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## Comments

Possibly you could normalize values by expressing the current reading as a per cent of a moving average of Force, for instance

rank by [Force(21)/sma(21, Force(21))]

But, I'm not sure how that would work because Force can generate positive and negative values. Dividing a negative by a negative gives you a positive, but you wouldn't want to rank a symbol with extreme negative Force and MA next to a symbol with extreme positive Force and MA. You would want those at opposite ends of the ranking.

So, another possibility is Force divided by average volume.

rank by [Force(21)/sma(21, volume)]

This does seem to rank symbols in a more meaningful way - big positive Force values are near the top of the ranking and negative near the bottom. But, you can get symbols with high positive values where Force is falling from even higher Force values ranking next to symbols with high positive values where Force is climbing from lower values, and those are different technical situations.

But, maybe you can deal with that in the scan by requiring current Force to be greater than x days ago Force, etc., or whatever other characteristics you are looking for. For instance:

[group is sp100]

and [Force(21) > 0]

and [Force(21) = max(5, Force(21))]

// or you could do something like

// and [Force(21) > 5 days ago Force(21)]

rank by [Force(21)/sma(21, volume)]

:

divide by force appears not to work

divide by volume is ok ... change to 200 ma longer average volume

PPO readings are not subject to the price level of the security. Second, PPO readings for different securities can be compared, even when there are large differences in the price. See the ChartSchool article on MACD for information on signals common to both MACD and PPO.

rank by [daily PctChange(1,Force(13))] //shows change in past 13 days

also, could use monthly:

rank by [monthly PctChange(1,Force(2))] //change in past 2 months

also weekly seems to work as well:

rank by [weekly PctChange(1,Force(2))] //change in Force in past 2 weeks