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Hello All!
Yesterday we presented a webinar for Interactive Brokers on using Mean Variance Optimization for ETF Portfolio Rebalancing. We implemented it in the Algorithm Framework -- a new way we're offering for users to design algorithms which is more modular and extensible.
The Framework was built using the most common abstractions we see in the community. It has 5 modules - Universe Selection, Alpha Creation, Portfolio Construction, Execution and Risk Management. If you're interested I'd recommend checking it out as a way to improve your algorithm foo!
The key module we discuss here is the Mean Variance Portfolio Construction Model. This takes the historical returns of the assets presented by the Historical Returns Alpha Model and uses them to construct the portfolio! Its fascinating stuff on the cutting edge of Quant finance so I'd highly recommend learning the new framework.
Check out the video and slides below!
Jared Broad
Here are the backtest and notebook for the webinar
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.
Quant Trader
Interesting stuff!
Serge d'Adesky
Kudos, guys, QC just took a major step forward!
Jared Broad
Thanks Serge :) We're very excited by its potential. Still working out last couple of API changes but it'll be polished this week.
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.
Roberto M
Hi, thanks for sharing. This opens quite new interesting perspectives. I have one issue tough. Yesterday I cloned the algo and run it ok. Today I am trying to run it and I am getting and error - see below. I cloned it again, but same problem. Any idea?
Runtime Error: TypeError : no constructor matches given arguments at Update in HistoricalReturnsAlphaModel.py:line 55 TypeError : no constructor matches given argumentsJared Broad
Apologies Roberto M - we've been heavily editing the API in some last minute polishing. We standardized the Insight() creation API between the method overloads -- and renamed the SetPortfolioSelection -> SetUniverseSelection so its not confused with SetPortfolioConstruction methods.
We just merged a bunch of demonstrations in Python and C#. More documentation to follow! See the updated building backtest -
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.
Mochunhei
I think this is amazing!! I really like the idea of modularization!!
Mochunhei
Jared, which language do you suggest us to work with this framework?
I see that on GitHub, some modules are only available for C# (e.g. StandardDeviationExecutionModel.cs), some modules are only available for Python (e.g. MeanVarianceOptimizationPortfolioConstructionModel.py), some modules are available for both.
Will all modules have both versions eventually? Which language will get more updates? Thanks.
Alexandre Catarino
We aim to have versions for both languages as examples like we have for algorithms.
Some modules, for example Universe Selection, may only have a C# version, because users can plug their code (the universe selection method) into them.
The MVO-PC model are only available in python because it uses some scipy magic. :-) In this case, a brave C# quant will probably make a C# version using MathNet (please checkout PortfolioOptimizationNumericsAlgorithm).
Jared Broad
You can actually use all the C# modules in Python; C#-users can't use the Python ones =).
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.
Mochunhei
I didn't think about that, how do you do that?
Will you give us an example? Or maybe describe it in the docs? Thanks.
Eric Summers
Wow! Great idea. Can't wait to get into it
Jonathon Rogane
I seem to be having trouble implementing a fundamental universe selection into this. there doesnt seem to be any documents on this.
Thanks
Lucas Silva
Cloned and it gives error:
5 | 04:30:19:Successfully sent backtest request for 'Jumping Brown Buffalo', (Compile Id: 4f6d6fbcded5cedf1bea2a4a7e53d826-5b9f35497d14d48d10be6379c60792c6)6 | 04:30:34:During the algorithm initialization, the following exception has occurred: AttributeError : 'MeanVarianceOptimizationAlgorithm' object has no attribute 'SetPortfolioSelection'at Initialize in main.py:line 37
AttributeError : 'MeanVarianceOptimizationAlgorithm' object has no attribute 'SetPortfolioSelection'
7 | 04:30:44:Your log was successfully created and can be retrieved from: https://www.quantconnect.com/backtest/5915/1480838/efef14a303b7d288b971607a130ed4e2-log.txtJared Broad
Sorry Lucas this was renamed to "SetUniverseSelection"
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.
Lexx7
Still doesn't work after changing that
Runtime Error: TypeError : no constructor matches given arguments at Update in HistoricalReturnsAlphaModel.py:line 92 TypeError : no constructor matches given argumentsLexx7
Nevermind.... cloned the wrong one.
I've read through the docs, where do you set things like:
- Rebalance frequency
- Number of assets to hold at any time
- Lookback period for estimation
I assume these would be variables in the framework?
Gurumeher Sawhney
The number of assets to hold at anytime is determined during the Initialize() handler, based upon the universe selection and portfolio construction. The rebalancing frequency is dependent on OnSecuritiesChanged(), and will maintain the portfolio target that was initialized. Refer to the first link to see how these variables work. Some portfolios (i.e Black-Litterman) use lookback period, and that can be initialized in the constructor. The second link below shows Black-Litterman's implementation.
Jared Broad
Lexx7 those are all characteristics of your alpha model.
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.
Lexx7
Ok so I have tried playing around with the parameters. I set the below
self.SetPortfolioConstruction(MeanVarianceOptimizationPortfolioConstructionModel(lookback=180, period=20, minimum_weight=0, maximum_weight=0.99))
Questions
1. How do I set it to rebalance monthly? This is not clear to me & I can't find this in any of the algorithm framework examples.
2. What is the difference between lookback & period? One should be the lookback for returns & the other for volatility but it is not clear to me based on the descriptions provided in the code.
Errors
Why does it fail to make fills & crash if I try run the attached algo over a longer timeframe? What decides when it goes into margin?
Trying to emulate the attached example as a sanity check:
Jared Broad
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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