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For data analysis, particularly for large data analysis project, pretty much most of the top quant hedge funds and a lot of the banks are using Python (over R) for a couple of reasons, although many still have bits and pieces of R for specific packages or functions (I work at a bank and interface with quite a few quant hedge funds on data analysis): ...

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I've used both R and Python with Pandas in a professional quantitative financial work to do both large scale and small scale projects. I would strongly recommend Python with Pandas over R for most new projects in the field especially in time series analysis. While I don't dispute vonjd in that you will find more libraries in R with algorithms on the ...

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If you can add linear constriants (as you can do in quadprog) then you can formulate $w \mu = c_1$ as linear constraint, no matter what $\mu$ is (and first delete it from the objective by setting the parameter to zero. The only problem is the one norm. Let my clarify, this is: $$\sum_{i=1}^n |w_i| < c_2$$ Thus you allow for short sales but you want to ...

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I wonder if it's possible to use solve.QP from quadprog by using dummy variables. One dummy variable $y_i$ would be used for each $w_i$, each $y_i$ would be constrained to be greater than zero, and the leverage constraint would be applied to the sum of the $y_i$. Problem formulation would look like $$\text{min } w^tΣw$$ subject to the ...

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Yes, the function does not consider cases when the price is flat. The solution is very simple. Look at the OBV2 function below. The series from OBV and OBV2 are highly correlated, but the strict definition would be higher (smaller) depending on the market evolution. In the QQQ case that difference is about 50% 1. You could find the maintainer here: ...

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This is interesting because I see another trend: Matlab is being replaced by R, but I guess this is another story... :-) I use R for my academic (I am also teaching this stuff) as well as my consulting work (I am mainly working in the $\mathbb{P}$ area, with some excursions into $\mathbb{Q}$). I tried Python but it didn't work for me. I think the main ...

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It is not about estimating those equations via PC. There are various methods to estimate the latent factor fth, one of which is principal components. They have asked us to use that. Series(z) in those equations is observed data so we use the estimated fth and observed z to perform the OLS as suggested AR(3) or ARMA(1,0,3) would make the residual series ...

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In the paper you cited in the question, the equation (1) is not the equation of state in kalman filter model, but an $AR(3)$ estimated via OLS as shown in Stock & Watson (2002). What the authors estimated in the paper using the Kalman filter is the latent variables $f_t,_h$ and the relative lags through which they estimated both the equation (1) and ...

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