All language subtitles for 009 Bollinger Bands_en

And this lecture, we're going to be talking about Bollinger bands, so what are Wallinger events?

Bollinger bands are going to be the first technical indicator we look at now.

What are technical indicators?

They are things that are drawn on top of chart or beneath charts that are computed using the prices

and or volume on the charts that are going to highlight something for us trying to tell us if something

might go down or might go up.

OK.

So if you go on treating you and you click here on Indicator's, these are where you're going to find

the technical indicators.

You're going to see there's a bunch of them.

Most of them are really bad.

And anybody can create their own indicator.

You can create one tomorrow and add it here.

Right now, Bollinger bands this one, which you can click on it and it'll look like this is one of

the most popular technical indicators out there.

And, you know, for people who know me or have seen my other courses, this is the one that I use the

most.

This is the one that across all firms that I've worked at, that is the most used by professional traders.

And there's a reason for that because it really makes a lot of sense for certain strategies to use that

indicator.

Now, like with all other indicators, you could get away with, not even using it because you're not

to draw any indicator, what you're actually doing is you're taking prices and volume and you're creating

that indicator based on a formula that you have.

Well, if you only need prices and volume, then why not just look at the prices on volume yourself?

Why do you actually need to draw the indicator?

The only thing that the indicator does is just highlight something that's already that you can already

deduct by yourself.

But, you know, it's important because you want these things highlighted for you.

That's the purpose of it.

So it just makes your life easier.

So what makes this indicator really good?

Let's explain what it is.

OK, and to do that, let's explain it.

True, an example.

Let's say this, let's say you are in math class and you just received your grades, your grades.

This.

Seventy percent.

Or let's say 60 percent, actually, let's say six.

And well, you know what?

No, let's say 7:00.

We'll go with some sun is going to be the.

Let's say you have 70 percent and then your friend, who's in the same school as you, is doing the

same math class, but with a different teacher.

He comes up to you and he's like, yo, I got 80 percent.

And you know that you're smarter than your friend, you know?

So you're like, how come, you know, is this guy smarter than me?

What do you guys think?

Do you think that because this person has a higher grade that he's smarter than you in that subject?

It's math in math.

Or is he better than you in this class?

Well, not necessarily because his teacher might be easier.

Teacher, your teacher might be really hard, way more strict gives you harder, you know, exam questions

or whatever, or his teaching style is worse and he doesn't teach well.

So that's not fair.

So what you're going to have to do is you're going to have to look at something else.

You're going to have to look at the average.

Or you can you can call it to the average of your class, so let's say, for example, you're the average

in your class is 60 percent.

You might say, well, that means I'm good, I have 10 percent over, you know, the group, what is

the average in his class is maybe 50 percent.

So, you know, you have 10 percent over your class, but he has 30 percent over his class average.

Now, does that mean he's better than you?

What do you think?

Again, this is not enough information to know you need another you need something else because you

don't know how you know how dispersed the grids are in this class.

You know, maybe in his class, in your friends class, you know, you have students that have zero

percent and students will have one hundred percent.

So there's a very high dispersion amongst the Greeks.

Which means that your friend isn't an outlier in this class, he's just another guy who has 80 percent,

maybe there's a few others.

A lot of other people have 80 percent of nineteen ninety five and a bunch of zero percent.

So he's not really an outlier.

And maybe in your class, everybody's clustered around the average of 60 percent.

People have fifty eight percent.

Sixty two.

Sixty one.

Everybody's clustered there and you have 70 percent.

So you're an outlier compared to your group.

And that would mean you're better.

So how would we be able to compute that?

What would need is we need to look at the dispersion of the grades, how far they are from the mean.

And to do that, we can use what we call the standard deviation.

And I'm not going to go into the maths of that.

But you can Google it at the center of deviation is going to tell you how dispersed the grades are.

Now, let's say in your class, the standard deviation is five percent.

And in your friend's class, the standard deviation is 30 percent.

Well, now we have enough information to know who's better.

OK, why?

Because now we can draw what we call the bell curve, a normal distribution which will look like this,

a normal distribution.

This is something that's used a lot in statistics.

And it looks like a bell curve and in the middle here, we are going to put the mean, which is you

and the mean is going to be, in our case, 60 percent of the average in the class.

And what is the normal distribution we see is that you are going to have 68 percent of people.

So here, about 68 percent.

Of people that are going to be one standard deviation around.

One standard deviation around the mean.

So in this case, one standard deviation, which is five percent above 60 percent, is going to be 65

percent and one standard deviation below 60 percent, which is going to be 55 percent.

So in this class, we know that about 68 percent of students should have agreed between 55 and 65 percent.

Now, if you want to look at two standard deviations around the mean.

So two standard deviations.

Around Amien.

Minus two standard deviations.

Well, another standard deviation is another five percent would be 70 percent and here would be 50 percent.

And here we're going to have, I think, about thirteen point something percent, let's say thirteen

point five, thirteen point five percent, and here's thirteen point five percent.

So in this whole.

S. here you're going to have about 96 percent of students, so if you have 70 percent of the grade,

you know, and here there's going to be like another two percent and another two percent here.

So if you have a 70 percent grade, you know that you're better than about, you know, all of these

people, which is like 98 percent of people.

That's a great, great.

It means you're an outlier.

Really good.

But in this case, this guy here is only one standard deviation above the mean.

And if you have one standard deviation above the mean, while you have sixty eight percent here and

13 percent and then two percent, or you're about 80 to eighty four percent better than everybody else,

you know.

So you're better than eighty four percent of people.

But this guy is better than 98 percent of people.

So he, he's better.

And this way of computing, if somebody is an outlier or how far this person is, is used, let's say

for example, here in CEGEP, in college in Montreal, this is what they use to grade students and give

them an R score, OK, that's what they do.

So they use that for in a lot of places because it's a fair model.

OK, and in statistics, it's used in F in everywhere.

For example, if you're looking at it, I can see the average height of a man is one point seven meters,

and then I can look at the dispersion of the volatility between men of their heights.

And then I can say, well, if you're one point eight meters, then you're taller than, you know,

whatever percentage of the population you can use it for, which you can use it for whatever.

If you go on Google here and I'm going to put the normal distribution, standard deviation.

So just normal distribution, standard deviation.

And you can go on images and you'll see it's.

Here, right, so you have the normal distribution and you have 68 percent of the population is going

to be within one definition of the mean, ninety five point four percent will be within two standard

deviation and ninety nine point seven percent will be within three standard deviations of the mean.

OK.

Now, you might be wondering, why is this guy talking about this, how am I going to be using it to

treat cryptocurrency?

Well, because imagine this.

Imagine I take a chart of prices.

Now, have, you know, the price of a cryptocurrency going up, going down and, you know, fluctuating

and then.

I look at it and I say, OK, well, you know what, what is the meaning of this process?

And I use something like a moving average, a 20 day moving average or a 20 period.

So I look at the last 20 periods and I do their average and I say, well, the average is going to be

the mean.

So in this case, maybe the average price of the last 20 periods here was one hundred.

That's the average.

Right, and then I look at the volatility of the scandals of the last 20 periods and I see that the

volatility is maybe five dollars.

And I say, OK, well, look, the volatility is five, I want to draw two lines.

Well, first, I want to draw the moving average in the middle here, which is going to be the dimin.

And I want to draw two lines and one line is going to be two standard deviations below and one line

is going to be two standard deviations above.

Right.

So basically what I'm doing is I'm drawing a bell curve here around the chart.

Let me just raise this here, give a the more space I'm drawing a bell curve.

Here.

And I'm saying, look at two standard deviations above, I want to draw a line here.

And the two standard deviations below, I want to draw another line, and this is going to tell me that

there is a ninety six percent chance of prices remaining within this range.

And then if prices are way outside of the range here, I know that this is very unlikely and prices

reconversion or come back.

That's what it told me.

OK, so for example, if I go back on trading view here and you can put any chart, I can choose any

coin and you can actually you can do it on any time frame.

So I can select one hour.

And, you know, I'm looking at this and I add indicator.

I go here and I click Beebee for Bullivant.

It pops up here.

It is what this would tell me.

And if I double click on it, you see that they used a twenty period moving average for this middle

line, which is going to give us the average.

And they're using a two standard deviation to draw these lines, just like we've seen.

And what this tells me here, if I'm looking now, tells me while the average of the last 20 days is

this price, the last 20 periods, which I'm using one hour Candleshoe, the last 20 hours is this price.

And then based on the volatility of these last twenty candles, I am ninety five.

There is ninety five percent probability that the prices will remain within this range.

OK, so basically for you, as a trader, you might use this like this, you might be like, oh well,

look, prices here are way outside of the strange let me so let me not just keep holding it and you

might sell your position and, you know.

Right.

Enough prices will go down.

And if you want to buy them, you can be like, oh, well, this is way further from the mean, the

average, it's way lower.

So maybe I should buy it now because it shouldn't be that low and you might buy it.

And then here you might sell it again.

And when it's low you might buy it again and sell it and buy it and sell it by then and sell it and

here buy again.

So basically you can use this, you know, to tell you where prices are versus the average.

OK, now you can use a different standard deviations if you want to be ninety nine point seven percent

sure that prices are going to be within this range.

You can use a three standard deviation, which is going to be wider.

Right.

So the band's got wider.

So you're going to have less trades.

But I guess better ones in a way, not necessarily better, but more confident ones.

OK, now the thing with this and you'll see that the standard deviation also changes because you see

when prices were very volatile, well, the volatility, the standard deviation got bigger.

And three standard deviation was this big.

Right.

This was three standard deviations.

This big, because these scandals are big, so the volatility is high.

Right, but now if you look here, look at the standard deviation, how small it is.

Here.

Now, it's only this big three standardizations, why?

Because the candles are so small now that, you know, three standard deviations is not that far away,

so you'll see that the bands themselves, they widen as the the security that you're treating, the

cryptocurrency that you're creating is more volatile and it will narrow as it's less volatile.

So for different crypto currencies, if one is more volatile, the bands will be wider.

If one is less volatile, they'll be tighter.

And if that same cryptocurrency becomes more wider, it'll widen up and narrow.

Now, this is the percentage that I give.

You aren't actually going to work in cryptocurrency or in any financial market.

Why?

Because in the financial markets, we don't actually have a regular distribution like this.

We don't have a regular bilker.

What we do have is something that's called a fat tailed distribution.

OK, and I'll explain this here.

So a regular bell curve is going to look like this.

This irregular bell curve is going to look like this.

OK.

Just like we've seen now, a fat tailed distribution is going to look more like.

I'm sorry for my drawing here, it's not the best drawing.

Actually, this is awful.

It'll look like this.

This is a little better.

I think Google would be a better frontier.

Now, what is the difference between these two?

The difference is the things that are way more common to happen.

Are going to happen even more more often, and the things that are very unlikely to happen like this

very, you know, outlier or, you know, Six Sigma events or things that are very not common in the

financial market, they're actually way, way more common than you would think.

So even things that you would think would never happen, like prices soaring up a lot or crashing a

lot.

They do happen in the financial markets.

So we have a failed curve.

So you have to you know, you have to know this because things that you think are unlikely, basically

you get a price higher and you're like, well, in the next hour, because I'm looking at another chart,

the likelihood that we get here is less than one percent.

Well, not.

It's not because the fact because of the fact of distribution, they are likely so this can really go

down or really go up outside the bounds.

So you have to be careful where you use an indicator like this.

OK, this indicator, you're going to want to use it when you think that something is mean, reverting.

Right, because you have things that are in momentum and things that are minority.

These are two types of strategies.

You know, momentum is when something is going in a direction and you think it's going to keep going

in that same direction.

You're running a momentum strategy.

You don't want to use this indicator when running a momentum strategy, because this is an indicator

that tells you when something is away from it's mean.

If you don't think that something is coming back to its mean anyways, why use an indicator for that?

If something is really in momentum, there's an event.

Don't use this indicator.

You're going to want to use this when you're looking at something that when it deviates from its mean,

it has high likelihood to come down something something that isn't in momentum.

OK, there's not an upcoming event or news or hype behind it.

So this is when you would want to use that.

And to be honest, one of the reasons why this is a very popular indicator and I hate to use it, is

because the market is usually in mean reversion.

So 80 percent of the time things are just going up and then back down and up and down, up and down.

And nothing is really happening.

It's only 20 percent of the time that something is really skyrocketing in a direction.

OK, so as a trader or an active trader, interrupting strategies are very good because they make you

a bit of money all the time instead of just banking on 20 percent times where something is really going.

So you're making money off of all these little movement that the market makes.

Now, that being said, we aren't going to use the Bohlinger brands in the simple way.

And the strategies that I'm going to show you in this course, we are going to use Bohlinger bands in

a more advanced way by combining different cryptocurrency.

You'll see you love the way that we use it and you really understand why an indicator like this is extremely

helpful won't be using it just by putting it on one cryptocurrency like that and just buying and selling.

It'll be a more advanced way of using it.

And you're only going to like it, I think.

So you're going to be excited when you look at the strategies.

That being said, this is Bohlinger bands and I'll see you guys in the next lecture.