<story><title>Kelly Criterion – how to calculate optimal bet sizes</title><url>https://fhur.github.io/notes/articles/the-kelly-criterion/index.html</url></story><parent_chain></parent_chain><comment><author>YossarianFrPrez</author><text>It&amp;#x27;s well worth implementing and testing out the Kelly Criterion. It&amp;#x27;s super simple to code up in a Jupyter Notebook so that you get to enter an amount to bet each time. When I tried it, I found my own psychology changing as the bets continued, even when I knew the coin&amp;#x27;s bias. It&amp;#x27;s a really great demonstration of the difference between a) intellectually knowing the optimal strategy, and b) what actually happens.&lt;p&gt;A bet on a biased coin paradigm was actually tried in the real world with finance professionals, with a cap on the maximum payout. The results are described here: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1701.01427.pdf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1701.01427.pdf&lt;/a&gt;
It&amp;#x27;s pretty interesting. (Note though that the &amp;quot;average returns&amp;quot; reported hide a lot of variation.)</text></comment>
<story><title>Kelly Criterion – how to calculate optimal bet sizes</title><url>https://fhur.github.io/notes/articles/the-kelly-criterion/index.html</url></story><parent_chain></parent_chain><comment><author>fairity</author><text>Perhaps I&amp;#x27;m misunderstanding, but the post says that the optimal bet size f = 1-2p (where p is the probability of winning).  But, this seems backwards.  As p goes up, you should be betting more.&lt;p&gt;Shouldn&amp;#x27;t the optimal bet size, f, be 2p-1?</text></comment>