Intersection of ROC comparison using OUT_DAY approach

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This is the original name of the algorithm that I created as a result of a successful collaboration
on the Quantopian forum thread "New Strategy - In & Out" in October 2020.
Unfortunately, the collaboration did not continue on the QuantConnect forum.
At least I am very uncomfortable with the strange names used by Peter Gunther in the algorithms,
such as "Distilled Bear", variable names and decision making logic.
This algorithm unlike those from Peter Gunther has three pairs as a source, two parameters and
concensus of all three for exit signal.
I did not optimized parameters, so you can get better results.

I want to thank Jared Broad and his team for giving me the opportunity to recover one of
my favorite algorithms.

Happy New Year to all

Update Backtest








 
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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.


@T Smith ,

The issue with your 'out of sample' is it is not out of sample.  The moment you optimize any of the parameters by 'optimizing the strategy', all you have done is taken a out of sample test and turned it into an in sample test. An out of sample would backtest the same exact parameters as the  Vladimir  original strategy but during the period you tested. This is Menno Dreischor whole point. In essence, your test has been fitted for the time period you tested. It is now in-sample.

I believe with out optimization you will see massively reduced Sharpe's, PSR's, average wins, and overall compound returns.  Try testing with zero optimization but using the original parameters.  Let's see what that backtest indicates for this time period.

-TC

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Thunder Chicken You've missed the point. The two parameters VOLA and BASE_RET we're optimised on data from 2001-2010.

Optimizing our parameters over that data 2001-2010, the best iteration was:

        self.VOLA = 128
        self.BASE_RET = 90

Here is the winning IN-SAMPLE backtest.

Bear in mind we are using only 1 out of 3 signals due to lack of data. Would love to hear people thoughts on this as well as ideas to investigate further.

 

 

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T Smith

Thank you for sharing the results of your research reducing signal just to XLI/XLU.

 

Thunder Chicken

I used T Smith reduced signal research with my original parameters

    self.VOLA = 126; self.BASE_RET = 85

and backtested it from 2002 to 2008

Isn't that "out of sample"?

9882_1612289488.jpg

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Vladimir, I would say, that's out-of-sample, and not bad looking at first glance. It might be useful to calculate the probabilistic Sharpe ratio compared to the buy and hold situation, which should give you an idea what the probability is, that the strategy actually improves on the benchmark in terms of risk-adjusted return. The QC statistics give a probabilistic Sharpe ratio, but it uses a Sharpe of 1 as a benchmark, which I think is less relevant, if you are looking, whether the strategy achieves its objectives, which in this case relates to correctly timing your exits from the benchmark. 

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Hello, Vladimir and Menno Dreischor 

Sorry, was at work and couldn't get back sooner. Yes, Vladimir, this is a much better representation of an out-of-sample test. While not representative of the original strategy with all in-out signals, it is something representative of out-of-sample. I think therefore you see a lower Sharpe (.713) and a lower PSR (19.304%).

We could consider looking @ recreating the other in-out signals during the same period to see if it affects performance, with no optimization.

This is a great thread, regardless. I believe folks are learning a lot. 

Cheers to everyone,

- TC.

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Hi everyone,

It's been over five weeks since I posted in QC

"Intersection of ROC comparison using OUT_DAY approach".

Since then, I have published 6 versions, and more than a dozen versions have been published by Elsid Aliaj, Mikko M, Leandro Maia, Nate Miller, Frank Shikarsky, Guy Fleury, T. Smith.

Each of them has its own flavor and improvements.

I have a few sketches for the following versions, which are variations of v1.3,

but with a different universe selection based on my Quantopian pipeline experience:

v1.7

9882_1612512725.jpg

v1.8

9882_1612512810.jpg

v1.9

9882_1612512862.jpg

There is a shortfall in my time budget, so I'm leaving their final development for later.

I want to thank Jared Broad and his team for giving us the opportunity to significantly improve this algorithm.

Thank you all for your successful cooperation!!!

My regards to a PhD who was here, not to share algos.

   

 

 

 

 

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Update Backtest





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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.


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