One way to do this is to take the basic polling data which runs from early July to the the present and make Moving Average Calculations on that data. This basically applies a low pass filter to the data. It gets rid of the "noise" in the polling data. The downside is that it will lag the real time data.
So here are my plots of the "Trump Margin" for the full data set in blue and the 7 Day (Gray) and 14 Day (Orange) Moving Averages. What this shows is that Trump has mostly led in the race. He took a big jump up during the GOP convention but took a big downturn during and for some time after the Dem Convention. But the trend has been very definitely up for Trump since about mid-July to the present.
Take a look:
How long has this poll been around, and what is their track record of successful predictions??
In 2012, after the Republican convention, with lots of balloons and smiling Republicans promising everything for everyone, Romney took the lead. Then, after the Democrat convention, with lots of balloons and smiling Democrats promising everything for everyone, Obama took the lead. But, unlike Gallup (which in retrospect I believe skewed the poll averages badly), Romney never regained the lead. The final poll had Obama up by 2.6%, he actually won by 3.9%. I'd rather see Trump up by 10% now. But given the choice, I'd rather see Trump up 2.6% now than down by 10% now.
http://graphics.latimes.com/usc-presidential-poll-dashboard/
I think the poll data you’re starting with is already a 7 day moving average, so if you do a moving average of that, I’m not sure what you would call it!
Anyway, I get your drift, it’s useful to get rid of some of the noise.
The poll seems to have echoes like ripples like in a pond. If Trump has a real good day, then 7 days later the poll looks like he had a bad day, because the good day just dropped out of the average.