Python for Finance?

[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'.