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I guess what they are trying to say here is that, assume you have two time series $X$ and $Y$ which are exactly the same i.e. $X=Y$, the correlation is : $$\rho_{X,Y}= \frac{Cov(X,Y)}{\sigma_X \sigma_Y}\overset{X=Y}{=}\frac{Cov(X,X)}{\sigma_X \sigma_X}=\frac{\sigma_X^2}{\sigma_X^2}=1$$ Now assume a time series $Z=2 \cdot X$, you have: $$\sigma_Z=2 ...


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I'd put this down as a comment, but don't have the reputation to do so. There is (or at least used to be) a two part MOOC course over at Coursera by one of the developers of QuantSoftware Toolkit. This is not an endorsement of the course or the software, just a statement of fact (for the record, I did do a part of the course, but found it too simplistic and ...


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As far as I know the Newton method is the preferred method for yield calculation. Two ideas to optimize the loop spring to mind: Run the loop in parallel. Use the last yield as starting value. If you have a good guess the number of iterations necessary per optimization is reduced significantly. How to get the most out of the previously calculated yield ...


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If you don't know the meaning of the other matrices, I'd look more at the docs and the definition of the quadratic program: http://cvxopt.org/userguide/coneprog.html#quadratic-programming This is also an example from the book: http://www.ee.ucla.edu/~vandenbe/publications/mlbook.pdf And there is a good deal of explanation there. Finally, if you don't ...


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I currently use a combination of matplotlib and Oanda's FX API. Their API is REST based, and would essentially allow for any type of library to handle calculations. A python wrapper for the Oanda API is on github



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