Comment by dmmalam | Hacker News Reader

Comment by dmmalam | Hacker News Reader

Being pedantic, 4000 trades a day isn't HFT. This is stil algo trading, of which HFT is a subset.

I consider HFT to be any strategy where speed itself is the what gives the edge. Colocation is usually a prerequisite, though not sufficient. It's a shame HFT gets all the attention, when it's really a tiny portion of trading activity. Algo-trading in general is 70%+ of market activity in the US.

Also limiting trades isn't really adequate risk management. The tech exists to very accurately model your exposures. This is something I see underdeveloped a lot, and what separates the top trading firms from the rest.

Still I commend you creating a model, working out how to test and execute it automatically and actually trading your own money.

I really think more hackers should be actively managing their money, (in general, not like in the article). We have these amazing liquid markets, all time low spreads/commissions, products like ETFs/derivatives to accurately and cheaply execute a given strategy, and a huge increase in tech to model risk, but personal personal investing is the same as the 60s.

In the US, HFT is mostly synonymous with "all out tech war, flooding the order queue so your less-equipped peers get lags". (Nanex publishes analysis on these events, which are not occuring several times a month and keep accelerating).

In Europe, HFT is mostly what OP describes, because they have reasonable control (e.g., you have to have one execution per 10 orders or pay a fine; in US exchanges, you can sometime finds 10,000 orders submitted in 3 seconds, hundreds of thousands per hour, with 10-20 executions).

> Also limiting trades isn't really adequate risk management. The tech exists to very accurately model your exposures.

That's basically what AIG did with copulas. Unfortunately, the assumptions in these models tend to break during crisis, when correlations go to one. And AIG went bankrupt.

Limiting trades, done correctly (mathematically AND legally) is the ONLY way to do risk management properly. With more assumptions, you can have "more efficient" risk management in terms of leverage (e.g., you can net S&P and RUSSELL exposure by assuming their correlation structure) - but as AIG has shown, that does not mean you are doing a better job of managing your risk.

Some US venues impose fees for having low fill rate. As far as I know, most US HFT firms' strategies are not based on creating latency by rapidly submitting and canceling orders, but the bad behavior of some players is much more visible than the orderly behavior of others.

When I say limiting trades, I mean naively saying 'I will have at most x positions outstanding'. Each trade has an associated risk (variance), that interacts in complicated ways in a portfolio, which I'm sure you know.

silly example, 100 small positions could be less risky than 1 large position or, 1 long, and 1 short trade will cancel each other out and create a riskless portfolio (with 0 return).

You need to have a risk budget, account for each trade, and work out the risk for the composite portfolio. Obviously this is not fool proof, but it's a way better approximation of the real world.

> interacts in complicated ways in a portfolio, which I'm sure you know.

Yes. And assumptions about this are bound to break at the most inopportune moment, see e.g. AIG, which I already referred to. Read about the "copula model" disaster, as your statement indicates you are unaware of its details. https://en.wikipedia.org/wiki/David_X._Li#CDOs_and_Gaussian_...

> 1 long, and 1 short trade will cancel each other out and create a riskless portfolio (with 0 return)

This is true if and only if the long and short are in the same exchange, AND exchange rules allow netting long vs. short deterministically. Otherwise, you have counterparty risk. E.g., you can be long SPY and short SP contract (in equal underlying), which would theoretically mean your only exposure is interest rate changes (and sometimes not even that!)

However, since this is in different exchanges, it might happen that during a flash crash, your SPY position will be liquidated for insufficient margin at a low price, but then the price bounces back, and you've lost money on a perfectly hedged position.

OP's model (limiting exposure and assuming the worst, if I understand correctly) is not statistically efficient use of margin, but it's way better at actually managing risk than any statistical model.

I don't know the exact definition of HFT but I did run my algorithm from a server collocated with my broker close to the exchange. I modelled lag time in simulation and not having it collocated certainly would have hurt.

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