反向凯利标准？
因此，大家都知道有多少投资者和交易者使用凯利标准来计算交易最佳的头寸规模？我在想是否有可能，因为大多数人比正确的是正确的，而是使用凯利标准来最佳空间？
例如，简称吉姆·克莱默（Jim Cramer）的所有建议或一些卑鄙的卖面研究(短贸易结构的prob use a bit of discussion) and then position each short based upon the Kelly criterion? A possible future step would then be to design a trading system around this strategy and structure some hedges to remove risk like beta. Theoretically, in the long run, wouldn't the portfolio result in a net gain?
当然，我并没有真正深入研究Kelly Criterion背后的数学，所以如果可能的话，这只是一个突然的想法，今天我想知道这是否是为了好奇的目的，这是否可行。
评论 （5）
其他问题：L/S对冲基金（想想，通常的嫌疑人毫米/SM字段）使用Kelly标准？
kelly just tells you how to size bets based on your statistical edge
在长/短期股权世界中，我从未见过任何有意思的方法来得出您“统计优势”的估计（例如，我是52 vs 60％的可能就在这里！），如果您与PMS交谈，有些人会知道这个概念，但实际上没有人根据我的实习经验使用它
不要以为这种方法对于短销量而言是超级差异，这只是您尝试找出边缘然后使用凯利（Kelly
凯利（Kelly
one way you could figure out your statistical edge w kelly is to look at your trading history, but that assumes you already have a large enough sample size
凯利（Kelly）与短范围的系统策略/高频信息更为重要，因为您实际上在您的统计边缘上获得了大量有意义的数据
if you are doing anything longer horizon than calling the next quarter, idk how you would get remotely meaningful data on ur statistical edge without waiting like a couple decades
I agree with the above, but I would just like to add further caution to blindly using Kelly. It's basically sizing expected value vs. r/r. If you get overestimate the r/r it'll tell you to put on a big position (even if you use fractional Kelly) and then you'll get annihilated on the downside. Always think about the *noise* in your estimation of expected value and risk/reward. This is the same reason MV optimization doesn't work. I'd encourage you to keep things simple and size by proportional expected (idiosyncratic) return where you choose a number of positions (+ other factors) to目标卷。
yep
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