thomsch wrote: ↑Wed Mar 20, 2019 10:05 am
I take it you would use back testing to know whether a strategy was +ve expectancy in the first place? Do you know how to do this for football?
A quick (cold) example.
I've often wondered whether the Super6 "what people are saying" is connected to the pricing of the match.
For example, if 94% of participants think Chelsea vs. Everton, is a win for Chelsea - is it profitable to back teams (@ any price, @kickoff), if the "what people are saying" is >90%?
So, I'm gonna go out collecting the data, splitting it into in/out sample, optimise the variables (i.e. >90%), test it on the out-of-sample. & if the profitability is "similar" on the out of sample, it gets moved onto sim for a bit, still good, then live small.
Most stuff fails, when it reaches the out-of-sample data. I have very few things that genuinely pass that stage
Also, if someone could explain, how that idea above, gets turned into a "model" I'm all ears
thomsch wrote: ↑Wed Mar 20, 2019 10:05 am
This is interesting and something to look in to. I am by no means an expert with Excel, would it be easy enough to do?
I haven't done it myself yet; but I like the logic - & I'm pretty sure (done right), is better than any drawdown metric