# Tag Info

15

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 ...

14

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 ...

12

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 ...

12

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 ...

11

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 ...

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. ...

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.

7

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 but 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): Earlier ...

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 Instead of wild guesses about R's/python's future in the community, here some facts: The following query on StackExchange Data Explorer counts the number of questions that have <r> or <python> tags. If you scroll down on one of the three webpages provided below, you can see a graph with data on a monthly basis. You can easily run this query on ... 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 4 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 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 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 ... 3 For the tasks listed, both Python and R perform very well. There are some packages in Python not in R and vice 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 ... 3 Ok, this is a partial answer.. points for anyone who can improve. This will not work for the current month if we've passed the IMM date already. eg. U5 yields 9/17/2015, while it should instead return 09/18/2016. def getIMMDate(IMMcode): '''Takes 2 digit IMM code and returns effective date as datetime object''' from datetime import datetime, timedelta ... 3 What you need is more mutual information rather than Shannon entropy. It is dedicated to capture the influence of one variable on another (you can think about it as a non linear version of Pearson correlations). They are closely related since the mutual information I between two variables X and Y reads:$$I(X;Y) = H(X,Y) - H(X|Y) - H(Y|X) where $H$ ...

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

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

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

I calculate duration in Python using numpy, it's nice and simple: def durations(cfs, rates, price, ytm, no_coupons): import numpy as np mac_dur = np.sum([cfs[i]*i/np.power(1+rates[i],i) for i in range(len(cfs))])/price mod_dur = mac_dur/(1+ytm/no_coupons) return mac_dur, mod_dur

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

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

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 ...

Only top voted, non community-wiki answers of a minimum length are eligible