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That depends on what you mean by "platform." Professional quantitative traders do not use services like this one. Virtually all of them - profitable and otherwi

That depends on what you mean by "platform." Professional quantitative traders do not use services like this one. Virtually all of them - profitable and otherwise - work within firms that have separate teams for strategy research, strategy development, infrastructure, IT and internal software development. Typically there are sub-teams specializing in research and implementation (each) for risk management, execution, data processing and portfolio optimization. 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.

le-mark 8y ago
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. 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.
- throwawaymath 8y ago
  Before I answer your larger question, I'm going to outright contest this particular claim: > 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).

le-mark 8y ago

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. 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.

throwawaymath 8y ago
Before I answer your larger question, I'm going to outright contest this particular claim: > 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).