection? I mean that should be the very basic application for the MeanVarianceOptimizationPortfolioConstructionModel.

Application would be:

1) Two Securities. e.g. SPY and TLT

2) Every 30 days, determin the wights such as the variance for the portfolio is minimal, according to the last 30 days returns.

Alternatively to 2) the question could also be: Every 30 days, determin the wights such as the sharpe ratio for the portfolio is maximal, according to the last 30 days returns

Can the MeanVarianceOptimizationPortfolioConstructionModel be used for that, or do I need to implement such a function manually?

Thx again,

Eugene

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Hi Eugene ,

In theory, the MVOPCM can be used by that. However, in practice, if there is no solution for the given target, it's not possible.
I tried the MaximumSharpeRatioPortfolioOptimizer:

SetPortfolioConstruction(new MeanVarianceOptimizationPortfolioConstructionModel(
TimeSpan.FromDays(30),
PortfolioBias.Long,
1,
63,
Resolution.Daily,
0.02,
new MaximumSharpeRatioPortfolioOptimizer(0,1,0)));

but got the same results. So it might be the case that the equal-weighting portfolio is the minimum variance portfolio for this particular case.

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Update Backtest





0

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


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