Yves Hilpisch on Quantitative Finance

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We are recording today on December 30th 20 15, and your host as usual are Tobias Macy and Chris Patti.

Today, we are interviewing Yves Hilpisch about quantitative finance. Yves, could you please introduce yourself?

Well, first of all, thank you for having me. Yeah, my name is Yves.

I said it already.

I'm German.

My background actually is

in math finance. I wrote my PhD thesis, oh, already back in 2000. I finished my PhD,

actually, and I've been working in the industry since then.

So my my focus today is actually that we're working, with Python for finance, actually.

I founded the company called the Python Quants,

and there we focus actually on supporting,

small, medium size, as well as the biggest institutions that we have in the financial industry,

like hedge funds, big banks, exchanges,

other data service providers, and so forth. So we're also, doing

conferences in this in this context and then much, much more like running meetup groups in

New York and London, actually. So it's quite diverse, but, actually,

the common field is Python for finance. This this this is what you can say what I'm doing all day, all all week, all year, actually.

Very cool. So,

Ios, how did you get introduced to Python?

Well, this is actually the incident. I was working for a technology client. It's, well,

back

more than 10 years.

Back then,

I was working for this technology client in a consulting capacity. I wasn't involved in technology itself,

but there was this particular project where I learned to know Python. And I thought, well, this might be actually good language to do quantitative finance.

Back then,

maybe only a few have been using Python in this regard.

When I started using it and I talked to other people about it, they always say, well, Python is a very well suited to attack the typical problems of quantitative finance because it's an interpreted language. It might be too slow, or they even say, well, it is too slow. You can't use it. But I think over the last 10 years, we have come a long way in the Python world.

All these strategies, I would say, they are no longer

true or valid these days. We have the amazing Python ecosystem that make Python a very good choice. So but again, it was just by incident. I like the language from the first day that I saw it. I thought it's so close to the mathematical syntax that you used to use in in, in quantitative finance. I said, I I thought simply this must be a perfect match, and I guess,

history, at least the last 10 years,

have provided lots of support, for my,

back then it was just a vision or it was just like a feeling that this might be a good fit. But the biggest institutions in the world like Bank of America Merrill Lynch, they're heavy Python users these days. They prove right,

that Python is indeed a very good choice for finance.

Yeah. It's definitely pretty amazing how

quickly the ecosystem has grown up around the use cases for doing

quantitative analysis and various forms of data science and other kinds of number crunching within the Python language and all the different,

sort of native libraries that are that have bindings to help alleviate that issue with speed that you mentioned.

So can you explain what quantitative finance is and also perhaps differentiate that from algorithmic trading?

You know what? Quant finance, I, at least as far as my thinking goes, I see

3 major subdisciplines

under quantitative finance.

There are, of course, those people who work on the models, I would say, for example, to value

complex, derivatives

like options or hybrid,

securities or or other structures where you might have embedded options. These are the guys who write down the equations,

who try to solve them by,

mere frame power if you like.

Then the second pillar from my point of view is computational

finance where I say, well, now we have the model, but we have to implement that,

by the means of for example, Python or typically what they use these days is c plus plus due to the speed and so forth. This is the second pillar, the computational part. And then, of course, we have, what I call a financial data science where this this whole financial logistics,

topic that you need to cover in order to get your hands around the massive amount of data, actually.

This is actually from my point of view, the closest part to algorithmic trading in that sense,

that you have to process masses of of data.

You can speak of big data in this regard, but, typically, I understand that the big data more or less, unstructured data. What we see in quantitative finance is actually usually very structured data, which you can store in tabular form. So there's no need to attack these,

let's say, very structured

datasets, which might be big with the typical means that are applied, for example, by Google or the Twitters of the world, where the problems are completely different. Speaking for example of the of the retail banking side, you have problems similar to the ones that are faced by Twitter and Google. But on the quantitative side, it's usually very structured data. Just think of kind of a table, a SQL,

based table or, for example, a simple Excel spreadsheet where you have your numbers in such

a 2 dimensional

spreadsheet form. This is data that we face in quantitative finance. But nevertheless, the financial logistics

part is, equally,

as important as in the other disciplines.

So,

again, I see these 3 pillars, the theoretical part, the computation finance part, and the financial data science part, if you like. At least this is my thinking.

And you alluded to it a little bit, but I'm curious how common it is for Python to be used in an investment bank or a hedge fund for the purpose of quantitative finance or possibly other uses as well.

Well, I think,

coming from,

yeah, a history where Python hasn't played a role in the quantitative finance part, We usually see if finances is if Python is used at all kind of a blend of technologies,

that they use in quantitative finance.

I said it before,

major banks these days have implemented the analytics libraries, their derivatives analytics, their risk analytics libraries in c plus plus due to the speed advantage that, these,

compiled languages provided,

10, 20 years ago, actually.

But again,

you have to differentiate this, to maybe new startup funds. Like, there are many, many hedge funds that you can get started,

in 2015

where many big shops, are closing. The funds actually.

This this is 1 of the major headlines this,

this year about the hedge funds.

And these new shops, they they saw the potential for Python to provide a unified

technology platform,

in a sense that Python might not be only used for prototyping purposes or to script something up quite quickly, but also for kind of the core technology and the core,

analytics speed, risk analytics, derivatives analytics, or for the purpose of implementing trading strategies and so forth. So it is used, but I think to a different degree depending on, on the, the companies you're looking at. I mentioned the example before bank of America Merrill Lynch,

their their major, trading and risk management platform, which is called Quartz. The last number I heard is that they have about 12, 000, 000 lines of Python code in production there though. It might be 1 of the biggest financial applications in the world and they're obviously heavy, Python users. Others might be using it only to a smaller extent but again, all the big institutions these days be it hedge funds, be it asset managers or be it the big investment banks, they're more or less using Python but again to a different degree maybe.

And

just to dig a little bit deeper into the factors that contribute to the choice of whether or not to use Python in that kind of environment,

I know you mentioned,

having it as more of a unifying language, but I'm curious if you can just dig a bit more into that.

So the unification,

power of Python, I think, is 1 that is,

yeah, dominant these days. So when you when you start out, then you can decide on your own. Again, the example,

a team coming out of a big investment bank, they set up shop with their new fund, their emerging managers,

they can decide on what technology to start with. Obviously, for a big investment bank, this is not a choice when you say, well, I have millions of lines of code, in c plus plus. There's no point, no business case in in migrating that to Python. But if you start out a new, I guess this is kind of, the right thing to go these day to use Python. Many, many do that, these days, actually. But there are other factors as well. I mean, it is kind of a a hip language these days, so many people like to work with Python. And I guess it's also a factor when it comes to recruiting. They just say, well, we are we are innovative. We use Python. You can work in Python here. They are not the old compiled languages that they need to use, that they need to master.

But again,

usually, it's kind of a blend of technologies, and they seldom people these days are writing,

stuff only in 1 language. But Python might be a very good 1 when it comes to,

fascinating

and acquiring people and get the best recruits out there in the market, actually.

Others, I mentioned that before, other factors, might be the ecosystem, of course. Things like pandas, data analytics library

written back then or still supported by Wes McKinney. It's kind of a major library

where I see that people get drawn into Python solely by this particular library. So, when I give seminars or or we're doing or running our conferences, they are

very often people who come from a different background like r or MetLab and they say, well, I'm so fascinated by by what Python has to offer these days. But again, think back 5 years,

we have come a long way when it comes to these libraries like pandas or performance library libraries like Python and Numba, which make, Python really fast. So I think we have lots to offer and this might be in the end, I guess, the the most important thing that we have this particular,

powerful particularly powerful ecosystem,

which I guess distinguishes Python, for example,

from languages like Ruby, which are which is pretty close, in terms of syntax, which is also quite nice. Many people like it. But the ecosystem for doing what we do in quantitative finance is mainly missing in most other languages that you might come up with, when it comes to the beauty of the language itself, but not the powerful or the power of the the ecosystem,

which surrounds the language.

This is definitely something that I think the Python community should consider marketing

a little bit more aggressively in that. I've actually had this discussion a number of times of late.

You know, Python is 1 of those languages that can tend to be polarizing

in terms of if people have really strong opinions about the, you know, syntactic white space or whatever the case may be. And I've just been trying to tell people,

I don't care. Like, even if you hate the language itself,

you know, it does the ecosystem that has been built up around it deserves to be explored because

people are doing really interesting things with Python that just aren't happening in other programming language camps, at least not in the depth and diversity that they're happening here. Yeah. I I completely agree. I mean,

if you differentiate

the the work of a of a developer who's writing, like, production code,

to the work, for example, of quant analyst who is more or less doing interactive

work.

And then you can say, for example, that pandas in and of itself,

but represent kind of

a new even higher language in a certain sense because you can work with pandas alone these days,

which wraps actually all the nice libraries that are available in the ecosystem like, Py tables for HDF5 storage or map lib for plotting or num expression for parallel, numerical

expression evaluation, and so forth. And you can, for example, if you're a non

computer scientist or non developer whatsoever, you can learn pandas as a language in itself, not even taking too much care of what Python has to offer. So I I'm playing with this, thought for quite a while. I'm thinking of kind of, more like executive level, of people who might be interested in learning pandas alone. So what you're saying is is completely right. Even if you if you don't,

like the language itself, the ecosystem around that and pandas from from my point of view is kind of the the prime example in this case. It's so powerful and and you find so many useful things that you might not find in other areas that it's always worth to have a look at least at at at the specialized libraries in this regard.

That's very interesting that you mentioned people coming to Python

the

language.

And

it reminds the language. And it reminds me a lot of the experience that people have had with Ruby and Ruby on Rails where people come to the Ruby community just to use Rails and

for the first while that they're using it don't even necessarily realize that Ruby is its own distinct entity

apart from that framework. So

it's it's an interesting parallel to have that happening with pandas and using that to bring people to Python.

Yeah. Exactly. Exactly. I mean, usually people have problems, and they want to solve their problems. And,

only

the second instance they care about the means in a sense that they are not religious about that. So

again, the example is pandas

has been modeled more or less in the beginning after the data frame, which is which is provided by r. But more and more people are coming to Python to to pandas these days because, the data frame that we have in pandas is much more powerful than the data frame that is available in R. So it's kind of,

the upside down if you like that people now are coming to us because we have something which was modeled in the 1st place after something that the our community had available for quite a while. So, this is, also illustrative from my point of view for for the long way we have we have come and what we have to offer these days also for people, yeah, with a with a different,

background and and, yeah, a different education in this regard, actually.

So, Yves, are there any performance bottlenecks or other considerations inherent in using Python for quantitative finance?

Yeah. Of course. I'm always saying, you can write

really slow Python code. This is this is for sure.

And there are many, many straps that you can tap into. The problem with Python,

is that you can achieve

the same result by, I don't know, a multitude of different ways because we have multiple paradigms that you can use. You have different idiomatic ways that you can choose,

to reach the result and so forth. So for many people coming to Python,

with a different background, it might be a little bit tricky to get it right, in the first place. For example, the major example is that many many algorithms

in qualitative

finance, when implemented in any kind of,

language,

programming language, are based in the end of of a loop or even a nested loop.

And everybody knows that, Python loops are slow. And therefore, some are reasoning that, well, part of the finance is in the end about looping or nested looping, and Python is bad at looping. So Python must be bad for quantitative finance. But, again, when you write the programs and the algorithms right, you will, try to avoid writing loops on the Python level as far as possible. For example, using

the powerful vectorization

capabilities of NumPy,

where the looping is so to say,

delegated to the c level.

So so to say in a machine room of NumPy, actually, where everything happens at the speed of c code because NumPy is more or less completely implemented in c. There's also a 4th round part, but usually what you're using as a default is the is the c part of the implementation of NumPy of the n d array object. And this is then in the end quite,

quite fast actually, but you have to know that. People coming from r or for example, MATLAB, they might know that. But people coming, for example, with the c plus plus or c sharp backgrounds or or Java where they used to write their, their loops. Yeah. They might be inclined to to write their loops in Python as well. And this might be, for example, a track where you can tap into where you say, well, performance is is all about what I what I expected or what we need here for this particular application.

And this might be tricky in the 1st place, but I guess a good training or there are so many tutorials available on the web to teach that stuff. This might

get you around the the traps that I was mentioning before. And in the end, if this is not enough, let's say for example, working with the umpire is nice, but my my special problem cannot be cast into a setting where full vectorization

or for example,

I cannot afford to allocate too much memory,

for the vectorization. Then there are other libraries I mentioned, for example, Numba before, which might help you, for example, with your with your specialized,

loops and asset loops. And it's a very powerful tool to, dynamically compile

your pure Python code if you like to a machine code, and then you get a speed again, but in a more or less than customized fashion. So there are

bottlenecks if you like, if you're doing it wrong. But usually, if you know how to do your stuff for qualitative finance, for computational finance,

then Python might be as fast as any other language that, you use usually for these,

particular topics.

You asked before

about algorithmic trading or if you think for high frequency trading. I mean, when we speak of the milliseconds that matter,

in these particular areas, then Python might indeed not be the right choice. So,

I always differentiate when I say, well, for the typical, big data problems,

Python is hasn't been developed actually,

also not for the for the faster things in the world, but Python can interact with all the technologies that are used there, both in a big data,

world and in the high frequency and then the

and then the in the high performance,

area if you like. But Python in and of itself was never designed to attack these particular problems. But again, as a general language, as a general programming language attacking

the regular problems, let's say that the 98% of typical problems,

Python might be, actually a good choice to go for. But again, you have to take care of the idioms of the approaches that they use because otherwise, you might end up indeed with slow, nonperforming Python code.

That's really interesting that you and and a few other people also have brought up the slowness around loops and and iteration in Python. I wonder if in, you know, Guido and and all the other folks sort of, you know, working on python.next

and and and investigating the future of the language, I wonder if any thought is being given towards

how that performance

issue might be mitigated.

I think, for sure, they are working on that. But by the very design,

there are limits to solving this particular issue. First of all, Python is a dynamically typed language, so the interpreter has to decide during runtime what kind of type it faces. This brings a lot of generality, but also, obviously, lots of overhead,

compared to a compiled language where you say, well, this is a float. This is that. This is this.

In Python, you can pass kind of anything, for example, into a function, and then the interpreter has to decide what has passed to the function. And for example, there might be functions that work on a float, that work on an array, and they might even also work on a string object.

This is the nice point about that, about dynamic typing that you get lots of generality, but this brings again, the overhead with it.

On the other end yeah. I mean, it is and is, an interpreted language. And if you speak of the CPython

implementation,

this is as it is. Of course, we have, the other Python versions like the Python project where you have something like just in time compiling. And then we usually see speed ups of like 6 to 8 times, compared to

the compared to the,

interpreted version,

actually. So, obviously, there are projects implemented. But in the end, if you stick to the 2 major paradigms that have a dynamically typed language and you have an integrated 1, then there might be limits to speeding up, the the,

the loops. But in the end, from my point of view for quantitative finance,

what we are talking about, this is actually not a problem. But if you have if you have, for example, a a big application or, let's say,

a module from a from a analytics library, then usually you have kind of 2 or 3 spots only where this bottleneck might arise arise where you say, well, here is kind of my nested loop, which is too slow. And then you can pick that particular part out,

and maybe vectorize it as a first approach or you do dynamic compiling or you do a static compiling using and then interface with the with the compiled version and then the bottleneck, so to say, has vanished.

You don't have to write, like, if you have a 1, 000 lines of code and maybe 5 or 10 lines only might

and to rewrite or to compile, dynamically, statically compile

the 5 or 10 lines of code which represent the bottleneck, and this is kind of my thing. You can start out with pure Python, your profile, your code, you say, well, here here we see kind of,

where where the slowness comes from, the execution, and then you go you go on and and improve this particular part. In the end, you might be as fast as with kind of a completely compiled version of what you have

given. And, just to go a little bit deeper into the weeds for profiling your code, do you just use the standard,

built in profiling module from the standard library? Or are there any other tools that you like to leverage for that purpose?

Yeah. Yeah. There are multiple options available. What we kind of use is kind of

usually the very standard. But

if you have experience,

you usually don't need to,

to run it through a profiling engine,

to say, well, this is where the bottleneck is. The

majority of algorithms in the end look like the same. And with a little bit of experience, you know where the where the where the important parts are, which you have to, yeah, compile in the end. So this is more or less kind of,

more a thing of experience than of profiling in the end,

I would say, actually, for what we do.

So what kinds of background are typical and necessary for getting started in quantitative finance?

I think here, we also have come a long way.

Back then, I would say so in the in the eighties, even in the nineties,

what they call the people who did qualitative finance, they were usually called the rocket scientists, and and the major reason for this was because they have been rocket scientists,

actually.

Many people had their background in physics or mechanical engineering or in mathematics

or or some similar very formal field where they got the the necessary

math education to attack quantitative finance

problems. These days, when you have a look at the educational market, you have many, many specialized degrees,

even called the master of financial engineering,

master of quantitative finance.

So again, we've come a long way in a sense that we have today the the the education industry, the universities that support

education in this regard. And I guess people interested in getting into quantitative finance,

they usually go that route that they do their bachelor and and later on get their specialization with the typical program.

I don't know how many there are alone in New York and in London,

where they get their master or even later on they go on, like, the PhD thesis that she got.

So this is kind of the background,

that is typically, I would say, these days required that you know how to program.

They learn, for example, c plus plus and more and more Python these days, obviously.

And, they get the the math education that they need to to work in this particular field. But, obviously, there are other people

that might come with, for example, a math background or an engineering background,

and they are already in their professional careers, and they wanna get into the field. Then there's also,

a professional education market.

For example, Fitch Learning, they offer kind of the very famous, CQF,

the certificate in quantitative finance. I think since more than 12 years, I, this year, got a lecturer for the program. So they introduced Python as well. Here we see that Python is scanning crawling in this part,

of the spectrum as well,

because,

Raveed Cook, which is the academic director, he said, well, we have to keep up with the times and Python obviously has become a force in quantity finance. So we wanna introduce Python into the curriculum and and here we go. So I'm teaching,

quant finance or actually computational finance. I must I must specify given my own,

subclassification,

based on Python in this program right now. And I guess going forward, more and more Python will also be included in this

professional education program. We also, for example,

discussing, and we will provide this probably next year for the first time to do a certification

in Python for quantitative finance, which is, a little bit shorter, but a little bit more specialized. So for people who say, well, I might have a background, for example, in financial engineering with masters, but I've never done any Python.

But I know for example what the plexigold, stochastic differential equation is for the geometric Brownian motion, then they might learn during this certification, which will be, over 4 days with the with kind of final test, a final exam,

then would then they will learn how to apply Python actually,

in in this particular field. So there are many, many ways. Usually, these days, I guess, people who are interested, they they start out,

studying this at university.

But again, there's also kind of a market,

which provides professional

education

offerings for those who are already in their career and now want to shift focus or want to deepen their knowledge in this particular field.

Yeah. It's interesting how new technologies have given rise to completely new classes of occupation that didn't exist before computers became so ubiquitous and also how

software in general has

allowed a number of people to

reimagine their careers

at various points without necessarily having to go all the way back to school and get another degree or take a significant amount of time off of the work that they're already doing in order to gain new certifications.

So what kinds of libraries or algorithms in Python are useful for the day to day work of a quant?

Well, actually, this is pretty similar to what data scientists use. I mean, data scientists might be a little more popular field for which Python is used.

But we in Quant Finance use more or less the same toolset. So it all starts out with the with the scientific stack as it's usually called,

which is based more or less completely on NumPy, which gives the, and the array object for numerical computations

based on array structures.

Then obviously, I mentioned it quite a few times already, using pandas for data crunching, for visualization for example.

And these are already the 2 major libraries I'm working with on a day to day basis.

Of course, there are much more specialized libraries and so forth, but I guess these 2 are the workhorse actually these days.

I really like, storing data in HDF 5 format, which is a very fast format,

binary format to store data almost at the speed of the hardware, that is available.

So usually even working on a on a small MacBook that I have in front here, I get to, write and read speeds of 500 megabytes per second with Python is already built in.

This is my example that I'm usually mentioning when I say, well, the the HPC part when it comes to IO is usually built in in Python. Other language, they have to,

yeah, crap a little bit more with the input output

part. And there are many others. There's so many so many nice,

libraries and stuff out there, but I think in the end, it

very problem that that you are attacking.

But again, I can only stress the 2 ones that are the workhorse. It's it's, NumPy and it's Pandas. And I mentioned already the the the,

performance,

libraries, which might be useful. But this more or less on a case by case basis that you might wanna use number for example when you when you face a nested loop which cannot be vectorized or which you wanna implement in a memory efficient way and all the the other nice things that we have like Thyssen and so forth. So but again, it is and stays the the scientific stack, which is used in almost any kind of scientific discipline or the data science part.

And quantitative finance, at least in this regard, is not that different from the other application areas of Python and the and the ecosystem surrounding it.

So talk me a little bit more about the actual data that is manipulated and used for powering these decisions. I'm curious,

I guess, where the different sources are. So is it all just market financial data? Is there proprietary data that companies bring into play? And also,

how useful or prevalent is semantic analysis of various news sources when

trying to reach decisions about what sorts of trades or investments to make?

1 part I measure also again before is kind of that we usually on the at least on the quantitative finance side, we're very structured data, and the structured data is typically kind of a financial time series Mhmm. Which says you have your day time stamps, let's say, on the on the 2nd March last year at 11 o'clock.

We had, for example, an index level for the S and P 500 of x y zed.

And this then you have, for example, over 30 years, you might have this on a daily basis or on a minis bar basis or even on the second level.

But nevertheless,

the structure of the data is the same. It's typically financial time series.

And these financial time series are generally provided by, at least in the professional context,

by the professional data, providers like Bloomberg and Thomson Reuters,

which, by the way, both of both of them provide Python,

based wrappers around their APIs. So it's very very convenient usually,

to interface with the day to day provider from a Python level.

Actually, it's not where it could be or should be.

I heard many people complaining

about practical issues in this regard, but at least there is the the possibility to interface from Python

to these sources.

But there are other areas as well where you can get financial data, and this is kind of a very nice

development I see in our market,

that we not only have open source, and we have the GitHub, and the Meetup movement, and you name it. We also have open data, which is coming more and more. And 1 of, the bigger players in this regard in our field, in the financial field is Quanda. It's called Quanda,

which provides kind of a unified interface

to I don't know how many open data sources actually.

For example, 1 of my examples I I use on on our platform on Datapark. Io is interfacing with Quandl and analyzing Bitcoin historical data for with regard to the exchange rate to the US dollar, with regard to the number of Bitcoins issued, with regard to the volume of Bitcoins traded, but they and so forth. So this is this is kind of an example of what kind of data you get there. You also get data from Quanda,

for free in an open standardized fashion,

for historical data. For example, for indices, for

commodities,

for foreign exchange rates, and so forth. But not only that, there are wealth of of different data source that you can tap into there. So if you have never heard of it, just give it a try. Have a look at it as a standard rest rest API,

JSON base, and you can easily interface, with Quandl, not only obviously from Python, but with any language,

because it's it's such a nice API that they provide. And this is kind of a major trend, which is, really useful. When I think back when I wrote my my diploma thesis and my PhD thesis,

getting,

even small chunks of data was was really

painful or in the end costly. So I remember 1 instance where I had to pay my professor like

back then when I studied 20 Deutschmarks for a very small,

data set that I needed. So it was much money back then for myself actually. But these days, when when you study in this field, you have so many open data sources that you can tap into.

I think the most popular 1 because it's around since quite a while is the the Yahoo Finance data set where you can for example, from pandas quite easily interface with And if you're, for example, interested in the in the stock quotes of German Daimler Benz or famous car manufacturer or for the German DAX index or to this and for the mix volatility index in the United States, you get the data for free,

from Yahoo, for example.

So, this is kind of a major trend that I see

coming or becoming even more, force in the future that we not have only,

open source, but also open data in that sense that people can, easily work with.

With regard to,

the other part of the question was with regard to semantic analytics. I have seen quite a few talks and I know quite a few people who are working on that.

But I don't have a coherent opinion on that. This is not a field that we are working with. So news analytics is an active field.

There are companies

who exclusively focus on that like Ravenpack, for example, is 1 provider

of, so to say, insights

based on, semantic analysis.

But again, this is neither a field, which I would consider 1 of our expertise nor do I have kind of a coherent view on on how far fetched it's used. I think all the big players in the world when it comes to the buy side, the hedge fund and asset managers, they have have tried it out, but I don't

know

how far they really base in the end their decision to trade on this kind of information. I've seen quite good research results

actually with a percent of their their studies, but, in the end approval lies and using it, to make real money. And there, I I can't mention numbers. So I say, well, this, this or that shop has used it,

to generate, I don't know, 3 percentage points or plus over kind of benchmark or whatsoever.

So I'm not an expert in that. Maybe others, might,

have a stronger views on that.

So

this next question that I had may potentially have been somewhat off the mark, and you'll forgive me if so.

But in any case, is Python actually used to enact the trades? What protocols, APIs, and libraries are used in this process?

The first part of the answer is Python actually used to enact the trades, I would say, for sure. Yes.

But,

I think

depending again on the company you look at, it might be from completely different angles that the input,

comes from the Python script or application or whatever is used there.

So some might even,

implement their,

the complete, trade process based on on on Python.

Others might only use it, for example, to analyze data, come up with an investment idea, and then implement it later on using a different technology.

So, from the 1 end, doing it completely based on Python, like automatons,

iometric,

algorithmic trading to just like being a supportive tool in order to come up with ideas and to back up ideas, so to say.

I can see,

Python,

yeah, used in many many places in in this particular regard. When it comes to protocols, APIs, and libraries,

the first part is not that,

not that easy to answer. I mean, there are

so many different applications, so many different areas where where it is used.

I wouldn't say that there is kind of a a very standard. What we see these days, obviously, is JSON based

APIs. We can easily interact with, for example, online brokers, for example, where they have standardized APIs. But again,

shifting from 1 online broker to the next,

you might encounter a completely different API.

So I don't see any kind of

real standardization in this regard.

When it comes to libraries,

actually,

they are also,

at least when I give my talks at,

Python conferences,

what I mentioned usually usually is that this is kind of I wouldn't say a blind spot actually that we don't have financial libraries.

What we

don't have

really standardized

ones yet or,

I'll say powerful enough libraries yet.

We have the the amazing Python ecosystem we have been talking about already a few times with non Python and so forth.

But there is not kind of when you say, well, which 1 is the financial library I should use when I wanna trade this or that?

This is still missing. What we are working on since quite a while is a a standardized library which you call DX Analytics.

You find all the information on the website just just Google it or go to dxdashanalytics.com.

But this currently has a strong focus on derivatives analytics,

which might not be the focus of the majority of people, who use Python for finance. But I think when it comes to derivatives and risk analytics

and portfolio analytics,

we might, have to offer some special things here. So this is also 1 of my focuses for next year to build out our library and discussion,

right now with, a few bigger partners who might support, our actions in this regard,

to build our DX analytics to more or less kind of a full fledged library covering also back testing, trading, more on the portfolio analytic sides, and and,

including more models like, currently we're working, for example, on the LIBOR market model, which is kind of very complex 1,

but heavily and often used when it comes to the interest rate space.

So in this regard, we have been,

too weak if you like. But again, on the other part, there's the the real estate analytics part, and there the focus is on equities.

I think our library is already quite strong, and there it is also used kind of in practice for for some of our clients where we do kind of derivatives and portfolio analytics on a regular basis have implemented

the parts of their analytics,

infrastructure based on our open source library, DX Analytics.

And there are others, of course, not only ours. There's, for example, the SIP line library, which was open sourced by Quantopian,

which is an algorithmic platform,

and the crowdsourced,

hedge fund. Actually,

they open source tip line, which has a focus on back testing trading strategies, so they are more on the trading side. And they just recently open sourced another library, which they call Pie Folio,

which is more or less about the,

performance

estimation of different trading strategies. So the 2, the supply and the portfolio are closely intertwined. The 1 is for back testing and coming up with the trading strategies, and the other 1 is for,

estimating the performance and giving lots of statistics

for different trading strategies in practice.

And this is actually what what their platform is about.

We are providing

people an environment

to, to generate

trading ideas and to test them in practice, either by paper trading or going direct to, interactive brokers where they can trade with, real money if they like. If they have enough trust in their in their trading strategies.

So again, there are some financial libraries. Another 1 I'm at, I could mention is, actually,

written by a friend of mine, Sayeed Amin.

He has also focused more on the trading side and the data crunching side. He He has, for example, interfaces to Bloomberg.

But, again, we we are missing kind of this

this 1, this single library, and I imagine something like pandas.

What what pandas has accomplished for this data science part, here kind of groundwork. So this covers kind of 89% of what we typically see on a daily basis, and I'm imagining something,

here in the future. And, again, my my priority, and this is written here on my whiteboard to the right of my desk

for 2016 is to build out our library

as far as possible and and maybe to get to a standard and to benchmark library if you like. In this regard, we can say, well, Python also has kind of this 1 particular financial library where you can go to and you find kind of a multitude of useful things that you need to do, quantitative and and computational finance.

Very cool. I wanna ask you a question about DX Analytics in particular. So, obviously, I have a very limited understanding of the intricacies of derivatives trading.

I did sort of do a chunk of

reading around the market crash of a few years ago that a lot of people sort of blame

derivatives trading for taking a rather large part in the the American market crash at least. Does DX Analytics sort of help you analyze the actual worth of a given derivative

or, like, does it help you try to make smart investment choices around, are these derivatives

good investments or not? Like, what what kinds of things can you do with it?

Well, I think it's not as simple as that, unfortunately.

So a typical Derivatives analytics library

has not the main objective, at least from my point of view, that you say, well, this is kind of a good derivative or not. It always depends on what you wanna do, what your expectations are, what your kind of base position is.

Usually,

these libraries only help in coming up with your evaluations,

where your estimates if you like,

and also with kind of risk

figures,

the so called creaks. I mean, now we're getting a little bit technical,

which you need to manage your derivatives position. In this regard, our library might help people because we're implementing kind of a rather new approach when it comes to front office analytics,

to get a little bit of a more comprehensive

picture of

what the derivatives portfolio,

might,

be all about in a sense that,

we provide much more

statistical information because the whole library is based on is based on Monte Carlo simulation, which which allows you to,

get a complete

picture of the of the distribution.

For example,

to make it a little bit more concrete. So I don't wanna use too many technical terms, technically quant finance terms. What we do with the library, for example, if you have a if you think of a derivatives portfolio which consists of 10

different options,

complex derivatives,

Then there might be kind of subtle interactions between the ones when, for example, the market moves when you're when you're trading, for example, options on the s and p 500 and so forth. What we do with the library is what is typically done in the back office these days,

but we do it as a front office approach in a sense that

we do multi color simulation for the front office analytics. And this allows us, for example, to get the complete distribution

of,

of, the

possible future pay

of such a complex portfolio in a sense that we're implementing, for example, 250, 000 or even a 1000000

simulations for a complete portfolio. So we end again, ending up with not only kind of a single point estimate that we say, well, the portfolio has currently a worth of say, 1, 000, 000.

We get kind of a distribution of the of the potential future values. And in this sense, actually,

we get a more complete and more comprehensive picture when it comes to the analysis of these portfolios. But I wouldn't say that DX Analytics in the first place really helps you in deciding,

which options to choose and so forth. This is driven more or less either by a necessity,

given

the portfolio that you have already or by the simply the strategy you wanna implement. But when it comes to analyzing your positions and what what you

have right now and what the future might look like, then DX Analytics

might really help in providing a little bit more insight than the typical approaches allow you. And this is, I guess,

mainly true because we have implemented something which is very computationally demanding in a sense,

that you need lots of compute power, lots of memory, actually.

But this was actually 1 of the or these are 2 hypotheses that we started with that that we said. Well, in these days when we start implementing a new library,

we shouldn't

care too much about restriction when it comes to compute power. We today have easy scalability.

We have easy means to parallelize

code execution even with Python. This is easily accomplished. So we started out saying no matter how computation defining an approach might be, if it's a better approach, let's go for it. So these are the major premises if you like that we say, well, we work

at least with the working hypothesis

that we say,

compute and memory,

actually are unlimited,

are available,

or at least

all

that all that we need is affordable these days. This is in the end, what we see there. 10 or 20 years ago,

is available with a few clicks, actually, and a few dollars, actually, hasn't been available. You might have thought differently, but we started out doing this,

on the premise that we said will compute and and where we are not binding for what we for what we do. So let's go for the best numerical methods and and even if there might be a little bit more complex. So in that sense, you have to pay a price for the additional insights, but I think you mentioned the crisis.

When you can avoid the next crisis,

I wouldn't go as far as that. But when you can avoid personal crisis or portfolio crisis,

I think this is, when it pays when it pays off that you have to invest a little bit more maybe in the compute power, that you need to do that.

So could you please walk us through how a simple analysis using DX analytics might work?

Actually,

I mentioned before, this is all based on global, valuation approach. It's kind of a technical term, which in the end means that all the risk factors

that you need to model your portfolio are

modeled non redundantly.

For example, again, think of the S and P 500, for example,

You might start

modeling the s and p 500 with a model which you think is kind of a good choice.

The most simple 1 might be a geometric boundary motion, which dates back to 1973

where Plexigold and Merton published their path breaking, research about the valuation of European options.

But we also provide in DX100, it's much more sophisticated

models involving stochastic volatility

or jump,

diffusion parts and so forth. So you have a great choice of different models that you can use. Once you have modeled your risk factors, again, think of the S and P 500

or the Euro 50, for example, being, me here in Europe.

Then you go on and model the,

the relative positions that you have. For example, think of, European call option on the s and p 5 100 and maybe an American option on the Eurostox 50. So then you have your 2,

different options based on 2 different risk factors,

and, the next step would then be to combine that to a derivatives portfolio, how it is called in the external analytics.

You have, for example, to define whether the risk factors are correlated here in this case, we would guess yes. The S and P 500 and Euro stocks, they might not be perfectly correlated, but there's

probably some correlations, some positive correlation.

You model that, you provide all the information, the market environment information to the portfolio class, and portfolio class in the end then takes care of the valuation,

taking care of all the interdependencies

of the correlations.

The random numbers are generated. The simulate

are are carried out, and they are carried out in kind of consistent fashion that you say, well, if we do 5 500, 000 simulations for the Euro stocks and the S and p 500, then you can be sure in the end that they are done in a coherent and consistent

manner that you can add up values path by path,

scenario

that you have in the end risk,

statistics that are consistent in a sense that everything has been modeled consistently.

Everything has been simulated consistently,

And you end up with something where I say, well, we are here, in a very consistent world and we can trust the numbers at least when it comes to the aggregation. And this is what I always had in mind and what global valuation,

is in the end all about. And I and I stress that point because sometimes you you see,

derivatives libraries used in the market where they use for example, 1 numerical method like a binomial

or trinomial,

a lattice approach for the 1 option, then they use

a multicolor simulation for a more complex 1, and then they use a finite difference scheme for the third 1. And I say, well, the first has a value of 5, the next of 10, and the and the third 1 of 20, and then they add up the numbers. But,

I'm always a little bit,

yeah, well, I think it's a little bit, suspicious,

that they say, well, I have 3 different methods. They spit out numbers, and in the end, I simply add these numbers up.

So what we were always looking for is kind of the unified approach, which again, cost a little bit more compute power, but, we then end up with consistent numbers when it comes to values and risk.

So,

and, again, we have the software and the hardware available, which allows us to do things,

which were impossible 10 or 15 years ago.

And continuing,

this conversation about some of the work you've done with, your company, the Python Quants, what other kinds of services do you provide and what kinds of organizations typically hire you for that?

Well, again, it's all centered around Python for finance.

So we I would say we cover more or less the complete value chain in this regard.

So we provide technology, I mentioned DX analytics before, but we have also an offering where we provide

a an interactive,

yeah, data science, financial analytics platform,

which is called datapark.io,

which is the link, people might wanna have a look at. You can sign up. There are free trials, and usually for the,

for the for the personal individual accounts,

we don't charge anything, and they remain open,

at at least, for the moment.

This is in a professional context, we call that a quant platform. I think the quant platform might be a little more expressive

in a sense that we always imagine

a platform where quants stop working,

doing their stuff, implementing

things, and in the end, not only doing interactive,

analytics, for example, but also

deploy, for example,

a Python based application. But not only Python. I mean, the the, the platform itself is more or less, language agnostic.

It's centered around the Jupyter Notebook, which is, I guess,

the most amazing tool that our ecosystem has to offer,

but recently they,

they made it completely language agnostic. So we, for example, also provide Julia

on the platform, we provide

R as a language on the platform and much much more.

So technology

is 1 part of what we do at the Python Quans. Again, the Quans platform or Datapark IO is our infrastructure where people can work on. Analytics

is kind of the financial library that people can use to attack their more sophisticated

derivatives and risk analytics problems,

and a few more minor things that we provide, but these are the 2 major pillars, actually.

Then around that,

I have also written

a few books. My 1 book from O'Reilly is called Python for Finance. It was published last year just before Christmas,

2014.

Actually, it's it's selling quite well

all across, the world. So I'm really happy about that. I think it's also an indicator,

for the popularity of Python and finance these days. Then this July, my second book came out. It's called Derivatives Analytics with Python.

And actually taking the 2 books together

is actually a very good starting point and more or less,

all what DX Analytics is about is explained in the combination of these 2 books. So,

we have, of course, a website. I mentioned before the x dash analytics.com.

But if you want to understand what's going on behind the scenes of DX Analytics, this is explained in the books to realtors analytics with Python and Python for finance actually.

And Python for finance also the book that I use when I teach Python classes, be it public ones or corporate ones,

and this brings me to the next part of what we do. We do trainings,

And I mentioned before, they are public ones.

For example, during our Python Quants conference series and also

corporate customized ones,

that we do on a regular basis for our clients.

And,

actually,

around that or typically in the context of these

workshops, and we call them workshop in London and boot camps in New York. We have also offer Python Quants conference.

This is actually, next thing that we do. We are we're doing our own events,

and this is already,

we have,

done already 4 for Python Quantes Conferences. Next year, we will again do at least 2.

The next 1 most probably be, at the end of April in New York again and then at the end of the year again in London.

And we are thinking of doing it in Asia as well, but, this has not yet fixed. But at least there are plans, and I hope that will, that it will materialize

this next year, 2016,

that we will see the for Python Q1's conference series in

in,

Asia as well then. In addition to that, I've also done a conference in Germany, which is called Source for Quant Finance in cooperation with, Deutsche Borse.

And usually, what we do with these conference is that we do this in cooperation with bigger companies, which have

venues and infrastructure.

Before Python 1 series, which I mentioned before, is done in cooperation with the Fitch Learning and the CQF Institute.

And this brings me to the next kind of event that we do. This is,

our,

also the biggest meetup I'm running

is in London, which is called Python for 1 Finance.

We have now more than 1100

members in this group, and it's quite an active group. We have,

7 to 8 bigger meetups with around 100 people per year in London,

and it's again quite an active group and it always send the raw Python for confinance with typically speakers from very diverse fields in our industry

It's always very, very useful, I guess, for all participants. So if you're located in London,

give it a try, and I'm pretty sure at the end of January or beginning of February, we'll have our next

1 there again. And this is supported usually by, Thomson Reuters, which is the partner there.

So we are pretty happy that we have these strong big partners. We support

our purpose and and which makes it

possible that we can do these

amazing events,

around the globe actually.

And what we live off, actually, this is the last part,

but not least more, is consulting and development work. So, when it comes to revenues

and income streams,

the consulting and the and the development part, by far the the most important ones that we consult,

a bigger financial institutions or asset managers or,

exchanges,

hedge funds and so forth when it comes to Python for Quant Finance.

And there, it is very, very different what we do there. For example, I mentioned before, applying Deeks Analytics in practice,

running production code, that we've implemented forward with institution risk

based models based on Python and so forth. So it's quite diverse, but again, in the end, it's all about Python for finance and in particular,

the computational finance,

part and the financial data science part is what we focus on.

And, yeah, you see, we are only small company, but this is what we do on a daily basis. And I think we cover almost all the bases when it comes to this

niche of the

market.

It's very cool that you folks have managed

to keep the, you know, keep the lights on and and get enough consulting income and yet have that work allow you to contribute back to open source? Because I know sometimes big enterprises

not exactly open source friendly.

Yeah. That's that's true. That's true. I mean,

in the end, but,

yeah, I've always this discussion with

living out of open source. I think you should contribute back,

to open source, and,

it's also kind of a model that you can choose these days, and I guess the the whole culture and and the infrastructure

have changed kind kind of a bit that you can say, well, you can't even live off your open source work.

And this is kind of fascinating. They say, well, you share with the world, people contribute back, and you can still make a living out of something that you share, which might be counterintuitive

for people,

yeah, who are not used to these things. But more and more, and I've seen seen, and I've joined that, joined it over the year. The people saying, well, if what you did, for example, inspired me to open source, for example, the the 1 example that I mentioned from Sayid Amin, from the Thelesians.

And he said, well, I've seen so many, good talks about people who argued why it is good to open source libraries and this actually,

helped me made up my mind and and make the decision to open source Python lessons as well. So this is actually something that I really enjoy that people pick up, the idea and the culture and say, well, we go that way. But on the other hand, we still have proprietary technology. For example, the the the platform part and what we provide there is not open source yet, I would say.

I didn't see the point to do that. But again, here we provided for free more or less. So we have many, many students working on the platform. We have a few 1, 000 users on the platform, and I know that many students and so forth that might not have to budget,

to afford kind of professional environment like that. They're really happy that they can use that for free, this nice service integrated platform. So we try our best to provide, good value here back to the community as well even if this particular thing is not, open sourced yet again.

So is there anything that we should have asked you but didn't?

Or are there any places where our listeners can help

contribute to the open source work that you folks do?

Yeah. For sure. I mean, we we are running a few websites. So it's it's difficult

to point people to a single page. But a good starting point for people who are interested in what we do, and there you find almost all the links to the other stuff, like to the books, to the media group, to the events pages, and so forth is our company page, which is, h t p,

colon slash slash, tpqdot

io. Again, tpq.io

for the Python quants.

There you find, again, links to many, many other places and resources. You find the link, for example,

again, to DX Analytics. We have, of course,

our GitHub repo,

up with, the code of DX Analytics. You can try it out on on, the quant platform.

The link for that is quantashplatform.com.

If you prefer more the datapark,

styling. Actually,

the technology behind it is the same, but the styling is a little bit different. You can use Datapark dot io.

It's more or less the same.

So go to these places, have a look, and if you have questions, yeah, just,

either drop me an email. You'll find their contact details,

or you can also follow me on Twitter. My Twitter handle is,

d y g h or just Google me up. You should find lots of, information about myself.

Yeah. I see you're you're writing it down here, actually.

But Twitter is kind of a good way to stay in touch and to see what we are doing. You get usually the latest news with regard to conference we're doing or if there's an upcoming meetup, and so forth.

So it's kind of a good way to keep up,

and to get it in a more interactive fashion.

And, again, go to tpq.il,

and then you'll find many, many links to many, many other resources that you can use, also to my private website, hillippish.com,

where I usually store all the presentations

that I give,

where you also find the, videos and so forth.

And,

yeah, I think there's a lot there. And people who are interested, just just get in touch. I'm happy

to discuss,

things.

So I see, again, my, my Twitter handle is j like

Johannes.

It's d y. Not g.

It's j h.

Well,

thank you. This has been a lot of fun.

So if there is nothing else before we move to the pics, then we can go ahead and do that.

Alright. So I will get us started.

So this week I wanna pick a novel that I've been reading. I'm about halfway through it. It's Kraken by China Meeval.

I mentioned him as an author before on 1 of the other episodes,

and

this is in his typical fashion

very different than any of his other novels but at the same time the character of the text is the same. He does a very good job of using very innovative

and

evocative language.

And it's a story,

it starts off it seems like it's just going to be a regular kind of mystery story but it very quickly incorporates a lot of supernatural elements that make it very

compelling.

And it does a really good job of juxtaposing those elements against the normal

everyday world that the story is taking place in. It's set in London,

Definitely a great novel. I recommend checking it out.

My next pick is a series of books that I've been reading with my son called Heroes in Training,

and there are I think 7 or 8 books in total and they're all about the

gods of Olympus

at the age of about 10 and it's the story of them

trying to

overthrow Kronos.

And so it's got a lot of good humor baked into it,

very well targeted at a younger age range, so been having a lot of fun reading that with him.

Along the same lines, there's a set of graphic novels that I've been, that I got for him and I've been reading as well called Olympians.

And the I believe the author's name is George O'Connor. And it's just very well done, very well researched graphic novels that present the gods of Olympus

and you know I've done a fair bit of reading about different Greek mythology and there were still a lot of elements that showed up in those graphic novels that I had never come across before. So

definitely interesting, definitely recommend reading those as well.

And my last pick, kind of along the lines of our data focus here is the Data Elixir newsletter

and it's just a weekly newsletter that has a bunch of links to interesting and useful articles about what's happening and the different aspects of data and data science.

And with that, I will pass it to you, Chris.

So,

I was just in Vermont for a week. We typically go there every Christmas. And for me, Vermont is many things, beautiful scenery, great food, and also, it is home of really, really great beer.

So I have a few beer picks this time. My first pick is from a Vermont brewery called Hill Farmstead

Brewing, and,

the beer is called Edward because, apparently, that's their grandfather was named Edward. They made this ale in his honor.

It's an American pale ale.

It's really

interesting. It's it's got a really incredible aroma, and it is

really kind

of hopsy and tart,

with flowery and pine notes, which I know sounds kinda strange drinking a beer with flower and pine, but it's actually really delicious and and and definitely worth hunting down.

My next pick is,

long trail brewing. A lot of people who live in New England kinda know Long Trail because of what they find from them in bottles and stores. And that's you know, those beers are good. But if you ever manage to get to,

Bridgewater Corners,

where the actual plant is, they have a number of special beers that you can only get there on tap that are fantastic.

The first 1 is called culmination.

It's a chocolate porter. I don't usually like chocolate porters, but this 1 is really

exceptional. It's

less sweet than the average. It's,

just got a lot of complexity

and depth. It's it's really good stuff. The next 1 is gonna sound really cheeky because of the name, but it's a really excellent beer. It's called,

space juice, double IPA.

It is

a seriously, seriously hoppy,

IPA,

caramel

malts,

really smooth,

and I because I was, again, I was there at the at the at the actual brewpub.

They happen to have it cast conditioned on tap, which was just delightful.

So that's definitely worth checking out. My last pick is amazing amazing, a Python pick. I usually don't have much to pick because usually there are smarter people on here than I

discussing, you know, Python things that that are much more interesting. But I just encountered something that really hit a sweet spot for me. It's called Flask

dash restless,

And essentially, it allows you to take your sort of plain old Python classes,

and make them into a REST API.

And what I really like about it is that in addition to being sort of very easy to set up and hook up, it interfaces with SQLAlchemy

very nicely.

What you end up with is a Flask application, and so

all of these sort of, you know, other pieces of the Flask ecosystem,

like authentication

or whatever other kind of plug ins you may wanna use,

will probably work.

So I've really been enjoying this. I got a side project going that I wanna write a web service for. And of all of the things of of this ilk out there, this 1 just fit the best for me.

That's it for me this week. Yves, what do you have for us for picks? Well, I've also a few books actually. Currently, I think it's a good time to read.

Usually, when there's kind of the middle of the year and you're busy busy working.

But a fascinating book that I read recently is kinda it's called The Willpower Instinct.

I think it's around since quite a while. I think it was published 2011 by Kelly McGonigal.

It's kind of a fascinating book about, how how humans function actually and how you might not be able to change your behavior, how how you get caught in your habits, and what's going on

chemically,

psychologically. And I I really wanna I I listen to the book actually,

my my iPhone, but I also read it

on paper. But it was really, like, kind of every chapter was, like,

I had the feeling at least about myself and and how I miss recently doing this or that and so forth. So if you're interested in changing your behavior and and or at least to understand,

how man how human mankind,

actually is working, this is kind of a very good book. The best, at least in this regard, I know actually.

Another book, I am reading currently is The Way of the Seal. It's kind of a nice book by Mark Divine. If you wanna get stronger and wanna also kind of develop willpower,

in the way the seals do, and I guess they're 1 of the best, elites,

units in the world to actually special forces.

So it's kind of also a good book, a little bit more practical 1 and that there are many, many exercises how you can challenge yourself,

to get maybe a better person. Another 1 a little bit different, is called,

Sapiens,

a previous 3 volume of mankind, but you will know a Harari, a kind of a nice book,

from the very beginning,

maybe 20, 30000 years ago,

Homer Erectus, was around and then Neanderthals and so forth, up to today. So the scientific revolution

actually is a very nice read. Usually, nothing I read, but again, during the holidays, kind of very nice read for a little bit, better understanding,

what shaped actually,

our, yeah, our species, if you like, like religion and agricultural,

and evolution and so forth.

Also very, very, yeah, short while read, actually, very well written. And the last 1 is also a Python 1,

It's also had the Python pick. I wanna, recommend the book high performance computing

Python by Ian Oswald and and Michelle,

Gralik.

It's kind of a very good

book from my point of view, which is not only about high performance,

Python actually, but it's more or less about,

yeah, how Python, how the interpreter works,

and and what is going on behind the scenes. So we discussed many, many things today in this podcast,

how to make Python,

more fast and so forth. But the very reasons and and the workings behind the scenes when it comes to data structures, data storage, and and then any critique kind of, details that you have to be, aware of if you wanna go professional in this regard is explained very well from my point of view in this book. So I really like it. And, if you wanna do a Python for Quant Finance, this might be 1 of the books that should be on your on your bookshelf actually.

Very interesting.

Well, thank you again for taking the time out of your day to join us and tell us all some more about your work in quantitative finance.

You've already told us about how to get in touch with you. So, yeah, thank you very much, and I hope you enjoy the rest of your evening as well as your new year.

Thank you, Chris. Tobias, thank you for having me, and, yeah, happy new year, and all the best for 2016.

Happy new year to you as well. Bye bye.

The Python Podcast.init

Summary

Brief Introduction

Interview with Yves Hilpisch

Picks

Keep In Touch

Links

The Python Podcast.__init__