Dislikedin your last example i understand : at 14h GMT compute return for the last 9 day bar return at the same hour (14h gmt return in pip or percent) -- >this is the input like [a,b,c,d,e,f,g,h,i] Output is the return from now + 70 hours (delta from now) and your add 1 is TP is reach first or 0 is SL is reach first (2 output vector) like [X,Y] this for 225day.Ignored
the input is the return of the past 9 bars defined as (Open[N]-Open[N-1])/Open[N-1] where N=0 is the currently used entry bar.
The output contains two values, one for the profit/loss of a short trade and another for the profit/loss of a long trade. It is not simply 0/1, it is the actual value in absolute price terms, using absolute values and doing regression instead of classification is critical. So for example an output vector might be 0.0034,-0.0043 which means that a long trade would have hit a TP of 34 pips and a short trade a stop loss at 43 pips. As I said before it is of vital important not to use classifiers but regression models, this will become even more important later on.
The linear regression model should be retrained on each bar where a trade decision is taken. I repeat, do not use classifiers or you won't be able to reproduce these results.