- Quantopian: Was good, has tons of data and makes things very easy. Unfortunately you can't easily live trade your algorithms anymore.

- QuantConnect: Open source, works okay, at the time I wanted it for options trading though and their support for options chains isn't so good.

- Zorro: Closed source, paid, uses a weird custom variant of C but works surprisingly well. Mainly focused on forex though.

- Others: There are others, like this one, that are mostly unusable because they miss something essential, like broker support or data.

QuantConnect is the only real packaged option these days I think but it's also not hard to roll your own. There are brokerages with _really_ easy to use APIs (Tradier is the first that comes to mind).

So the most honest answer to your question, insofar as the question makes sense, is that algorithmic traders roll their own under the auspices of the firm employing them. In fact the way that this service structures their pricing is strange and unintuitive to me, and I can't imagine professional traders would agree with it.

While I have known people to trade on their own in a way that can be legitimately called "quantitative" (and even do so profitably), they all worked within a firm before doing that, and all of them targeted strategies that allowed them to capture a greater amount of the profit for an otherwise small total revenue that would generally be considered too illiquid and capacity constrained for institutional trading.

Quantitative trading in a solo capacity is basically a myth. Most of those who actually have a non-trivial attempt at it think they're doing it because they're working with a large amount of data and doing "backtesting", but that's well below table stakes. To put it in perspective, that's like saying you wrote and maintain an internet-wide search engine half as good as DuckDuckGo. It's a meteoric amount of work for one person to keep all aspects of an algorithmic trading strategy running smoothly.

Can you define "table stakes" used here? The tone is a bit dismissive, but if there are individual investor doing algo trading with a few $100k and making some multiples of 10% gains annually, in bear and bull markets, seems like non a myth to me? Ie a couple of these people post here on occassion.

> a couple of these people post here on occassion.

I'd be frankly shocked if there are more than five people active on HN who are "doing algo trading with a few $100k and making some multiples of 10% gains annually, in bear and bull markets" on their own, with no outside capital. There might be folks automating some kind of trading that relies on fundamental financial analysis empowered by information asymmetry, but that's not really the same thing (though it's also hard). The implicit idea of "algorithmic trading" is that an automated trading strategy can capitalize on an (empirically true) hypothesis about the world. That means it should be consistent; automating certain deployment, execution and risk management processes isn't sufficient for algorithmic trading. That's just empowering the trading process with software. You can't really deduce a rigorously defensible and profitable trading algorithm just by filtering a giant amount of data through a backtesting engine.

As for the larger question, table stakes for a quantitative trading outfit is typically 1) a strategy research team that identifies and analyzes opportunities primarily via mathematical modeling, 2) a strategy implementation team that develops and deploys the algorithms which capitalize on those opportunities and 3) an infrastructure team that develops the internal research, data processing and trading platforms used by the former two teams. More abstractly, these are three separate functions (research, implementation and maintenance) spanning several core competencies (advanced math/theory, software engineering and reliability). Backtesting and whatever flavor of the week definition of "big data" are generally involved, but they're a relatively smaller part of a much larger engine in the context of quantitative trading. Backtesting in particular is extremely sophisticated and, in some ways, largely automated in the context of professional funds.

Theoretically one person (or a very small team) could be competent at each of those functions, especially if they're working with a relatively small amount of capital and targeting market inefficiencies that are too small, niche or illiquid to be productive and profitable for institutional trading. But that kind of individual almost certainly cut their teeth in an actual firm before going solo, and if they are working solo then they're doing way more than just finding useful data, using it to deduce a trading strategy and backtesting that trading strategy across historical data. It's comparatively easy to develop an algorithm that takes advantage of information asymmetry using a combination of domain knowledge, intuition and some untapped data source. But insofar as quantitative trading means something other than a marketing term, that's an insufficient level of modeling and an orthogonal use of data to satisfy the bar.

The other obstacle is that the stars kind of have to align in a very strange and particular way for someone to be not only competent enough, but incentivized enough to crank this entire Rube Goldberg machine on their own and with their own capital. Assuming their skills are generalizable, a hypothetical person who can be profitable while doing all research, implementation and infrastructure work on their own can likely pull down mid 7 - low 8 figures in annual compensation at a number of different hedge funds with much better career stability, financial security, personal risk and work life balance. If nothing else they could probably scale up and hire 10 or so others to found their own fund, and it would be strictly irrational not to (unless they're already capacity constrained, in which case they can't).

To clarify; if you read this persons history, they have automated a strategy that’s been very profitable, according to them. Is this algorithmic trading in the same sense you mean?

Bingo !

Although there are a lot of platforms for trading crypto, when it comes to arb, I found that you need to have control over a lot of things, most importantly:

- which data center you host your code in - the way you get data from the exchange - not just prices and order books, but also current balances and orders - the way you push orders to the exchange

With "the way" I mean mostly which APIs you use and how, the last few milliseconds of optimization for me were usually gained by making multiple simultaneous connections to the exchange and trying to figure out the fastest one.

For my rebalanced index-like portfolio I made a little script initially and then turned it into a side project, plug: https://nazcabot.io

During the december craze: very profitable. Now: better than not trading.

If I were american and had to pay higher taxes on short term gains, it would probably not be worth it.

I'm unique hybrid as I have a MBA (finance) before the great depression and because of the great depression I couldn't find a job. So I taught myself how to code (~2010) and have been working in code ever since, while still doing some algorithmic trading on the side.

I build custom trading systems on the side. Interactive Brokers is the only firm that I have found that has a good API. TD ameritrade seems to be starting in that direction. As for Trader9, well best of luck. For a little self promotion...

http://www.strategic-options.com/insight/the-best-and-worst-...

Since you've been alive for over 100 years, what's the key to a happy life?

Thank you for the api list! In general the IB api is great to work with but it has some smaller annoying bugs. For example the market data api suddenly stops sending updates for the VIX and requires a re-subscribe.

Disclaimer: I've worked for two of the largest HFT firms in the US over the course of the past 11 years.

The way to break into finance, besides some luck or a recruiter, is being exceptional at what you do. If you write C++, be the best C++ developer you can be. If you tune Linux servers, you better know every sysctl knob literally in and out AND where in the kernel those are actually applied. If the knob doesn't work, perhaps you'll need to write your own (kernel patch) to toggle a specific tcp/ip tunable. I breathe Linux in my sleep because I've always found it fascinating and got lucky when a random recruiter found me while I was working on the Core Systems backend Linux team at Ticketmaster. Then it is simply a matter of putting your resume out and/or finding a recruiter who works with those firms. Some of them are more niche. Jane Street Trading, as an example, requires every employee to write Erlang. Being good at erlang isn't super common so that might be an "in". Two Sigma does a lot of open source work (they have a github and have contributed a lot of stuff to both the Mesos and Kubernetes communities). Honestly man, just do some research, find something you want to learn, and strive to be the best.

https://www.scheller.gatech.edu/degree-programs/interdiscipl...