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9

Non-disclosure agreements work on the legal side but not in reality, no agreement prevents someone with intent to still steal code or ideas. Protect core code in obfuscated code bases, through APIs installed on the local machine or have it on a server that others do not have access to to and provide access through function calls. Make sure the local ...

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I am not sure they are comparable as they serve for slightly different purposes. In robotics (specifically vision), Hough Transform is used for objects (or shape) detection. This can subsequently be used for objects tracking, but Hough Transform has no prediction phase. On the other hand, Kalman Filter is a two phase algorithm; measure and predict. With ...

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CUSIP and ISIN codes are issued in the US by CUSIP Global Services, run by S&P. While the data structure follows a certain logic, there is no way to extract relevant security information from the number. You generally have to rely on a data vendor for specific data such as the maturity date for a bond. In the case of US Treasuries, however, the Treasury ...

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CARA Utility function $u(c)=\frac{-e^{-ac}}{a}$ for $a>0.$ Expected utility $E(u(c))=\int_{-\infty}^{\infty} u(c) f(c) dc,$ where f is a density. Example f(10)=0.3, f(20)=0.7, else f=0 and a=2. Then $E(u(c))=0.3\times u(10)+0.7\times u(20)=0.3\times \frac{-e^{-2*10}}{2}+0.7\times \frac{-e^{-2*20}}{2}$ Now, for normal density ...

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If you haven't already, I would check out Nikolaus Hautsch's book: Modelling Irregularly Spaced Financial Data [1]. I have just started reading it, but it looks like he devotes a good portion of the book to going through real world applications of ACD models. I work at a market maker and spend a bit of my time analyzing our liquidity, so I thought it could ...

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Well, Structurers are simply guys that actually have something to sell. They (usually) try to identify if their clients have some complex balance sheet problems, or risks to hedge, or returns to get, and propose them Structured Products" that can fit those problematic. Regards

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do input attributes need to be scaled? No. It is not required. It is only a heuristic [1]. It is primarily motivated because of the following: From the Feature Scaling article: Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization. For example, the ...

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Aside from TikZ approach that you've found, these scripting languages would work: MetaPost PSTricks Asymptote MATLAB, see cfplot Mathematica, see Cashflow Vector graphics are most suitable for the task, so if you prefer GUI point-and-click: Adobe Illustrator CorelDRAW Inkscape (free)

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I found a nice example on this page using the pgf/tikZ package for $\LaTeX$. The website is giving the code for $\LaTeX$, if you are using LyX, in order to get the same sample than the website you have to do the following. First edit the document preamble using Document->Settings: \usepackage{siunitx} \usepackage{tikz} \usetikzlibrary{calc,matrix} and ...

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The reason you're seeing the bias is because of the adjustment, commonly referred to the "fudge factor" that they government applies to basically all of their published statistics. This is almost always in the direction to make the numbers rosier, and therefore your error term likely will exhibit signs of positive serial correlation. The standard test ...

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For Black-Litterman you need to have weights of the market portfolio, or a close benchmark, where you know the asset allocation. The common approach in practice is to find a large fund with a low tracking error for your investment universe. Depending on your goal it may be enough to have the allocation over classes of your fund, which will allow you to ...

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For algorithms/strategies/indicators, the way this is normally done is you describe the general algorithm, but leave out the parameters. They are then taken from an settings file. So we do all the testing, including acceptance tests using x=12, y=18. But then you run it yourself using your secret numbers, x=10.56 and y=21.22. That is in the context of ...

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Break the code into parts. (unit tests, error checking, algo) Decide what code is sensitive and what code can be shared. Create a "fake" version of the sensitive code for testing. Have the programmer work on the part that is not sensitive. Code the sensitive part (the algo) yourself. (Example: There is no reason a programmer cannot work on the part of the ...

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Mark Joshi has a great collection of tips and interview questions on this topic. Edit: A bit harder to spot, so here it is directly: http://www.markjoshi.com/downloads/advice.pdf

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Van Tharp addresses expectancy in his book, Trade Your Way to Financial Freedom. Here is his definition of expectancy. $\frac{winPct * winAmt - losePct * loseAmt}{trades}$ I would recast your trades as follows: Won €1 Lost €1 Won €3 Won €3 Your winning percentage is 75%. Your losing percentage is 25%. Your winning amount is €7. Your losing amount is ...

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