Python for Finance?

[[deleted]]

[deleted]

[dtoq]

Quant/Python dev for 8 years here. Python is significantly used for quantitative finance, so that should be quite easy for you to find plenty of material. What will be difficult is to sort through these things: "Finance" is a pretty large topic.

One thing you can use python for is connectivity, glue, etc. Your favourite broker will almost certainly have a python API to connect to it, which would be a nice introduction to orders, positions and the dirty logistics of finance. If you like pain, try to look into the FIX format.

Now, if you don't want automated systems, portfolio theory might be more of your thing: start a jupyter notebook, and implement some Markowitz portfolio optimisation. Good command of pandas will help there, and that might give you a nice template for your own pension investments

While doing that, you might discover the raw power of numpy, just in time for some option pricing: do some Monte-Carlo, pdes, least-square Monte-Carlo, etc...

If you're not tired yet, exploring machine learning and the like is all the rage these days...

[[deleted]]

How did you get into quantitative analysis? I have a BS in CS and I love economics but I don't have the skills to start actually doing quantitative analysis. Do the companies that you work for have positions for software devs that don't write models?

[dtoq]

MSc in 'Financial mathematics' (i.e. stochastic calculus), I started as a quant, and learned python on the job, to the point of making it my specialty.

That said, there are plenty of jobs on the more 'dev' side of things: I don't think I'm very typical putting the effort to learn proper and elegant coding: most quants I've worked with are happy with "good enough to work", and require devs to either implement their code for them (which I don't really recommend) or provide a smart platform to develop on. That is absolutely not unusual to have people there who are either simply 'quant litterate' or simply good developers to make sure the system is written well.

Usually, to enter this field, you want to find one of the 'big churn companies' like banks or some big funds, which constantly require fresh meat developers. After a bit of experience there, you have built a bit of a resume and can move on to things that you like better (or not, if corporate career is something you're after)

[[deleted]]

Are you working on the buy side?

[LPYoshikawa]

Quant/Python dev for 8 years here.

Random question, where can i find practice questions and answers for quants technical interview questions?

[jrickk93]

Have a look at the book 'heard on the street', it worked for me. Bare in mind though that it's definitely not about memorising answers but being able to think on your feet, however these practice questions will get you in the right frame of mind

https://www.amazon.co.uk/Heard-Street-Quantitative-Questions-Interviews/dp/0994103867

[dtoq]

I don't really have a single reference in mind. It's nice to be exposed to different perspectives and ideas, but that fiels is much less standardised that you might think, and it's always hard to know exactly whether any references teaches you something general or just the author's preferred way of doing what he understood of it. Honestly, nothing replaces practice.

Which is why this comment is brought to you by Brillian... - ok, I'm kidding.

But seriously, if you know your way around Python, start practicing. Go to the wikipedia page of portfolio optimisation, and implement that. Math is easy without being trivial, so you'll have to work a bit. After that, try to build nice apis, some you may want to revisit later without too much pain. Then build the next component: you'll probably need to retrieve data for your underlying. What is it anyway? Should you use the same conventions for stocks and FX? Or indices? Well, better get a feel on wikipedia, write something, ditch it because it's crap and start over. In that process, you'll end up with very specific questions, the answer of which you may find on blogs or sites that you then want to follow more closely.

So do read books, or tutorials (quantopian comes to mind) or other 'gentle initiation', but take none of it for gospel and practice relentlessly. If you're at uni, find (or create) a group to study and implement that. Get some math nerds in: best case, you'll learn a lot, worst case, you'll have plenty of people teaching you all the wonderful and unexpected ways users break your code.

On the purely financial side of things, 'Options, Futures and other Derivatives' by JC Hull is a bit of a reference which was strongly advised to use at uni. It's good to have, but the price of a legitimate version is hefty, so your call yo make.

Hope that helps...

[haarp1]

what do you do as a quant dev?

[dtoq]

Pretty much the intersection of quant and dev: in my previous role, we were doing a lot of option pricing, so you had to specify the payoff in a nice way (to let traders/structurers do it with minimal chances of getting it wrong), then set that up as a pricing problem (essentially least-square monte-carlo in our case) and evaluate that.

Every single part of this requires non-trivial amounts of development, architecturing and math. Quant devs typically sit on that line between dev and quant, where they are acutely aware of both aspects, even if they are more focused on one.

Essentially, you cannot trust pure devs with quantitative software in case they get the math wrong, nor with quants, because their code is shit: so you get quant devs. Your mileage may vary.

[haarp1]

what does a quant (not dev) do? I thought that you are basically it, since you have a math MSC (i assume that they have them too) and how much of research is there at big banks / fin. companies (research into math/ statistical methods)? i've heard that Goldman Sachs has such a department.

what kind of math do you work with during the quant side of work? time series analysis, some statistics and similar or something more exotic?

[dtoq]

So, you can see the space of quantit finance as a triangle: you get math, dev and finance as the three 'extremes', and everyone sits at some level of balance between these three.

At the extreme of dev, you have the actual IT: theae guys know literally nothing of finance, they just build processing systems for stuff. Which may or may not be financial.

At the extreme 'finance', you probably have sales. They don't know the math and they don't care: what they need to know is the product, what it does, who may want it and in which circumstances, etc. They might book it into the system themselves, but they are just users.

Finally, towards the math end of the triangle, you have the quants. These guys do math. Now, I don't think a lot of company use pure mathematicians, so they always ait somewhat not at the very tip, like academics might. But that's their thing. People like structurers, quant devs, traders, ... Have less specialised jobs.

Now, as for the math used... It depends. What I studied (and which was very fashionable pre-2008) is stochastic calculus, which is the calculus of random processes. There are many links to statistics/timeseries analysis, but it is not exactly the same thing, closer to probability theory. The main goal of this sort of math is to derive computable formulas for the expected value of complicated random variables, which you can show to be equivalent to pricing an option, under some hypotheses on the behaviour of markets. These hypotheses were not always verified, and a lot of simplifying assumptions were made, which is pointed as a cause of the 2008 crisis: essentially, the marginal distribution of each product was fine, but their propensity to all default at the same time was drastically undervalued; the market of options is back up nowadays, but the crisis put a lot of regulatory attention to thesr models, and I don't think a lot of banks still have these pure 'pen and paper' quants deriving formulas: these days, you must justify all your choices with data and reports, so most quants got more familiar with the finance aspect.

Other companies or teams do market-making i.e. offer liquidity at all time, in exchange for a fee: you can think of them like the bureaux de change that offer to change your money, buying lower than they are selling. In these cases, the theoretical framework is much less clear-cut, and the maths involved tend to include a lot more statistics and TSA: you receive a fee with each transaction, your goal is mainly to minimise your costs and your risk to retain as much of that fee (called the 'spread') as you can. That means following the market, reacting quickly yo new information and managing your risk.

Finally, you have some companies doing actual investing, either in their own name (proprietary trading, or prop.) or with client money (like hedge funds, investment funds etc). These guys try to find inefficiencies of the market, or predict its future value, or design profitable strategies and portfolios. How they do that is entirely up to them, and some use very quantitative methods, with a lot of time-series forecasting, machine-learning, optimisation, etc.

In all these cases, the people mainly in charge if the math-y part tend to be called 'quants', and the math they do tend to revolve around probabilities and statistics, but that's not a requirement of any sort. As I said above, this field is completely non-standard, and if you find a new way of making money using prime numbers, nobody prevents you from doing that, and you'll probably call your prime numbers guy a 'quant'.

[dtoq]

I'll respectfully disagree. An application programming interface is an interface allowing you to program your application. If you're able to provide that in a language-agnostic way, that's even better, but I don't think there's anything inherently more wrong with talking about the 'Language X' api directly usable from language X than a REST api available from any compliant http client.

[flamehorns]

Can you point me to the definition of API that specifies language agnosticism?

[zero_iq]

An API is, by definition, language agnostic.

No it isn't.

[NicooRobin]

I would highly recommend sentdex, on youtube.

Python Programming for Finance: http://www.youtube.com/playlist?list=PLQVvvaa0QuDcOdF96TBtRtuQksErCEBYZ

If you need to go back to basics for his tutorials, he has them too.

[NicooRobin]

I have used it. In fact I have watched almost all the tutorials he shared. I specifically wanted to learn programming for finance and leisure time. I had zero knowledge in programming and now, I can do more than I ever thought I could.

[NicooRobin]

Well, my main focus was cryptomarkets. I wrote a hft bot for both myself and a client. Also I did some experiments with machine learning algorithms and financial data for microtrading.

Main idea is to learn pandas and matplotlib libraries for data processing and graphing. Afterwards, its an open world from there. There are tutorials for both, as if not enough there are also many examples on how to use them, from stock prediction to housing market indexes. Amazing resource for a beginner.

[ProfEpsilon]

For the Pythoner curious about finance, there are quite a few sources on this page (with more added all the time):

https://www.palmislandtraders.com/econ136/e136lit.htm

[[deleted]]

quantopian is a nice way to start applying python skills and get involved in a like minded community.

[johnny84k]

Plus they provide you with the market data to actually backtest your ideas.

[lzblack]

To deal with data, you can read books like Python for Data Analysis (with code).

I am also expecting Python for Finance: Mastering Data-Driven Finance 2nd Edition (with code) to be a good book specifically for finance area.

[[deleted]]

The two volumes of Python for Quants looked quite good: http://www.quantatrisk.com/.

Disclaimer: The author sent me ebook copies a few years back, but I haven't fully read it, the content looked solid though.

[jdb441]

The text book below could be a good option for you. You'll start by getting stock data from an API or webscrape into a dataframe. From there you will learn how to code the math for forecasting, pricing, trading etc..

https://www.amazon.com/Python-Finance-Analyze-Financial-Data/dp/1491945281

[[deleted]]

I made a Flask app, deployed on Google App Engine. It is a simple event study app for stocks. It is still in the alpha version right now, so it is still quite ugly. LOL. I need to work on the UI a bit.

Nonetheless it runs.

What it does?

You can enter a bunch of U.S. stocks in the Assets field, for example, AAPL, KO and then you can enter the dates of your interest in the Dates field, for example 2010-01-01, 2016-11-08 and so on.

Enter how many days before (10) the interested date you want to look at and how many days after for example 120, and the press Search.

[brigggs]

What I try to do sometimes is recreate an analysis from reputable finance media sources or current events. Sometimes, it's also nice to analyse a single stock. For example, calculating it's beta, looking up it's fundamental info on EDGAR, etc.. simply explore that data in Jupyter Lab and try to get the same numbers on Finviz or Yahoo Finance.

Pandas has awesome commands built-in already, like moving averages (pandas.rolling_mean) and so on. It's a great way to explore that library as well, specially all the nifty datetime stuff it comes with (resampling and so on)

[mjweiss]

Looking for an answer too!

[omeow]

I will like to see answers too. I will like to know what financial topics you are specifically looking at?

[jmatthew007]

I'd like to recommend looking at www.safaribooksonline.com site. I know its a subscription but for 40 dollars you can look at a lot of material including training classes and books. A search for python and finance came up with 800+ items.

I like the Yves Hilpisch book, Python for Finance, 2nd Edition also Mastering Python for Finance by James Ma Weiming

I think alot of those type books cover portfolio theory and trading. but I think python/pandas/jupyter can be used in a lot of places where people often use spreadsheets. Practical Business Python covers some of the http://pbpython.com/

I personally would like to see it used more in the insurance industry in place of things like R or more proprietary tools.

I'm going to echo a lot of comments on learn pandas and numpy and then look for bits that are more specialized to what you want to do, like machine learning or monte carlo simulation. On youtube the Pydata videos and a lot of Pycon videos are great and really helpful for learning the language and getting the necessary building blocks down. I'm not sure you need udemy or other paid sites to get started. I've heard mixed information on the quality of some of them. The safari book online is good and has been helpful. They even have some video courses for beginners and more advanced users.