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A variety of powerful, many-core processors are beginning to make their way into the the hardware acceleration space that was previously completely "owned" by FPGAs. Companies like Tilera, Adapteva, and Coherent Logix all provide these processors here in the US, with Enyx from France also making inroads. The true measure of effectiveness of these massively ...


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I want to highlight the “digital signal processing” (DSP) block with ALUs. Today’s FPGAs have hundreds of programmable DSP blocks – the very largest having thousands. Now, suddenly, you have thousands of small processors at your disposal, all able to perform calculations in parallel. This is vastly in excess of parallelism provided by the Xeon Phi or GPUs. ...


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got an answer from one of my pals, thought it might be interesting to share it here. The reason why we often use the normal distribution is because the distribution will be stable regardless of the number of samples (central limit theorem). Imagine you had a normal distribution after transforming x amount of samples, and across time, u get more variables ...


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Well the answer depends on what are you considering a fee? Do you included per trade regulatory fees or just exchange fees? Many exchanges will pay you for being the passive side of a trade, so technically the fees in that case are negative. For the big exchanges, I'm not sure that you can negotiate the fee's. I'll confess I've never tried and the ...


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Short answer: It offers some degree -- and in many cases, a greater degree -- of comparability between two types of data (different assets, returns, etc.) Long answer: You may already know this, but keep in mind that "normalization" can mean different things (see this question). There are various methods and purposes for normalizing data (financial or ...


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In Hamilton's book there is a chapter on Spectral Analysis. It is equivalent to Fourier Analysis of deterministic functions, but now in a stochastic setting. Intuitively, it is similar to the 'construction' of a Brownian motion as the limit of a Fourier series with random (but carefully selected) coefficients. Extracting and studying these coefficients can ...


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The direct filter approach (DFA) is a time series filter which is calculated in Fourier space. DFA minimizes the mean square error of a time series $y_t$ compared to a filter estimate $\hat{y_t}$ $ E[(y_t - \hat{y_t})^2] = \frac{1}{2 \pi} \int_{-\pi}^{\pi} |\Gamma(\omega)- \hat{\Gamma}(\omega)|^2 h(\omega) d\omega $ The minimization is done in the ...


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You will find that the level of success you have using Neural Networks (NN) as a tool for financial market prediction is strongly dependent on what initially appear to be some quite subtle factors. In particular: Input data: You mention using "certain technical indicators". I assume that you mean the standard TA set of price-based indicators such as Moving ...



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