# Tag Info

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

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 ...

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 ...

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

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 ...

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

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 $H$ ...

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

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 ...

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

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

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

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 ...

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

You can get this using a pandas rolling_max to find the past maximum in a window to calculate the current day's drawdown, then use a rolling_min to determine the maximum drawdown that has been experienced. Lets say we wanted the moving 1-year (252 trading day) maximum drawdown experienced by a particular symbol. The following should do the trick: import ...

2

It is all in the code:: Rcpp::List rl = Rcpp::List::create(Rcpp::Named("value") = opt.NPV(), Rcpp::Named("delta") = opt.delta(), Rcpp::Named("gamma") = opt.gamma(), Rcpp::Named("vega") = (excType=="european") ? opt.vega() : R_NaN, ...

2

PYTHON I have found this class from the statsmodels library for calculating Garch models. Unfortunately, I have not seen MGARCH class/library. Below you can see the basic information about the garch models in mentioned class from the statsmodels. Probably you have to implement it by your own in python, so this class might be used as a starting point. ...

1

I'm guessing your best bet would be to get setup with an SFTP account with Bloomberg. Just contact your rep to get the ball rolling. We upload 10's of protfolio's 10's of times a day. I don't think there is an actual api for pulling data from PRTU as I'm not sure exaclty what you'd pull other than the last upload time.

1

When doing series like this in Python, I usually just add 1 to each return, then multiply across these sums for cumulative returns. Such as, if my returns over three days were -5.2%, 2.1% & 4.8%, then the values I would store would be: 1 + (-0.052) = 0.948 1 + (0.021) = 1.021 1 + (0.048) = 1.048 Then, to calculate my cumulative returns, I ...

1

highOutput = [] lowOutput = [] lsth = [None]*window lstl = [None]*window for i in range(0, len(highValue)): maxValue=0 lsth.append(highValue[i]) lsth = lsth[-window:] for j in range(0, len(lsth)): if lsth[j] > maxValue: maxValue = lsth[j] highOutput.append(maxValue) for i in range(0, len(lowValue)): ...

1

Native support is very limited. TradeStation's WebAPI pretty much works with any language because it is wrapped in HTTP calls using RESTful. If a platform has an API that supports std C/C++ interfaces, you can write a wrapper to extend the API to python. Search for "Calling C from Python". It is more work to code, but otherwise your choices are very ...

1

True this is a stackoverflow question but have you tried the fool around with the package Pandas? You can do in Python import pandas as pd data = pd.read_csv('filepath/file.csv') That's the easiest way.

1

You need to use a groupby, timegrouper, and its annual option, and take the first value in each group. For example, if I grab yahoo data: import pandas as pd import pandas.io.data as web # Grab S&P 500 data going back to beginning of 2011 SPY_Dat = web.DataReader('SPY', 'yahoo', datetime.date(2011,1,1), end) # Convert to annual data: SPY_Ann_Dat = ...

1

The major advantage of Python (w/ pandas) over R is that Python supports OOP (object-oriented programming). It makes sense to organize a large code base using a hierarchy of classes. Python also supports the notion of polymorphism so that we can use well-known design patterns (e.g., Strategy, Observer, etc.) in our code.

1

Just checked in my python script for daily futures data from Interactive Brokers. Maybe it will be useful for you: https://github.com/busygin/ib_data_loader

1

The IB API calls your code asynchronously when there are account updates after you have called reqAccountUpdates. But you have to provide a callback function (handler) for the IB API to call. Looks like from the [ibPy documentation example] (https://code.google.com/p/ibpy/wiki/IbPyOptional) and how the Java IB API is defined, you want to call ...

1

In addition to the above answers - You should be very careful that you do not introduce survivorship bias in your creation of indices and choose your data source carefully to remove such bias. For example, Yahoo Finance only contains currently-listed securities.

1

Take a look at pinkfish. Disclaimer, I am the author. http://fja05680.github.io/pinkfish/

1

A (now) very mature (imho) Python backtesting framework is "backtrader": https://github.com/mementum/backtrader Blog posts with samples and new developments: http://www.backtrader.com The documentation on readthedocs can be reached from any of the above links Some features: Can run in (pseudo)event-mode (called 'next') or (pseudo)vectorized mode (called ...

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