Tag Info

47

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 ...

31

I can only talk about quantitative trading. As a rule of thumb, the lower frequency you work in, the more econometrics is important, whereas for a higher frequency, the more econometrics becomes useless. (I would still recommend a top econometrician for HFT since they have what it takes to succeed, it's just the models aren't out-of-the-box applicable.) But ...

29

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 ...

25

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 ...

23

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 ...

13

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 ...

13

A simple google search should get your started: I like this one the best because it compares different packages: http://stat-www.berkeley.edu/~brill/Stat248/kalmanfiltering.pdf and here couple more: http://www.r-bloggers.com/the-kalman-filter-for-financial-time-series/ http://cran.r-project.org/web/packages/dlm/index.html http://cran.r-project.org/web/...

13

I would say that most ML methods risk overfitting and it depends very much on the asset class. The only area where more sophisticated ML methods such as deep learning appear to make a major difference is in cash equities, where the feature space is very rich (NLP, news and announcements, corporate earnings, other financials) and the data is relatively good, ...

11

People get this problem wrong because they always end up discussing the theoretical advantages of these languages rather than the practical uses of these languages. Theoretically speaking: Haskell is elegant and has many of the theoretical advantages (language conciseness, orthogonality, parametric polymorphism, ADTs, higher-order functions, smart compiler)...

9

A great example of kalman filtering is in the Kyle Model. I have attached a presentation on the application of R to the kalman filter in the Kyle Model. http://www.rinfinance.com/RinFinance2009/presentations/microstructure-tutorial.pdf Basically in the Kyle Model, a market maker finds the likelihood an asset is ending up at a certain price given that a ...

8

Really you need a degree Reading any one book from the above will not set you up. Furthermore, you will find yourself trapped in a cycle, where really none of the books you suggested can be read in isolation. Taking for example a book on PDEs, you will quickly find you need a lot of knowledge of linear algebra if you want to approximate any of these. For ...

7

You're going to get a pretty broad range of answers with this kind of question, but I'll throw in my two cents. I'm not going to answer your question about the "next big thing" in programming languages, because that's just an opinion survey. Instead, I'm going to describe to you the characteristics of a few popular (and mature and well-supported/documented) ...

7

@user2763361 has a very thorough list of useful econometric topics for quantitative finance. I would add missing, mixed frequency, and irregular data as major issues that I'm either constantly dealing with or begrudgingly ignoring. Seasonal adjustment is important too for some data (like electricity futures), though the subject is also related to his ...

6

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 ...

6

MF is linked with physics mostly because it solves the same PDEs (Black-Scholes equation is a certain type of Schrödinger equation for instance). As for the specific links you mentioned : Lie Algebra : Magnus expansion (to build fast approximation of time dependent ODEs like those arising in credit risk) Differential geometry : link with Varadhan ...

6

Also in the high frequency / medium frequency field here. I received a "mixed" consensus regarding the use of R and its prevalence in the field (specifically HFT). Speaking with someone who works in the equity option industry at a relatively small proprietary firm in San Francisco, I was told, "R is a legacy language". However, speaking with someone who ...

6

Your questions is unclear but I guess you mean that for the return of stock A you find a model $$r_A = (0.5, 0.75) (r_F^1, r_F^2) + \epsilon_A$$ where $r_F^i$ are the factor returns and $\epsilon_A$ is an uncorrelated error. Let us denote $e_A = (0.5, 0.75)$, the exposure of stock $A$ to the factors. For $B$ you have  r_B = (0.75, 0.5) (r_F^1, r_F^2) +...

6

The kind of jobs a quant would do has changed a lot since the crisis. I wouldn’t say there is more or less demand for quants, just that there is demand for them to do different things. For example, Certain derivatives and structured products are less popular than they were before the crisis. For example, exotic equity or fixed income derivatives see much ...

5

I recenlty worked on a similar problem and solved it with the help of Quantlib library. Assuming you are working with EUR and USD: get cross currency (xccy) swap data EUR / USD. You want to know how the xccy is collateralized and if Mark-to-Market resets apply to the USD leg. get interest rates swaps fixed vs ois / 3m / 6m in EUR and USD build USD/FedFunds ...

5

The right amount of confidence and courage to take risks with other people's money without shading into overconfidence and bad judgement. Especially coping with the emotional pressure of losses without losing your head and doing the wrong thing. It also helps to do mental arithmetic quickly and accurately and have a good short term memory for figures, all in ...

4

It is very hard to answer this quiz as people might be good at different at tools. For example, if you are good at VBA, then you can achieve the same effect compared to R in most cases. The following parts are the reasons why I prefer to R based on my own situation. 'package'. This is the most obvious strength of R over Excel in terms of convenience. You ...

4

The following paper gives you a step-by-step presentation of how to use the Kalman filter in an application in a pricing model framework for a spot and futures market. Everything is explained using Excel: A Simplified Approach to Understanding the Kalman Filter Technique by T. Arnold, M. Bertus and J. M. Godbey

4

Just an update on my playlist, It has 33 videos now, roughly 3x more vids. I have included some more general economics and machine learning and programming vids, which have relevant applications in Q finance. https://www.youtube.com/watch?v=jXFNpDcYOxM&list=PLqMiStH7exaXmQqV7y-tg68f2ZYZK3Yur

4

To test your programming skills, try QuantLib. Can you do interest-rate modelling with QuantLib? Can you debug the 10-level C++ template? Do you know how to use day count? Do you know how to use business calendar? Do you know how to link a forward curve with a LIBOR market index? Do you know how to calibrate a model? If you could, you have proven yourself a ...

4

So you can get depo and swap rates from markit daily, at links like this: http://www.markit.com/news/InterestRates_<cncy>_<yyyymmdd>.zip i.e. http://www.markit.com/news/InterestRates_USD_20170105.zip and there's a spec for it here - though that's from 2009 so may be out of date, maybe you can find a more up to date one someone on their site, ...

4

The topics are relevant, so if you find something you don’t understand then would be worthwhile brushing up on the maths. It is unlikely that someone will ask you the exact same question though, the questions might be on related topic or sub part of the questions or a topic that is not covered in the book, but you can use these as an assessment of your ...

4

If the birthday would have been in June or Dec there would have been a chance that day-knowing C would have known it, because there are unique possible days-of-months in the list for possible birthdays in those months (Dec 2 and June 7). The only way the month-knowing person referred to as "you" in the problem could know that there is no chance for C, is ...

3

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.

3

As an overview, Expected Returns, by Antti Ilmanen, was recommended to me. He has a preference for data over theory, so it will appeal to quants. The book is longish, and got a bit heavy at times, but he covers all the investment products and all styles of investing. The biggest problem might be that it is now 3 years old, and was heavily influenced by ...

3

Some advantages of R over Excel: R is a scripting language, which allows to record a data manipulation script once and reuse it multiple times. R, as a [scripting] programming language is much more flexible than very limited Excel's GUI. In fact, R has become a de facto statistical programming environment, which delivers most recent statistical techniques. ...

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