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5

I would create categories, and work on risk parity among the categories. Otherwise, variance is not really a good measure of downside risk: Change your risk measure, use a rolling window historical VaR or Expected Shortfall at some horizon that matches your investment style. downside semi-variance could do the trick too if don't want to change your algo ...

5

You can find an exact algorithm with a step-by-step explanation here: https://www.dropbox.com/s/t4fq067kzx26mhw/project_paper.pdf As you can see from the URL it is an archived document because the original site is unfortunately long gone and the tool referenced in the paper with it :-( But it should be helpful anyway to understand what is going on. Notice ...

3

Assume $p_i(x)$ is a payoff of one particular option. You can try to reproduce the diagram using a bunch of options with strikes on the breakpoints (underlying is useless, because its payoff can always be modelled by buy&sell of a certain call and put). Then you can create a system of k equations with n unknowns (number of each kind of option). All other ...

3

How about an O(N log(n)) solution ? To be a viable trading strategy, you often expect them variances to be similar, so just calculate ordinary volatility and put it in an ordered array. Of course that's going to be period dependent, so pick a few arbitrary periods and see which instruments end up being together. Then you get clusters of vastly smaller ...

2

Another reason for C++ is control, or at least the illusion of it. If you really care about what exactly is going to happen and when it is going to happen then C++ is the best option. If you are prepared to put in the effort you can know and control everything all the way down to the metal. Of course the price for more control in C++ is that you often have ...

1

Are you working with futures data (mixing rate futures with equity futures) OR allocating between macro instruments? If so, using non-linear variants of GARCH (GJR-GARCH, TGARCH etc.) are common way to solve your risk parity allocation issue that you might be facing. One common related issue is not that the volatility to a couple of instruments drop ...

1

I know this is a really old question but here is something I ran into while trying to do essentially the same thing. One of the problems that you face when trying to detect patterns using (say) k means clustering is how do you encapsulate a pattern. For example, suppose on a certain day the index goes up 2% over a minute and then goes down 1% over the next ...

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Hello people. This is quite a complex problem if you want to solve this in a computationally efficient way for a rolling window. I have gone ahead and written a solution to this in C#. I want to share this as the effort required to replicate this work is quite high. First, here are the results: here we take a simple drawdown implementation and ...

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