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If the outcomes are i.i.d., maximizing ev(log(wealth))) yields an optimal fixed fraction strategy. If they are not i.i.d., with no restrictions on the distribution of the process, maximizing the conditionally expected log given current information is asymptotically optimal.
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what do you mean by "optimal"? I think (?) the following strategy will beat your proposed strategy more than 50% of the time: a) bet 1.1x Kelly until you're in the 75th percentile outcome that Kelly would be at or the 25th percentile b) afterwards, do Kelly
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Replying to @_charlienoyes @danrobinson and 2 others
what do you mean by "optimal"? I think (?) the following strategy will beat your proposed strategy more than 50% of the time: a) bet 1.1x Kelly until you're in the 75th percentile outcome that Kelly would be at or the 25th percentile b) afterwards, do Kelly
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That is simply not the case, as long as the outcomes are i.i.d. (which is why my claim is the expected log, not the conditionally expected). Suggest a gamble dynamic and I will let you do literally anything that you want, and whatever you do will be dominated in the limit.
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let's go with even money 60% bet note, though, the following: twitter.com/SBF_Alameda/st Alternately I *think* there's a simple strategy that doesn't require as many simulations, will post soon
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Replying to @SBF_FTX @_charlienoyes and 3 others
@_charlienoyes if that is what your claim is then I'm happy to go with "go all in every time" though note that, the further out in time we are simulating, we'll need to simulate exponentially many cases in order for mine to win
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