And I'd say that this situation is an advance. 20 years ago when I was a naive techie working in trading, most of the trading strategies I saw were tested by some young, high-testosterone guy wading into a pit, shouting a lot at people, and feeling vindicated if he made some money. (Or, in a pinch, feeling vindicated by whatever positive signal he received before getting creamed.)
For those interested in this, I strongly recommend Nassim Nicholas Taleb's "Fooled by Randomness", which gives the reader a feel for how much supposedly mathematical and rational finance runs on intuition, survivorship bias, and plain bullshit.
"Most trading strategies are not tested rigorously enough"
After having spent many-many years in the financial sector, I don't even know whether I should laugh or cry. :) The industry is not based on science, well, 99% of it isn't. Traders can be considered being the master of the universe just because pure luck. Well-researched, tested strategies are thrown out because they're not profitable enough to the senior management. If a model seems to bring in high profits then even the very makers of the model do not want to let it into production, the management will just use it because nobody cares of tail risk. And so on.
"Most trading strategies are not tested rigorously enough" After having spent many-many years in the financial sector, I don't even know whether I should laugh or cry. :) The industry is not based on science, well, 99% of it isn't. Traders can be considered being the master of the universe just because pure luck. Well-researched, tested strategies are thrown out because they're not profitable enough to the senior man…
The other thing that strikes me as bizarre is the way the everything seems so coupled. Its like the exact opposite of good software engineering - we build the systems to be so interlinked that everything affects everything else. Like when the oil price came down, it was threatening to put the UK (a net importer) into recession.
I don't know anybody that uses anything as weak as 2-sigma results in HFT, at least. Most of our valid signals have 20-50+ sigma.
Since you seem pretty well acquainted with HFT, I'm curious about how much of a constraint capacity is? From what I understand the amount earned per trade is very small (this paper[1] suggests $1.45 per $10,000 traded). And since HFT is already a large fraction of the daily volume it seems that the natural way to increase profit (i.e. just trade more) isn't an option in most cases. [1]: http://faculty.haas.berkeley.e…
$1.45 per 10k traded seems really high. A good S&P futures strategy (one of the biggest products in the world) is typically going to make 60-80 cents per contract traded. Since each contract is approximately 100k, thats closer to .06-.08 cents per $10k traded? Even for equities (I don't touch US equities, so I'm not entirely sure how good the best strategies perform), making a full price tick per contract is still In any case, capacity is probably the first constraint you hit. Most firms have reasonably accurate simulations, so most HFT strategies are scaled up to as large as they can possibly trade (ie. quote the largest amount passively or aggress with the largest amount you possibly can) within a few days of being released -- once you can confirm that your live trading is at least mostly matching simulation, you usually try to simulate the maximum possible size it can trade and just start live trading that. Since you're typically scalping a tick at a time, your maximum size is typically some fraction of the zero level bid/offer -- relatively small. Typically the way you scale up is either have better execution (know when to size up/size down appropriately) or better prediction quality -- since you're adversely selected, your bad trades get filled at a much higher percentage than your good trades, so as your have better prediction quality, a smaller percentage of your volume is bad and you can start to fire larger and larger.
When trading real money in a real market, predictions based on historical data go out the window. Historical data will never be able to truly simulate manipulation or sympathetic, symbiotic or parasitic relationships. Ever back-test a trading system that simulates a Market Maker letting low block go under the bid or dialing down the sensitivity of the bid vs. the ask? Speaking from experience. That's why I'm developi…
> That's why I'm developing an algorithmic trading system based on sympathetic, symbiotic and parasitic hidden connections.
Since you seem pretty well acquainted with HFT, I'm curious about how much of a constraint capacity is? From what I understand the amount earned per trade is very small (this paper[1] suggests $1.45 per $10,000 traded). And since HFT is already a large fraction of the daily volume it seems that the natural way to increase profit (i.e. just trade more) isn't an option in most cases. [1]: http://faculty.haas.berkeley.e…
$1.45 per 10k traded seems really high. A good S&P futures strategy (one of the biggest products in the world) is typically going to make 60-80 cents per contract traded. Since each contract is approximately 100k, thats closer to .06-.08 cents per $10k traded? Even for equities (I don't touch US equities, so I'm not entirely sure how good the best strategies perform), making a full price tick per contract is still In…
Thanks, that's really interesting. I hadn't even thought of the adverse selection problem. And you're right the $1.45 was high. I looked at the paper again and that number was before adding in costs. With trading costs the number was much lower, but still profitable (can't remember the exact number off the top of my head), and that paper was just US equities.
When trading real money in a real market, predictions based on historical data go out the window. Historical data will never be able to truly simulate manipulation or sympathetic, symbiotic or parasitic relationships. Ever back-test a trading system that simulates a Market Maker letting low block go under the bid or dialing down the sensitivity of the bid vs. the ask? Speaking from experience. That's why I'm developi…
> That's why I'm developing an algorithmic trading system based on sympathetic, symbiotic and parasitic hidden connections. That sounds fascinating. Do keep us informed!
To what extent does the industry use Bayesian methods? It seems to me that, for a given trading strategy, trading companies are really interested in the mean profit impact of the strategy and its distribution (e.g. "there is a 10% change we will lose more than $20M"). Bayesian methods will naturally produce such an answer.
Bayesian methods won't magically solve all problems (e.g. fitting to historical data) but could make the assumptions more clear.
20+ sigma is mostly kidding yourself about assumptions. That said, the 2-sigma tests are for client-facing algos, where the goal isn't to build wealth for the client, it's to generate fees.
Agreed, errors are non-normal so 20+ sigma is practically false. But it's not too far off -- I've seen strategies that never have a down day and have daily sharpes of 2.5-3.0, and those setups will generally have incredibly high sigma values even after accounting for non-normality (and other assumptions).