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14

Sorry for not being able to give more than one hyperlink, please do some web search for the project pages. Portfolio optimization could be done in python using the cvxopt package which covers convex optimization. This includes quadratic programming as a special case for the risk-return optimization. In this sense, the following example could be of some ...


11

I don't know why it was removed, but the R package "orderbook" was available: http://journal.r-project.org/archive/2011-1/RJournal_2011-1_Kane~et~al.pdf http://cran.r-project.org/web/packages/orderbook/index.html In the IBrokers package, the function "reqMktDepth" is used for streaming order book data. ...


9

Aside from Zipline, there are a number of algorithmic trading libraries in various stages of development for Python. From the commercial side, RapidQuant looks very interesting though I haven't tried it yet. It's from some of same developers that brought us the excellent Pandas data analysis library. I think Wes McKinney (Pandas's main author) is ...


7

So one such visualization package is demonstrated in http://www.tradeworx.com/movie/booklet_demo/temp/booklet_demo2.mov. AFAICT it looks like a tk script. Trading Technologies (TT) sells another visualization tool. But TBH writing your own tool takes a few hours and allows you to focus on what information you are interested in finding.


6

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 ...


6

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 ...


5

http://lobster.wiwi.hu-berlin.de/forum/viewtopic.php?f=4&t=30 R code, pictures and discussion, it's easy to modify it


5

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 ...


3

Firstly, you'll probably be directed to consider Zipline. It's worth a look but I don't think that it's a good starting point, since: Quantopian's developers don't have a financial background and it shows through in the Zipline source code. Zipline is dreadfully slow if you compare it to any commercial platform with backtesting functionality in a compiled ...


3

You said:"I understand that the generated ticks will be generated using interpolation (so they won't be exacts)". You are very optimistic, they will not only be far away from being exact, they (the tick data) will be completely removed from reality, the only parameters known for the tick data will be boundary conditions, such as open high low close. You ...


3

It doesn't matter if you use *100 or just pct_change, as long as you are consistent. However, in practice, due to underlying floating point numerical instabilities in the underlying optimization algorithms/default tolerances used in scipy/arch, having the returns expressed in %, i.e. multiplied by 100, will have a better chance of converging during the ...


3

TradeStation offers python support via their WebAPI. Check it out here: http://tradestation.github.io/webapi-docs/


3

I reproduced Ledoit and Wolf's experiment outlined in their paper "Honey I Shrunk the Covariance Matrix" in Python which includes an implementation of their method to shrink the covariance matrix (can be found here see the get_shrunk_covariance_matrix() method on line 417). All the code for the entire thing is on Github here. I make use of the cvxopt module ...


3

I know this is an old question, but Wes McKinney, the developer of pandas (mentioned in another answer) is releasing a new Python package called RapidQuant that I think might meet the OP's stated needs. It appears to include both non-standard risk definitions and portfolio optimization. However, it is not open source. While the OP didn't specifically mention ...


2

I came across B/View which is a Java application that visualizes the order book for a single stock on a single day. It encompasses some of the basic features I would expect in such a tool. It appears to be more a demonstration than a general purpose tool.


2

I know of no broker that provides an official, supported Python API. If you are at Interactive Brokers you can consider using their FIX gateway, but that comes with additional cost. QuickFix provides a Python API.


2

possible update: http://pmorissette.github.io/bt/ based on http://pmorissette.github.io/ffn/ both were easily installed and somewhat usable for a novice. would love some examples other that github documentatiion


2

def bbands(price, length=30, numsd=2): """ returns average, upper band, and lower band""" ave = pd.stats.moments.rolling_mean(price,length) sd = pd.stats.moments.rolling_std(price,length) upband = ave + (sd*numsd) dnband = ave - (sd*numsd) return np.round(ave,3), np.round(upband,3), np.round(dnband,3) sp['ave'], sp['upper'], ...


2

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 ...


2

You will need an entry and then a "Grouped" stop loss and take profit (one cancels other). An implementation of this exists in quantstrat in R called ordersets. Documentation and source code can be found here: https://r-forge.r-project.org/scm/viewvc.php/pkg/quantstrat/R/orders.R?view=markup&root=blotter You will unfortunately need to port this and ...


2

Quandl has a python api: https://www.quandl.com/help/api and free stock fundamentals (some) https://www.quandl.com/help/api-for-stock-data


2

Both R and Python can do this very nicely. For Python you would need the pandas package and its dependencies. pandas has a lot of basic statistics, but for more advanced statistics like it looks like you want to do, you can use the statsmodels package, which can work directly with pandas data types. It can also download the csv files directly off the ...


1

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 ...


1

Go talk to Fincad. Here is their page on integrating with scripting languages: http://www.fincad.com/news-events/assets/pdfs/mar07/using-fincad-developer-scripting-languages.pdf Their analytics libraries include bond analytics, and they have a spreadsheet product so you can test methods and results before implementing them. Disclaimer: I work for a ...


1

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 ...


1

Try to plot the rolling mean against your quotes for SP and see if it makes sense. Although you line of code to compute the rolling mean is correct, there might be something wrong in the data that you pass as input.


1

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


1

There is a module called visualize-wealth that provides: Documentation auto-generation capability with sphinx Portfolio construction methodologies in 3 ways (trade blotter, weight allocation frame, and static allocation series) All basic statistical measures, including many sophisticated ones such as CVaR, Mean Absolute Tracking Error, Cornish Fisher ...


1

I have also been searching for algo trading in Python. According to my findings: there are many such librairies available, open-source or proprietary, they are all built quite specifically. as a result, when you know how to use one, it is the only one you are able to use. their stage of development is quite heterogeneous and future uncertain, eg what did ...


1

You can check also QSTK It's an open source library developed by Georgia Tech and used in a Computational Investing course.



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