The first company that comes to mind in terms of publicly discussing its programming and computer science type engineering is JaneStreet. There are episodes of Software Engineering Daily and YouTube talks and blog posts. Many of them related to the OCaml language and system design.
Broadly speaking I think that you are speaking about two things: derivatives pricing (this is what people think when thy talk about quantitative finance) and algorithmic trading (which can be either pure market making or alpha seeking speculation ;).
>> Broadly speaking I think that you are speaking about two things: derivatives pricing (this is what people think when thy talk about quantitative finance) and algorithmic trading (which can be either pure market making or alpha seeking speculation ;).
"Quantitative finance" was mostly associated with derivatives pricing in the mid-late aughts, but nowadays I see a pretty loose classification tossed around that essentially makes it shorthand for "we're doing something data science-y to forecast things." It's more of a marketing/cultural term, and has sort of lost a lot of the meaning and precision it used to have. Handy to sort of succinctly sum up where on the Street you vaguely work (or at least which ingroup you'd like to signal therein), but not so handy for trying to e.g. Google around for what to learn. QuantStart is probably helpful there though.
Not a criticism of your point, just wanted to add some color to it for other readers. I agree with Wilmott forums being a great starting point at the very least, though it's going to be a little esoteric for someone totally new to finance.
To the OP: pick up Heard on the Street: Quantitative Questions From Wall Street Job Interviews. It's not perfect, but it's a good start. There aren't any textbooks that will take you all of the way in algorithmic trading (from finding alpha, to strategy implementation, to risk management, to portfolio theory, etc). If you're a CS student and looking for quantitative finance, core computer science (not just programming) is what you want to drill down on. You'll want to invest in your math skills quite a bit too, and especially statistics.
Research firms like Two Sigma, RenTec (good luck), AQR, D.E. Shaw and Bridgewater, which are wholly or in part devoted to what you might call "financial engineering." Learn about their processes and talk to as many people in the industry as you can to learn about what they do and network.
Finally, chollida1 does this better than I can, but please don't trade with your own money. It's an expensive tuition :)
As a CS student, I'd really make sure your stats knowledge is solid. Perhaps take a class that covers stochastic finance (Black-Scholes, etc.) if available.
I learned the hard way that it is quite difficult to break into finance as a non-student, so do everything you can now to land that first gig. Good luck!
Quantopian is home to 120,000 people learning algorithmic trading, including students, data scientists, academic researchers, developers, and finance professionals.
We provide a research platform, market simulation, and data for free. We also provide tutorials, community, and lectures to teach you how to get good at it. I recommend you take a look at the Getting Started Guide (https://www.quantopian.com/tutorials/getting-started) and then start going through the Lectures (https://www.quantopian.com/lectures). The lectures cover some important statistical topics, and they get into how to apply those concepts to algorithmic trading.
Related question: after creating a successful quant trading strategy - how would you publicize it?
I've created a fund that tracks my strategy at motifinvesting.com and am posting the trades at instavest.com. Where else should I go? Are there any contests I can enter? (Quantopian requires too much turnover I only do 50 trades/yr)
I'd recommend that you get good at stochastic processes and time series analysis, machine learning, and neural networking on the tech side, and managed futures and commodities trading as opposed to stocks.
Quantiacs is the best place to learn. They are the world's 1st and only crowdsourced hedge fund actively trading with institutional capital, you can contribute your algo to their marketplace and get matched with millions in investment allocations and you keep 10% of the profits and retain 100% of your IP, and they also run the world's largest quantitative finance competitions -- giving out $2.2M in allocations per Quarter.
Unlike Quantopian, Quantiacs focuses on managed futures as opposed to equities. This is important because managed futures are uncorrelated with the stock market and are the most liquid markets in the world -- it's where the professional quants play. So if you want to be successful with quantitative finance and algorithmic trading -- you should focus on managed futures more than equities. So even though there are more users on Quantopian, the best quants in the world are on Quantiacs.
Also on Quantiacs you can use either Python or Matlab so it's more flexible, and the learning curve is not as steep as with Quantopian.
I'd recommend that you get good at stochastic processes and time series analysis, machine learning, and neural networking on the tech side, and managed futures and commodities trading as opposed to stocks.
Quantiacs is the best place to learn. They are the world's 1st and only crowdsourced hedge fund actively trading with institutional capital, you can contribute your algo to their marketplace and get matched with millions in investment allocations and you keep 10% of the profits and retain 100% of your IP, and they also run the world's largest quantitative finance competitions -- giving out $2.2M in allocations per Quarter.
Unlike Quantopian, Quantiacs focuses on managed futures as opposed to equities. This is important because managed futures are uncorrelated with the stock market and are the most liquid markets in the world -- it's where the professional quants play. So if you want to be successful with quantitative finance and algorithmic trading -- you should focus on managed futures more than equities. So even though there are more users on Quantopian, the best quants in the world are on Quantiacs.
Also on Quantiacs you can use either Python or Matlab so it's more flexible, and the learning curve is not as steep as with Quantopian.
If you want to get a job at an HFT e.g. Jump: as a student you're not expected to know much about finance or trading, the prerequisite knowledge is similar to getting hired at eg Google. I work at one of these firms, when we hire people we have them come in and code in an IDE of their choice on a problem of our choice for about 2 hours, and we watch them do it and discuss it after. We also do algorithm interviews, and try to find people who are demonstrably smart and also excited to work with us (note this is for dev roles. If you want to work on trading roles you need to have a strong intuitive grasp of probability, games and asymmetric payoff situations, these will come up in interviews).
If you want to get a job as a quant: other comments here have addressed this.
If you want to learn about algorithmic trading from a tech perspective: go read some exchange specs (BATS, CME, Eurex tech specs and market model). That's the nitty gritty and you'll learn more about trading from that than anything else you can do if you're not employed in trading.
If you want to learn about machine learning in the context of finance: get a job at one of the quant hedge funds like Two Sigma. You do not have access to the data you would need to learn on your own, and you cannot afford to get it yourself.
In general to learn about modern algorithmic trading you have to work in the industry, there is almost no public information of any value (maybe read the Sniper in Mahwah blog if you haven't, he's pretty smart).
If you want to get a job you do not need to learn about this, you just need to be worth teaching it to.
edit: this blog needs to be organized: best post, recommended posts, etc for new readers. What a waste of writing if your new readers can't find good articles.
This is the best answer if you're looking into the field as a career. Within any subset you would be at a competitive disadvantage trading on your own whether its due to speed (HFT), execution costs (market making), or very expensive/exclusive datasets (machine learning). There's learning benefits to trading on your own, but you will most likely lose money and would have to keep it within certain bounds (i.e. daily position updates). As neuronsguy said, the best strategy is to be demonstrably smart/interested.
Might be a long shot but do you have any advice on how to get a "strong intuitive grasp" of probability, games (I'm assuming you mean game theory?) and asymmetric payoff situations.
That is specifically if you want to get a job as a trader.
The best way is by spending time playing poker, bridge, etc. Sports betting is also good. Any game where you are making decisions facing asymmetric payoffs under uncertainty.
Trader interviews for students tend to focus on whether the candidate can be rational and self-consistent. "How much would I have to pay you to not wear a seat belt on the ride home?" "You roll a 100,000-sided, and if it's a 6, you die. How much would you pay not to play that game?" Your answers to both questions should be self-consistent.
Asymmetric payoffs: tell me an interval around the number of bus stops in Taipei, where you'd be willing to bet 20:1 your interval contains the true answer. People make the interval way too tight. The interviewer chose this question to ask you. He knows the answer, and since he asked you this question, he probably also knows it's something surprising that people get wrong. 20:1 is a huge asymmetry. You should be afraid of making this bet.
That said I'm not the best person to ask about this, it's not my strong suit.
21 comments
[ 3.8 ms ] story [ 46.5 ms ] threadGood luck.
To better understand the difference between the different branches of QF world, visit this site:https://www.quantstart.com/articles/Quantitative-Finance-Rea...
It has a comprehensive list of references and articles describing in details what to expect in different jobs.
It won't harm if you occasionally visit https://forum.wilmott.com/.
And finally, for shit'n'giggles: http://www.zerohedge.com/ ;)
"Quantitative finance" was mostly associated with derivatives pricing in the mid-late aughts, but nowadays I see a pretty loose classification tossed around that essentially makes it shorthand for "we're doing something data science-y to forecast things." It's more of a marketing/cultural term, and has sort of lost a lot of the meaning and precision it used to have. Handy to sort of succinctly sum up where on the Street you vaguely work (or at least which ingroup you'd like to signal therein), but not so handy for trying to e.g. Google around for what to learn. QuantStart is probably helpful there though.
Not a criticism of your point, just wanted to add some color to it for other readers. I agree with Wilmott forums being a great starting point at the very least, though it's going to be a little esoteric for someone totally new to finance.
To the OP: pick up Heard on the Street: Quantitative Questions From Wall Street Job Interviews. It's not perfect, but it's a good start. There aren't any textbooks that will take you all of the way in algorithmic trading (from finding alpha, to strategy implementation, to risk management, to portfolio theory, etc). If you're a CS student and looking for quantitative finance, core computer science (not just programming) is what you want to drill down on. You'll want to invest in your math skills quite a bit too, and especially statistics.
Research firms like Two Sigma, RenTec (good luck), AQR, D.E. Shaw and Bridgewater, which are wholly or in part devoted to what you might call "financial engineering." Learn about their processes and talk to as many people in the industry as you can to learn about what they do and network.
Finally, chollida1 does this better than I can, but please don't trade with your own money. It's an expensive tuition :)
I learned the hard way that it is quite difficult to break into finance as a non-student, so do everything you can now to land that first gig. Good luck!
Some starting resources:
-Ernie Chan's books and blog (https://epchan.blogspot.com/)
-QuantStart has great starter material and a new book, although I haven't read it (https://www.quantstart.com/)
-"Inside the Black Box" (Narang) I've seen referenced a good bit but felt as though it leaned toward order execution and rather boring
-"Dark Pools" (Scott Patterson) a great story about the rise of algorithmic trading
-"Flash Boys' (Michael Lewis) offers a nice follow up (HFT), but considered a bit sensationalist
EDIT: If you're planning on using Python (a solid bet)...
-Python for Data Analysis (Wes McKinney) - Great, quick book for Pandas by former AQR (and now Two Sigma?) guy.
-Yves Hilpisch books: "Python for Finance" is introductory while "Derivative Analytics in Python" is quite math heavy.
We provide a research platform, market simulation, and data for free. We also provide tutorials, community, and lectures to teach you how to get good at it. I recommend you take a look at the Getting Started Guide (https://www.quantopian.com/tutorials/getting-started) and then start going through the Lectures (https://www.quantopian.com/lectures). The lectures cover some important statistical topics, and they get into how to apply those concepts to algorithmic trading.
disclosure: I work for Quantopian.
I've created a fund that tracks my strategy at motifinvesting.com and am posting the trades at instavest.com. Where else should I go? Are there any contests I can enter? (Quantopian requires too much turnover I only do 50 trades/yr)
Quantiacs is the best place to learn. They are the world's 1st and only crowdsourced hedge fund actively trading with institutional capital, you can contribute your algo to their marketplace and get matched with millions in investment allocations and you keep 10% of the profits and retain 100% of your IP, and they also run the world's largest quantitative finance competitions -- giving out $2.2M in allocations per Quarter.
Unlike Quantopian, Quantiacs focuses on managed futures as opposed to equities. This is important because managed futures are uncorrelated with the stock market and are the most liquid markets in the world -- it's where the professional quants play. So if you want to be successful with quantitative finance and algorithmic trading -- you should focus on managed futures more than equities. So even though there are more users on Quantopian, the best quants in the world are on Quantiacs.
Also on Quantiacs you can use either Python or Matlab so it's more flexible, and the learning curve is not as steep as with Quantopian.
I'd recommend that you take a look here for tutorials: https://quantiacs.com/GetStarted
And see what others have said about it here: https://www.quora.com/What-do-you-think-about-Quantiacs-com-...
Quantiacs is the best place to learn. They are the world's 1st and only crowdsourced hedge fund actively trading with institutional capital, you can contribute your algo to their marketplace and get matched with millions in investment allocations and you keep 10% of the profits and retain 100% of your IP, and they also run the world's largest quantitative finance competitions -- giving out $2.2M in allocations per Quarter.
Unlike Quantopian, Quantiacs focuses on managed futures as opposed to equities. This is important because managed futures are uncorrelated with the stock market and are the most liquid markets in the world -- it's where the professional quants play. So if you want to be successful with quantitative finance and algorithmic trading -- you should focus on managed futures more than equities. So even though there are more users on Quantopian, the best quants in the world are on Quantiacs.
Also on Quantiacs you can use either Python or Matlab so it's more flexible, and the learning curve is not as steep as with Quantopian.
I'd recommend that you take a look here for tutorials: https://quantiacs.com/GetStarted
And see what others have said about it here: https://www.quora.com/What-do-you-think-about-Quantiacs-com-...
If you want to get a job at an HFT e.g. Jump: as a student you're not expected to know much about finance or trading, the prerequisite knowledge is similar to getting hired at eg Google. I work at one of these firms, when we hire people we have them come in and code in an IDE of their choice on a problem of our choice for about 2 hours, and we watch them do it and discuss it after. We also do algorithm interviews, and try to find people who are demonstrably smart and also excited to work with us (note this is for dev roles. If you want to work on trading roles you need to have a strong intuitive grasp of probability, games and asymmetric payoff situations, these will come up in interviews).
If you want to get a job as a quant: other comments here have addressed this.
If you want to learn about algorithmic trading from a tech perspective: go read some exchange specs (BATS, CME, Eurex tech specs and market model). That's the nitty gritty and you'll learn more about trading from that than anything else you can do if you're not employed in trading.
If you want to learn about machine learning in the context of finance: get a job at one of the quant hedge funds like Two Sigma. You do not have access to the data you would need to learn on your own, and you cannot afford to get it yourself.
In general to learn about modern algorithmic trading you have to work in the industry, there is almost no public information of any value (maybe read the Sniper in Mahwah blog if you haven't, he's pretty smart).
If you want to get a job you do not need to learn about this, you just need to be worth teaching it to.
https://sniperinmahwah.wordpress.com/
edit: this blog needs to be organized: best post, recommended posts, etc for new readers. What a waste of writing if your new readers can't find good articles.
double edit: interesting investigation into HFT companies building microwave towers to cut transmission times to milliseconds from chicago to japan https://sniperinmahwah.wordpress.com/2016/09/23/once-upon-a-...
Any recommended study path?.
Thanks in advance.
The best way is by spending time playing poker, bridge, etc. Sports betting is also good. Any game where you are making decisions facing asymmetric payoffs under uncertainty.
Trader interviews for students tend to focus on whether the candidate can be rational and self-consistent. "How much would I have to pay you to not wear a seat belt on the ride home?" "You roll a 100,000-sided, and if it's a 6, you die. How much would you pay not to play that game?" Your answers to both questions should be self-consistent.
Asymmetric payoffs: tell me an interval around the number of bus stops in Taipei, where you'd be willing to bet 20:1 your interval contains the true answer. People make the interval way too tight. The interviewer chose this question to ask you. He knows the answer, and since he asked you this question, he probably also knows it's something surprising that people get wrong. 20:1 is a huge asymmetry. You should be afraid of making this bet.
That said I'm not the best person to ask about this, it's not my strong suit.