NuclearPhynance Archive

> I wonder if you're applying the idea to a strategy that belongs to the subset of strats for which the method will be least helpful?



Maybe but it wasn't obvious in advance of trying this. Just because the strategy is known to eventually recover doesn't mean that there is no way to modify sizing to mitigate drawdowns w/o sacrificing performance. And, just because I couldn't figure it out doesn't mean it's not possible.

@ronin



> Stop losses don't help, and they will ruin any strategy.

> You say "positive return autocorrelation" like it's a good thing. It's not.



While I have seen it myself in the real world, could you please give us some sort of mathematical explanation of this?



Thanks.

> some sort of mathematical explanation of this?



Well, I don't know how mathematical you want to get.



In a nuthsell, stop loss on a long position makes you sell low, buy high. I.e., you are paying gamma. Short convexity. Every day you have a negative contribution to the pnl from paying gamma. It is a negative bias that you have to overcome through your superb stock selection.



Contrast that to buying low, selling high - here you make gamma. Every day you generate a bit of positive pnl, but every once in a while you are run over when your "buying low" becomes "catching a falling knife". But then, this is something you can diversify away. Each single-stock-falling-knife is worth only 1/n of your portfolio. You are still making a bit of gamma on each of them every day. So your upside is n/n*gamma, your downside is 1/n.



The risk that remains is a system-wide falling knife, which is the only thing you can't diversify. That's n/n. But that only really happens every ten years or so - with a big enough basket, you can pick up enough single stock gamma during those ten years to keep your expected pnl positive.



On the other hand, long gamma can't be diversified away. There is no mathematical way to generate positive pnl expectation - you have to rely on your stock picking ability. That's orthogonal to mathematics.



> "positive return autocorrelation"



Positive autocorrelation generates fat tails - kurtosis. You never have just one positive or negative return - if you have one, you have several. I.e., fat tails. Which is a bad thing in trading strategies. If I wanted risk in the wings, I'd buy catastrophe bonds.



But then you overlay a stop loss on your fat tailed strategy. So you make negative runs shorter, but leave positive runs long. In other words, negative skew. You are losing a bit of money every day, but counting on positive runs to get you back to positive. What happens if you miss one positive run every once in a while? Or catch it too late? Or keep it too long? Antifragile it ain't.



But then, yours isn't just any inversely skewed strategy. Your inverse skew is engineered by overlaying a stop loss. Which has negative gamma. See above.



So you have a massive negative bias hill to climb just to break even.



At one point you just go "no, this is just going nowhere."

Have read chapter on electronic trading.



Example of fitting model to past prices which fails to perform on future prices with embedded mechanics of (past again) risk aversion resembles banks a lot.



It is worse - risk crowd imposes harder limits (even cultural) than a single person.

@nikol - for clarification - fitting model to past prices (not returns)?