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You're not going to like the answer.
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You're right, I don't like the answer. See the
Bayesians vs frequensists thread for why the calculations that are given in that post may not be the correct way to go (they overestimate the true win rate of a player who has a positive return over a number of hands).
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Well, if you don't like my answer, you're going to like the Bayesian answer even less, because the Bayesian estimate of win rate is a weighted average of the estimate under the prior distribution and the estimate under the posterior distribution. If you assume, as in the post you've linked, that your win rate under the posterior distribution is negative, but in fact, it is positive, then you will need a larger sample size than you would using a classical approach to overcome what is essentially a bias you've introduced by making a false (or at least a very conservative) assumption about your prior distribution.
My question is, Why would you treat yourself as a randomly selected member of a the general poker-playing population? For one thing, you're not randomly selected, you're yourself; and for another, you don't seem to fit the model, as, presumably, you've studied books, participatd in these forums, and have thought a lot more about correct poker playing than the general population.
To my way of thinking, using Bayesian statistics to estimate your own win rate is falling into Fancy Statistics Syndrome. When in doubt, follow the KISS principle: Keep It Simple, Stupid.
Jay