Quote from MAESTRO:
I am sorry, but none of you above are correct what so ever. The solution is in Bayes' theorem and the most vivid illustration of it is presented via the Monty Hall paradox. Please educate yourself.
MAESTRO
http://en.wikipedia.org/wiki/Monty_Hall_problem
http://en.wikipedia.org/wiki/Bayes'_
Hmmm, I had a couple of questions --
Is it necessary, to apply Bayes theorem, to assume that the OP's 65% winning rate has wins randomly distributed throughout the time series, and continuing forward?
In the material I've studied on Bayes theorem, the examples usually have conditions that exist at the same time (eg, false or positive test result and having or not a disease; choosing from 3 doors with a prize behind). Does it also apply to sequential events?
Do we have to assume the 65% win rate is somehow real or constant, and not overstated by an issue like small sample size?
Bayes theorem is based on conditional probability, but wouldn't trade outcomes be independent?
I suppose to test independence we'd have to see if these are true:
P(next trade wins | previous trade lost) = P(next trade wins) ??
P(next trade loses | previous trade won) = P(next trade loses) ??
thanks
