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1

My deal is HFT so what I care about is read/load data from file or DB quickly in memory perform very efficient data-munging operations (group,transform) visualize easily the data I think is is pretty clear that 3. goes to R, graphics and ggplot2 and others allow you to plot anything from scratch with little effort. About 1. and 2. I am amazed reading ...


0

It turns out that the Bloomberg Terminal QR function, when adjusting the timezone from Exchange/UTC to your timezone, will convert the time but not the day. Trades displayed via IntradayTickRequest API are correct in UTC time, trades displayed via Bloomberg Terminal QR may be incorrect due to failure to adjust the stated date for timezone adjustments.


0

"some data is offset by a day": you don't give enough detail about what security/data and what time is involved. This may have to do with the difference between the calendar date and the trading session date. For example S&P futures begin trading at 6pm new york time on Sunday. However that is considered part of the monday session. So you have to be ...


2

For the tasks listed, both Python and R preform very well. There are some packages in Python not in R and visa-versa, my solution for this is to simply call R from Python. This allows for the best of both worlds. It is also important to note I do not write any R code other than calling an R library from Python. Calling Python from R does not work equally ...


6

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): ...


8

I've used both R and Python with Pandas in a professional quantitative financial work to do both large 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 bleeding ...


1

In the Johansen methodology there are five models unrestricted constant and unrestricted trend unrestricted constant and restricted trend unrestricted constant and no trend restricted constant and no trend no constant and no trend. As these models are nested they can be tested sequentially using likelihood ratio tests. For the usual sample sizes these ...


9

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 reasons I ...


1

Thanks @Aksakal for suggesting Kalman Filter. Here I provide more details. We will view it as a state-space model: $$ \begin{split} z_t &= A_t z_{t-1} + B_t u_t + \epsilon_t, \\ y_t &= C_t z_t + D_t u_t + \delta_t, \\ \epsilon_t &\sim \mathcal{N}(0, Q_t),\ \delta_t \sim \mathcal{N}(0, R_t), \end{split} $$ where $z_t$ is the latent variable, ...


-1

Try use WiredTiger (http://www.wiredtiger.com/) WT is embedded no-sql solution and can be confugured as column-oriented or row-orinted on table level. Time series data may implemented by using time-sequenced primary key. MongoDB and Amazon using this database for their solutions. Also some of HFT trading systems using this database. Michael Cahill and ...


1

Machine learning is a very wide field. Most often it is used for classification or regression tasks when you have labelled data to train the model. For example you show thousands of labeled pictures with an apple and computer "learns" what set of features gives high probability that picture contains an apple (for example, round, red etc). Now in your case ...


1

My guess is that you may have different seasonalities in the data as per each agricultural commodity characteristics. My guess is that there may be seasonality within the year as per the specific commodity depending if one or more harvestings occur a year. Also due to the specific unceirtanty due to planting, growth season and harvesting times. In addition ...


3

Let's consider the following example: the process is initialized randomly with $\pm1$ and then stays there forever. Seems stationary to me, but it would never cross its mean.


2

Did you try solving for $w_k$? $$\bar{r}_t = \sum_{k=0}^p w_k r_{t-k}$$ $$\bar R = W R$$ Since you probably have $t>>k$, you can solve for $W$ using OLS $$\bar R = W R +\varepsilon$$ -- UPDATE You can try applying Kalman filter. Here, your state evolution is $$r_t=\mu+\varepsilon_t$$. You introduce new vector $x_t=(r_t, r_{t-1}, \dots, ...



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