All language subtitles for 6. Weighted, Exponential, And Adaptive Moving Averages

So with our discussion about moving averages, we've been talking about the simple moving average,

sometimes Sharona's some a on a chart, the simple moving average, and but there are other moving averages

that you could use as well.

And so there's a weighted exponential adaptive.

There's these are the main categories of moving averages beyond the simple moving average.

And they work in the same manner as far as like price crossovers and all that.

But let's say you want a moving average that's more reflective of the most recent price.

We know what a simple moving average is, an equal distribution between that price range.

But it could make sense where you'd be like, I'm more interested in what's happening.

More, Closter, more, more, you know, more weighted towards more recent pricing.

So, you know, I think, well, I'm not buying 200 days ago.

That's maybe too much like I'm looking for something a little bit more and I want to use a winning average

that might take advantage of that.

Now, a couple of ways you could do it.

One is you could just reduce the number of days and the simple moving average that would give more weight

to something that's a 10 day simple moving average, gives more weight to one tenth versus, let's say,

a 50 day moving average.

What's one fiftieth?

You know, so you'd have more weight towards that more recent pricing or more weighting, really, with

all the pricing over that time frame.

But you can also use moving averages that actually you intentionally put more weight on more recent

days.

And that's what we're going to talk about here for some of the ways that you can do that or basically

moving averages that you can choose to use if you like to do that and want to put more weight on it,

which, you know, makes sense as far as doing that.

So, first of all, look at, you know, think of like a weighted average.

Right.

And we think about weighting averages.

We could look at prices over, let's say, a period of time.

Here's an example.

Just from the math standpoint, this is over five days.

You can see the prices.

The furthest back price was twenty twenty dollars or rupee's or whatever units you're using in the more

recent one is thirty one and then there's a thirty three there.

So you can see the more recent prices are much higher than the four ones further back.

Now the simple moving average or the simple average on that would be twenty six.

Right.

You're one thirty.

Add them up divided by five now weighted average would put a weighting factor towards, towards those

numbers basically.

So the ones that are most recent would have a heavier weight to them than ones that are further back.

So if we look at that first bullet point there you can see the twenty say dollars has a weight of one,

whereas the thirty one has a weight of five and you do the math thirty one times five and you can see

the math there, how that breaks out and then you're going to divide that by not the you're going to

break down not by the number of days, but you're going to you're going to break that out by the weights,

you know, the number of weights.

So it wouldn't be five because it's that would be a simple average.

Are you divided by each of those weights?

One, two, three, four or five?

If it was 20, it would go up to 20 different weights.

And thus, mathematically, you would actually wait the more recent time by dividing 420 one divide

by 15, in this case, doing the simple math part or the math part would show that our weighted average

would be equal to twenty eight twenty eight point six, to be exact.

Now, here's the good news.

You don't have to calculate any of that when you're trading or doing working with your trading platform.

You just have to select that.

You want to use a weighted moving average as opposed to the simple moving average.

But I want to know it was important to understand that how it kind of works mathematically that they

are putting more weight on the more recent days when you do a weighted moving average.

Another one that's very popular is an exponential moving average, which puts even more weight to recent

days.

You know, so the weighting given to the most recent price is greater for a shorter term extended moving

average or M.A than for a longer period.

So, for example, they might apply multipliers of, let's say, an eighteen point one eight percent

multiplier is applied in the most recent price data for a 10 day exponential moving average.

Then for 20 to e-mail, that would be nine point five to as far as the multiplier is that they're using.

Again, you don't have to calculate how this will all be dramatically for you, but think that simple

moving average is pretty is an equal pattern, equal weighting, weighted moving average weights, the

most recent days, heavier exponential weights, the most recent days, even heavier still then weighted

moving average.

So if we looked at this on a graph, for example, we take our our graph.

We've been looking at our chart.

We've been looking at and you can see I've got the moving average over 50 day period.

You know, that would be a simple moving average.

And I have an exponential moving average of 50 on there.

So it's the same time frame.

But you can see how they might differ as far as in this case, the only differ slightly in terms of

the price.

Sometimes, especially, we use different time frames that could weighted even higher.

But this way you can see as far as, you know, the different weighting or you can use a different type

of waiting around then and weighting more recent prices versus prices longer back.

You can even see how the crossover at times to another one is an adaptive moving average, which is,

you know, kind of kind of a push and pull aspect worth looking at a short timeframe to identify the

beginning of trends quickly and as long as necessary.

You want to construct it to avoid constant by and selectivity or whipsaws.

That's where these adaptive moving averages came in, because with any of the other ones we talk about,

you can still have that frequently trading whipsaws.

So sometimes you might want to adapt to a smaller number of days and then sometimes a higher number

of days.

But you can kind of play around with it, but you don't want to force it because you're not sure which

is right as far as the right days.

So adapting, moving averages, uses, you know, calculation is complex and it's all automated calculations

to do this by determining the series to make the current price more apparent.

So its weighting as it's actually determining whether it's up or down and it's actually adjusting the

trend in the series of numbers is what it's doing with an adaptive moving average.

So some complexity behind that will, you'll see on trading platforms are probably the most popular.

One of them is the Koffman adaptive moving average or you'll see listed probably KMA named after Juman,

Perry Koffman.

So and what I'll do is we'll closely follow prices when the price swings are relatively small and the

noise is low.

Right.

So it's kind of adjusting for that.

And it's got a complex formula, a lot of multiple layers on it.

And they actually have some recommended settings.

Sometimes you can change the settings, but they have recommendations around those settings.

You know, for ten being the number of periods to the number of periods for any you a constant thirty

no periods for a slower M constant.

Those would be the traditional numbers in there and 10 to 30.

If you choose a coffee moving average in your platform, it'll actually prompt you for that.

Have a link there.

If you want to really learn the whole math behind it, you can kind of find that out as well.

So here's a here's an image or a chart here where it's showing that the coffee moving averages, you

know, it's looking that when prices spike there, it actually smooths it out even more.

And you have that town trend kind of coming down.

And then when the prices turned up over 50 percent, then it really started to impact and was worth

waiting that more recent price changes versus an outlier.

It's more waiting, a little bit more of a longer part of that of that average with an adaptive measure

is what it's doing.

So with each of them, they all have their strengths and weaknesses.

Simple moving averages would be the easiest to understand.

We in most used, we didn't moving averages.

You know, they're winning those most recent data.

Exponential takes even more weight on the comp and tries to use some more complex formulas around that,

you know, as far as, you know, the type of different types of moving averages.

So when you're looking at moving averages, you know, that's one of the considerations you want to

think about is which one you want to use.

There's some other considerations, too, which are going to talk about in the next lesson.