Share New Research 
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
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Quant Trader
Interesting stuff!
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Serge d'Adesky
Kudos, guys, QC just took a major step forward!
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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 argumentsThe 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.
Jared 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!!
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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.
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.
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).
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Jared Broad
You can actually use all the C# modules in Python; C#-users can't use the Python ones =).
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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.
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.
Eric Summers
Wow! Great idea. Can't wait to get into it
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.
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
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.
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.txtThe 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.
Jared 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 argumentsThe 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
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?
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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.
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Jared Broad
Lexx7 those are all characteristics of your alpha model.
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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:
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Frank Norman
This would lead to higher performing Alpha Streams. I would add it to the arsenal.
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Andrew martin czeizler
Is this being included in the backtest metrics?
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Jack Simonson
We haven't included it yet into the backtest metrics, but it's something that we're considering as well.
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Tim Bohmann
I would like to see this in the backtest metrics. This would be a quick "sanity test" when backtesting.
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Pangyuteng
Glad to see that Quantconnect seems to be agile and willing to update methods for their algo selection. I also find it comforting that Lean is opensource, so I can check the implementation and their test cases. :)
SPY notebook SR: 0.836, PSR: 50.000% Lean SR: 0.900, PSR: 41.185% ---- SHY notebook SR: 1.237, PSR: 68.901% Lean SR: 1.224, PSR: 69.799% ---- QQQ notebook SR: 0.910, PSR: 52.476% Lean SR: 0.990, PSR: 49.063% ---- AMZN notebook SR: 1.218, PSR: 83.669% Lean SR: 1.339, PSR: 78.661% ---- GE notebook SR: -0.458, PSR: 10.897% Lean SR: -0.306, PSR: 0.159% ----For those that want to do some quick and dirty backtest or optimization/investigation in research, please do keep in mind the inevitable computation differences between LEAN and simple PSR computation in notebook. I think this is one of those little but important things most engineers/data scientists have to deal with when working among different environments. Anyways... per Alexandra's response, "rolling PSR" is computed by LEAN (I can't seem to figure out where the rolling part is though in github), while Jack's PSR is a single value derived from one stream of returns. Below shows you the difference for Sharpe Ratio (SR) and PSR for a few tickers if you buy-and-hold in the same timeframe between notebook (using annualized SR) and Lean. I would expect PSR for SPY to be a bit closer to 50% from LEAN. Avid notebook users... beware!
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Kamer Ali YUKSEL
I would be more than glad if someone can also share how to calculate rolling PSR.
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Kamer Ali YUKSEL
I also have a question, why you haven't utilized Deflated Sharpe Ratio? That seems to be an extension over PSR.
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Jared Broad
DSR was not deterministic, and required analysis of many backtests. PSR was chosen because it could be self contained with a single analysis.
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Kamer Ali YUKSEL
Hello Jared, thanks for letting me know.!I have a small objection about PSR. Since that it uses a long-only SPY benchmark, it does not make perfect sense for long-short portfolios or a long-only ETF portfolio that includes some inverse ETF(s), such as the current universe. I sometimes obtain better results in many of the metrics but the PSR is lower. To sum up, it is not a very good metric in terms of taking the market-neutrality into account (except for a long-only portfolio that does not include any Inverse ETFs - which is not valid for the current competition universe).
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Jared Broad
We chose not to use SPY as the benchmark but a fixed Sharpe-ratio of 1.0 to make the measurement cross-asset / cross-strategy type; so the PSR readings in LEAN's case are the probability the real algorithm returns are greater than 1.0 Sharpe ratio.
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Kamer Ali YUKSEL
Just to let you know, I encountered another implementation of PSR in some other source codes that I have checked. The first one below is that other implementation and the second one below is the implementation in this thread. I did not yet compare the output of those but they seemed to be different to me. I would welcome an advise on this.
std = np.sqrt(1 - (sps.skew(ret)*sharpe) + sharpe**2 * (sps.kurtosis(ret)-1)/4)
return sps.norm.cdf(sharpe * np.sqrt(len(ret)-1) / std)
std = 1 - (sps.skew(ret) * sharpe) + ((sps.kurtosis(ret)-1)/4) * sharpe
return sps.norm.cdf(sharpe / np.sqrt( std / (len(ret) - 1)))
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Rahul Chowdhury
Hey Kamer,
The other implementation of PSR you provided is actually the correct implementation as defined by the paper The Sharpe Ratio Efficient Fronter by Bailey D. & Prado M. (2012). That equation is the one that is defined in LEAN and is the correct one. However, we made a small mistake in copying it over in the journal post, which we've since fixed. There was a missing factor of Sharpe in the kurtosis term. With the added Sharpe factor, the two equations are the same, just differently represented. Thanks for pointing it out!
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Kamer Ali YUKSEL
Hello Rahul, Yeah, I also verified that later but did not want to write it here to make a conclusion :) Thnx!
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Apollos Hill
So i am finally rereading this thread. Sorrry. I know its kind of old now and has been added ot the Alpha criteria. I'm looking for tips on how to increase my PSR past 50 .
" the Probabilistic Sharpe Ratio is an atemporal measure of strategy performance expressed in terms of probability of skill beyond a given benchmark..." If we are using 1.0 Sharpe as a benchmark and not SPY...if my strategy is doing 250% return over a 5 year backtest, how am i only at 25-50% PSR and 0.8 sharpe? IS it suggesting that I somehow am not maximizing the profit potential of my strategy? I don't want to post a backtest here but i am in forex, trading 4 out of the 8 major pairs. I can't trade all the pairs or else i'm over exposed. I'm trading on weekly bars and holding for months using million dollar positions. I'm also in positions as much as i can be without over trading. 250% over 5 years is about 50% annual returns. Is that not worthy of a 1.0 sharpe? I doubt the assets that Ted posted above (with LEan SR thanks) could achieve these returns.
Appreciate the help on increasing my PSR so i can lease this alpha stream.
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Jared Broad
Hi Apollos, it means the volatility of the strategy is too high. Perhaps you can invest smaller, or risk-adjusted positions somehow? Could you invest for shorter periods to help reduce the exposure to swings of the market?
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Apollos Hill
Hi Jared
I tried only investing 1% per trade. I'm in the process of seeing if i can go to daily bars instead of weekly. Right now i have a nice 45degree slope up. It doesn't seem volatile to me. My Annual Variance and my Beta are 0.00. Not sure if that helps you or not. my sharpe is 0.729 so i guess PSR at 25% is saying that i have a 25% chance that my sharpe will be over 1% . So i need to improve my money making, without increasing drawdown, correct? 10% DD on a 48% total return over 5 years. (nowthat i changed to 1% positions)
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Rahul Chowdhury
Hey Apollos,
There is currently an issue with beta calculation for daily resolution data which produces 0 beta for all strategies. The true beta of your strategy may actually be larger. If your strategy is trading the same symbols for the same duration, changing position sizes will not dramatically change your PSR because your returns and drawdowns will scale proportionally. Instead try shortening your insight period or changing how you emit insights.
> my sharpe is 0.729 so i guess PSR at 25% is saying that i have a 25% chance that my sharpe will be over 1%
It means there's a 25% change your live-Sharpe is greater than 1.0; (in part because the backtesting Sharpe is 0.7)
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.
Ritaelyn
Hi Jared,
What's your thoughts of allowing for a sortino ratio as another means for submission? One thing I'd like to submit to alpha is about 3.60 sortino but it's under 1.0 sharpe, as sharpe unfortunately penalizes both upside and downside volatility equally.
Thanks for considering it!
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.
Hector Barrio
Hello Ritaelyn, I agree with you. Any equity trading during the COVID instability will be highly penalized by SR/PSR metrics, even if properly hedged and/or profitable. The normality assumption of the sharpe ratio can, in extreme cases, make very large positive returns in possitively skewed strategies undesirable. It is also a possibilty to have strategies that could "fish" for higher PSR by extending the test period to low volatility-low return periods (cool periods) even at equity loss.
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.
.ekz. INVESTOR
+1 to Ritaelyn point. I'd love to see Sortino Ratio. From what I've been reading, sharpe ratio (and subsequently PSR as well) is not the best perf indicator for trend following strategies.
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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