121–130 of 254 posts
My question for everyone: Where do people get reliable data for back testing? Ideally I'd like data that goes back over 20 years. And with relatively few data integrity issues (e.g. does not exclude companies that no longer exist). Even more important: How do I know my data is accurate?
I made some good money (millions) in 2017 by algo trading crypto. Now in 2018, the bear market is on, but my pnl is still decent. I collected data, trained models, wrote execution strategies, automated everything. I was successful because I was moving fast, trying things, breaking things, etc. While crypto was and still is my turf, I think I could also do well in the stock market. The problem is that the entry barrie…
You made millions of dollars last year then applied for a series of senior developer jobs two months ago?
I know cases of algo traders coming from capital markets that have been so successful that they were banned in some crypto exchanges for using highly efficient strategies. One of the guys (he works in a top market maker during the day) told me he was making around 100btc/month back in Q1-Q2'17. Edit: Not saying it's easy to reach that level of profits, just that it's possible for professional/sophisticated traders.
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Trading volatility might imply that he's buying options in both directions. Big moves either up or down would be profitable. The only unprofitable move here would be no substantial moves in either direction. (In which case you lose your entire bet, but no more.)
> Big moves either up or down would be profitable. The problem is that much more often than not the “only unprofitable move” is the one that happens. Options in general are too expensive and that’s why selling “insurance” is profitable most of the time. Maybe he can identify consistently mispriced vol, though.
This was the method I used, as described in another comment.
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That is absolutely not within the definition of insider trading.
It seems pretty close? Insider trading is any trade that exploits non-public information, regardless of whether it's made by an employee.
That said, 99.99% out there claiming to have such a thing is just scam, crap, bs and/or non-sense.
But to your question: "smaller strategies" and "not be interesting enough for larger algorithmic trading firms": There is, but why would one tell??
We are seeing a number of market enthusiast coming up with trading strategies that work. Most don't have the staying power to get them to work enought to trade. Some do. Those that have the staying power often lack the financial resources to trade those algos for themselves. Problems = Capital, Access, Data, ... To overcome that some are turning to CloudQuant (where I work). The CloudQuant algo development environmen…
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> If someone says their algorithm relies on some sort of TA, run for the hills. Care to explain? I'm genuinely curious as I've had some success in this area. Curious if I should be aware of something that I'm not...
Excuse me for being ignorant, but what does TA mean in this context?
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Concur. You won't be able to beat players whose HFT systems are colocated in the same datacenters as the exchange. And you will not be able to beat those systems built using lower level languages like C++ or, worse yet, dedicated hardware. Source: 5+ years in HFT development
Why does a programming language matter in terms of algorithms? Can you elaborate on that?
Languages like python are immediately out, they make no attempt to be fast (which is fine for their niche). Even languages like Java are out, the JVM is too smart: it turns out that the algorithm needs to analyse a few thousand possible trades where the answer no trade before it gets one where the answer is yes, as a result Java will optimize for the common no path. In C++ the programmer will lie to the optimizer claiming that the yes path is the most likely even though it isn't! As a result code written in C++ will always have the yes path as most optimal: when a trade should be made the java program pays a CPU branch misprediction penalty and the C++ program will not thus C++ wins the trade. Of course for the above to work you need cleaver programmers who spend time at the profiler and know how to make the CPU work for them.
C++ has one other useful advantages over some of the competition: it won't waste CPU checking for things like dereferencing a null pointer. Speed is important, you should verify via other means (unit tests, formal methods static analysis, or have the checks that you run on dummy data and compile without them for production)
Thus C and C++ are the only useful choices: the compilers have good optimizers (this rules out a lot of other compiled languages), they don't use CPU to check for "can't happen" errors, and you can lie to the optimizer in useful ways. There are other advantages, but the rest are things that a good programmer could easily work around (ie write a new stat structure from your CS textbook)