91–100 of 118 posts
Earlier quoted context omitted.
> Speculation is a zero-sum activity between speculators I agree. > any individual seller's gain is some other seller's loss I disagree. A farmer can sell to a grocer and both can gain.
I think you don't disagree. The grocer is not in a seller in the same ecosystem as the farmer. The grocer is one of the external people who benefits from the zero-sum game the farmers are playing.
This type of thing has been done before: http://en.wikipedia.org/wiki/FatKat_(investment_software) http://www.idsia.ch/~juergen/finance.html http://www.technologyreview.com/blog/guest/25308/ I'm sure there are other examples. I think part of the problem is this guy thinks he can get rich quickly with just the right set of programming instructions.
But as these new techniques proliferate, some worry that promotion is outpacing reality. These techniques may be better for marketing than stock picking.
Earlier quoted context omitted.
So sad, so true. This is what happens when you play a zero-sum game. Even if you win, someone has to lose. When you play football too, if you win someone has to lose. And yet there are positive externalities, to wit millions of people watching the World Cup. In trading, the externalities might not be so obvious but they do exist.
Could you explain more please? Bonus points if you do it without using the phrase 'provide liquidity'.
You can keep adding data points to your spline/polynomial interpolation, but that doesn't mean the model will be any better. In fact, for the purposes of predictive power, it will probably be worse. I'm not extremely well educated on the subject, so somebody correct me, but these predictive models have always struck me as just complicated splines. Add a thousand data points, and the "predictions" for old datapoints l…
Some suggested in his comments that he should join a firm so that knowledge and experience could be shared towards finding the algorithm[s]. His reply to Henrik, "I’ve been wary about taking jobs at firms like that because of IP concerns. I always believe I am on the cusp of something great, and wouldn’t want to share my algorithms", is a factor in his progress, or lack of.
By working alone, he's more likely to earn 100% of nothing, when he could collaborate and earn a small % of a very large number.
That said, if he finds the algorith alone, then big congratulations and respect are in order for sticking to his convictions.
I don't think anyone can consider themselves a failure at 25, having spent 8 years learning a lot of programming, algorithms, data mining / machine learning and complex systems modelling.
It would be easier to think that if you hated what you were doing. But it sure doesn't sound like you do. So you just need to decide what you really want to be doing.
If it's algo trading, I concur with another poster that says go work for a hedge fund (or bank, or prop trading shop). It is super competitive, but they have the technology infrastructure and most importantly the capital. Getting in is not easy, but simply show them all your work (it doesn't work anyway, but is indicative of your skills and way of thinking). You will learn a lot, you may hate the people and environment, or you may love it even more. And then yes after a few years of experience you will most certainly be in a better position to go off on your own again (or do something totally different, by then you will really know if you like it or not!). Many top hedge fund managers / traders only started their own thing at 30, 35, 40, even 50... I know a dentist who became a prop trader. Anything is possible.
A PhD would be a great option IF it's for the right reasons. But if you want to do a startup (sounds like you might quite like the idea and you posted here on HN, so...): - you already live on ramen, so no lifestyle change there; - bootstrapping something can't cost more than losing money with trading algorithms; - you already have many of the requirements: coding / technical skills, low-cost living circumstance, love to solve tough problems and a huge amount of tenacity in the face of failure and overwhelming odds; - bonus: your interest in social systems modelling etc ties in pretty nicely with what's big right now and in the near future.
So if that is what you really want to do, go for it either alone (or find a co-founder), or find a small/medium startup to work for. To make the transition a bit more natural perhaps focus on ones that are data-driven and have machine learning / modelling at the core of their business. Think recommendations, systems modelling (www.flightcaster.com) and weather (www.weatherbill.com). There are many many examples of YC and other startup companies of this nature (many focused on the social network space).
Good luck in whatever you do decide to do with the next 60+ years of your life. On your deathbed you can post about whether you think you are a complete failure or not.
You can keep adding data points to your spline/polynomial interpolation, but that doesn't mean the model will be any better. In fact, for the purposes of predictive power, it will probably be worse. I'm not extremely well educated on the subject, so somebody correct me, but these predictive models have always struck me as just complicated splines. Add a thousand data points, and the "predictions" for old datapoints l…
Yes, that is the main pitfall with machine learning in general. Usually you do two things to mitigate it (but they don't fix it entirely): regularization and a test set. Regularization is some means of complexity control. You switch form trying to find the model that has minimal error on your data to finding the model that minimizes "error(model, data) + C*complexity(model)". There are many ways to measure the comple…
To wit, P(model|data)=P(data|model)P(model) / P(data), which is to say that you calculate the probability a model is true given the data you see based on the probability you see the data you see given a certain model, multiplied by the probability of the model you are using.
I guess the problem in all this is we want* the model to be very complex, but we can't tell that complexity from overfitting (and it may be that no model exists so the only thing you can do is overfit.)
Earlier quoted context omitted.
So sad, so true. This is what happens when you play a zero-sum game. Even if you win, someone has to lose. When you play football too, if you win someone has to lose. And yet there are positive externalities, to wit millions of people watching the World Cup. In trading, the externalities might not be so obvious but they do exist.
Could you explain more please? Bonus points if you do it without using the phrase 'provide liquidity'.
There is an economic progression from the switch from barter to money, to lending of money, to spread of risk by enabling minority ownership of multiple ventures (early corporations), to easier transferal of ownership in smaller pieces (allowing more of society to participate), to large-scale, low-friction, highly-liquid markets used by everyone, directly or indirectly. Each step further reduces economic friction, which lowers the threshold for economic activity to take place, causing more of it to occur, increasing the prosperity of the whole society.
Even if you lose money personally in the "zero-sum game" with unfortunate investments, you're still probably losing only a portion of the money you made working at a company that wouldn't exist if not for large, liquid, low-friction markets.
After eight years, I have nothing concrete to show for my efforts. So sad, so true. This is what happens when you play a zero-sum game. Even if you win, someone has to lose. Here's an idea. For the next 8 years, why don't you do something that helps others by making the pie bigger for everyone instead of just trying to game a bigger piece of a smaller pie for yourself. When you do for others, you always have somethin…
Calling trading a zero-sum game shows a fundamental lack of understanding of finance. We may have very negative views on the industry, but it exists because it serves many fundamental (and necessary) functions, including: * Accuracy in pricing shares (and it is important to know the actual value of a company for many reasons) * Enable market participation. In many illiquid OTC markets, it is all but impossible for an…
Define "accuracy"? Preferably in such a way that helps me reconcile it with events like the flash crash.
One of my biggest problems in understanding the financial markets is how you separate the signal (events reflecting actual change in company value) from the noise (speculation, flash crashes, etc.).
While I realize that there is real information (e.g. earnings reports) that goes into the pricing, it seems like there's also a hell of a lot of worthless noise (i.e. I see the flash crash as a massive noise spike).
It seems to me that separating the two is how people make money long-term.