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A Simple Way to Use Excel to Set Up a Monte Carlo Test - Duration: 11:01.

Hello this is Mark from Tradinfromed.com and welcome to this video on: a

Simple Way to Use Excel to Set up a Monte Carlo Test. In this video I'll be

using the example of a trading strategy to show you how we can set up a Monte

Carlo Simulation using Excel formulas and a VBA macro. Please remember if you

do like this video to hit the like button and also subscribe to this

channel for more videos like this. Now before we can do a Monte Carlo

simulation we need to have some background data and what I'm going to

use is the trades which is outputted from a back test that I've done of a

trading strategy and if I scroll across here you can see that I'm actually using

a back test spreadsheet this is a Tradinformed Backtest Model that can be

used to test all sorts of different trading strategies, different indicators

and different markets and timeframes and if you want more information about

Tradinformed Backtest Models there is a link on the screen at the moment OK so

as I was saying I'm using this column, this is our list of trades, and I'm going

to reorder them. The purpose for this is to work out the worst case scenario and

I'm going to be looking at the maximum drawdown as a proxy for our worst case

scenario now to begin with what we're going to do is I am going to order

the original trades using a number now I've just copied this number down to all

the rows below and then I am going to introduce an

Excel function generates a random number and again copy this down now Monte Carlo

methods are built on randomization and so this is how we're going to use

randomization in this example and the next thing that I want to do is to

translate this random number into a version of these numbers here and I'm

going to do that using the rank formula and what this is doing is basically

ranking each of these numbers, random numbers in the order that they appear in

the whole sequence. I'm going to use the dollar symbols to make this a fixed

range okay I'm going to double click this down and now what we have here is a

randomized trade order which is exactly the same as this number, these numbers

here but in a different order so it's nice to have these numbers in a

different order but what we want to do is to have the trades the trades over

here from column CA and the way I'm going to do that is to use my favorite

compound function in Excel which is Index Match whenever we're using index

match we always at the very start we put the range that we want to output in this

case this is the range of trades

then I'm gonna put a comma in here and I'm gonna put the match part of the

function and what I want to match is this new random trade order that we've

generated with the original trade order

once again I'm gonna use the $ symbol to make this a fixed reference

and finally I want to make it an exact match again I'm going to copy this down

and now what I have, is these trades moved over here except in a random order so

we're going to turn this into a capital our ongoing capital by using the sum

function which is the current value plus all the previous values starting with

a notional $100,000 now we can see that we have now populated this chart

here and also every time I make a keystroke I will change the order of the

trades and you may have seen this number over here changing so what we've got

here is this particular scenario the maximum drawdown for that particular

scenario now we can compare and contrast it with the maximum drawdown of my

original strategy I should say this is the drawdown function I am using which

is calculating 1 minus the current value divided by the maximum of all the

previous values so what we have here already as I said this is a simple way

what we have here already is a simple manual Monte Carlo simulator and I could

sit here and I could make 100 keystrokes and

I will have 100 different maximum drawdowns appear in this cell so we

could stop here but I want to show you how we can automate this process and we

do that using Visual Basic or VBA for Excel and I've already set up the code

for the VBA and a couple of simple inputs here firstly I'm going to test

tell it to do 10 iterations so go through 10 different random scenarios

and tell me what the worst drawdown of each of those is and I've got a button

here which I can click which will test my macro and we can

see that our strategy drawdown 16.9% what we would generally expect here is

that our Monte Carlo simulation has come up with a drawdown which is slightly

worse I can click that again and we find one that is actually slightly better I

can click that again and again what what I would generally expect to be the case

it's slightly worse now I can set this to 20 or 50 or a thousand or as many

iterations as you like so I put in some code for this macro here and I can show

you it's here now this is a simple way and I've got some pretty simple code

here now you could simply copy it from the screen if you like but I'll go over

how I've done it so I'm using here a simple counting system and first of

these of interest is here which is the number of iterations which must

correspond to this cell here back again and the next thing that I want to do is

always clear the value of this cell before we do anything

let's get rid of our old values and start afresh so again this range here

must always correspond with this cell here we use a loop which we use in all

sorts of programming every sort of programming really and we're going to do

until I start is equal to our number of iterations so until we've done ten

iterations what we're simply going to do is copy over each scenario at a time and

we're going to work out what I'm calling here the new DD which stands for the new

draw down and we're gonna then use a if statement an if statement to check

whether this particular scenario has a worse drawdown than all the other

scenarios that we have tested and if it does then it gets entered in this cell

here which you can see here is range CI 196 so if we happen to move this

particular cell all we need to do is match up this cell with the new value

it's going to loop and then the end of the macro so this is a nice simple macro

please remember that you can also get this spreadsheet with the Monte Carlo

simulator in it it is available in the Tradinformed shop and there is a link on

the screen if you want to check that out

so I hope you have enjoyed this video on Monte Carlo Simulations please remember

to subscribe to this channel for more videos like this also hit the like

button and comment below if you would like to know more about trading the

financial markets in particular back testing using Excel please go to

www.Tradinformed.com

For more infomation >> A Simple Way to Use Excel to Set Up a Monte Carlo Test - Duration: 11:01.

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Machine Learning Tutorial: Simple Example of Linear Regression & Neural Networks Basics - Duration: 22:18.

I'm talking about how machine learning works giving you a simple visual example

that you can follow explaining different trade-offs and things that are effect

our prediction results a short introduction to deep learning and

convolutional neural networks stay tuned

Not three separate devices this is one device

Hey everybody I'm Sam and this is Entiversal, you're watching part two of my series

understanding machine learning and today we're gonna look at a simple example of

linear regression which is basically the simplest machine learning algorithm of

how it works what are the different trade-offs that can affect if your

systems actually learning or not or if it actually gives you good predictions a

little reminder of what we said in the last video is about the difference

between convention software and artificial intelligence systems which

are using machine learning well in conventional software everything is

pre-programmed right so all the states of your software or program need to be

pre-programmed so depending on the input data or some kind of internal results in

computation it has to know where it needs to go

and obviously that becomes a problem when we have systems where there are too

many arguments features or just simple set inputs for example in classification

of images or even in a simple search right because there are different a lot

of different words in a lot of different languages in a lot of different

combinations and then in every word there could be some kind of mistakes and

so on and so on so the combinations are just incredibly incredibly big number

that's why we're using machine learning and they are very good in exactly

classification for example images sound you know facial recognition in your

cameras and you know how did they do it well the most advanced machine learning

algorithms now are called neural networks because they're trying to mimic

the way our brain in neuro system works we can look at every neuron

it's just some kind of filter that filters the signal coming in removing

all the data that it believes it is not meaningful and then

enhancing the data patterns that actually believe give us information

about what we see what we hear what we touch and so on so it is the same thing

with our neural networks and today I'm gonna show you an example which

basically represents only a single new one and as we said neurons are just

filters in our example our new one will be comprised of just two weights in

every neuron there are weights and the way it's tell us based on the results

that we have shown our algorithm previously how important every feature

that we input in our rhythm is for actually classifying what the prediction

output should be what we do is basically we have a bunch of input numbers our x

value and we multiply it by weight and it gives us a result for example which

two and then we have you know a training result of four then our weight will be

true because 2 x 2 is 4 and then we present it a new data that it hasn't

seen so it's 3 the weight is truth or output will be 6 and obviously that's

you know done the most simple simple example that can show you but you know I

hope you get the idea so let's get into our little script here and I've shown it

into our last video basically here it's in Python it's using a package called

numpy and I'll probably do another video explaining more about that you know how

you can build your own machine learning algorithms artificial intelligence

systems and a lot of lot of cool stuff but let's stick to the basics now so

what we have here is our function right this is how our input maps to our output

our input is our X because I'll always keep the end to be stable or the same

value so it's just a constant my variable will be X and then obviously

I have a output right which is what I returned which I'll call the labor or

our Y our result so here I construct my input variable values which is my X and

here I have 200 points between minus 1 and 0 that is my training set so I'll

train my machine learning algorithm on violence between minus 1 and 0 here is

my prediction input data set so those are the real values which I will give to

my machine learning algorithm and I expect it to give me proper results here

I construct my labels so again that's for training as you see that's just the

function and added some noise on my data value so I put my X's and I get my y's

here I've done the same thing for my prediction input interval and I do that

just because we can see if our predictions are actually the same as the

real values we should be getting if that's true then our algorithm in

warning is working if it's not then it's not working and we need to probably do

something here I construct my design matrix which is comprised of the

features of the input so it's very important to make a difference between

the input value of your variable in your features the input value is just values

between minus 1 and 0 so for example it could be 0 right that's my X but my

feature matrix is features that tell us information about my input and for

example here design matrix my features are just 1 right it's just a 1 and then

DX itself so for example for my input of 0 it will be just 1 0 so it'd just be a

vector of two values 1 0 here I construct my design matrix for my real

data or interval that I want predictions on and

again it's just comprised of once and the value itself so here it will be

between minus 1 and 1 and here is just 1 1 1 1 1 always here

is my training so what is the training I multiply a dot product of my two

matrices this is my design matrix which I constructed here and these are my

labels on my actual training results so as you see my weights are my filter by

inputs on my feature my design matrix and then I have some kind of output so

if we want to map it to that one for example this is just this new where I

have my input features in and then some kind of output going out and also that

is called supervised training because I actually give it labels egg should give

it the real values which I expected to get and then I expected to some kind of

recognize what the function itself is at the ends does the idea obviously it it

won't be perfect but I wanted to somehow estimate it and this estimation is

actually held into the weights themselves here I make my prediction how

do I make it I just multiply my weights by my design matrix and here as I said

that this my design matrix and I took the more Penrose inverse and you can

read a bit about it basically what it does it guarantees us that it gives us

the ultimate weights that are that gives us the smallest errors from our input

data and those errors actually called residuals in machine learning so we want

the smallest residuals and here I have just spotted my results so with red you

can see my training data those are the actual labels

or the two visas that I get with some noise with yellow are the true results

for my real data prediction interval which I expect to get and with one you

can see my prediction of the prediction of my machine learning algorithm as you

can see here it's very good in here it's actually generalized it catches the

trend and the prediction itself is pretty good

so what could go wrong here I have almost the same thing the only thing I

have changed is my end so here in the previous example was one always and now

it's true and what it does in our function is it makes it have a curve

here we have again our intervals of inputs and prediction we have our design

matrix we have our weights and again those are labels this is those are

actual true values that we expect in this our prediction and as you can see

things are not looking well at all so our prediction cannot catch the trend so

why does that happen in machine learning there is always a trade-off between

variance and bias what variance means is how well our algorithm Maps through our

training data and that's obviously very important because in order for the

algorithm to catch the trend and obviously the trend here is that there

is a curve right and as you can see if our algorithm doesn't catch the trend of

the coin so it doesn't catch the pattern what that means is it doesn't have

enough information and as we can remember our feature matrix is comprised

only of once and the X itself but what that results in is a straight wine so in

order to improve variance or in in order to make our rhythm understand

better our input data we need more feature information so we need to

actually understand and know more about our input and what I change here is when

I construct my design matrix I have the ones here again I have my eggs but also

I have added a third feature which is X on the power of two so that just x

squared and what that is it introduces it gives more information to our

algorithm and as you can see it is able to catch the trend and then over on our

unseen data here is our unseen data between zero at one it has almost

perfect prediction so here it is very important to understand that features

are very important obviously in a real life situation you know in something

like these here we have five features but if we have images we have a thousand

features or you know ten thousand features because we actually need to

have enough information for input so algorithm can actually extract the

patterns from it I hope this gives you an insight about what's happening and

here I have almost the same thing so what I have changed here is I have

trained it from on values between minus one is zero for input here I have

trained it on values from minus 0.5 0.5 where it can actually catch the actual

curve on its bottom I could say that and as you can see here is fitted even

better on the bottom but then at the end it's not fitted as well so obviously

another thing that's very important is is your interval of data actually

covering the most important points of you know of inflection that gives us

the biggest information because if I have trained it only on values from here

to here that's almost a straight wine so it carries no information at all about

the curve so that's why we try and you hear that you know artificial

intelligence needs a lot of input data you know millions of photos and so on

and that's why data is so important because the more data we have the better

we can fit our model to represent it the more information we can extract and then

obviously our predictions will be better so one thing is we need to have more

features but another thing is we have to have the most meaningful data possible

and then I want to talk to you a bit about the trade-off right so I said

there is a variance which is how well the algorithm fits

two-seam data but there is bias which represents how well the algorithm

generalizes so how well does it fit to UNC in data and obviously that's very

important because you know theoretically if we could have all possible

combinations of data we could fit it perfectly right but as we know that's

impossible so it is we want to fit it well to the

same data so we can actually catch the different patterns but then we want to

make it general so on unseen data it again predicts well because at the end

at the point whatever the data is we have a good prediction what happens here

is we have the same design matrix so here we have three features which are

once x and then x squared but what happens here is on our training data we

have a trend a very smooth curve and then the curve kind of doesn't doesn't

happen exactly as our algorithm picks it up because obviously there is noise our

greetin model Yoshio what of the word model in machine

learning model means our weights weighting of the features that we

believe pick up the patterns in data so our model fits quite well on scene

data but then it doesn't generalize why do you think is that well that is

because we have three features so what we have done is we have fitted it to

perfectly to sin theta but then in a point where you know the

date doesn't fall exactly the same trend as the scene data we can make a good

prediction so what we can do about that is instead of fitting it - well - we

introduce bias and obviously there are different ways about that but in our

very very simple structure what I do is I just remove the third feature so I'm

back to just once and access and as you can see what happens is it doesn't fit

as well to the scene data right but it fits much better

- unseen data or to general data and you know if you think I cheat somehow you

can see that those are the same values from 0 to 2 and you know from almost

zero to around 1 and here you can see it from 0 to 2 from almost 0 up to 1 so

bias you know and there is a lot of math behind it we probably will go much more

in depth in next episodes of the series by the idea is the bias makes it harder

for model to fit through well - seen data but that hopefully makes it much

more general so we can actually predict much better - unseen data I would like

to finish off with just a small introduction to deep learning you know

deep mind you know the cool stuff that you know most of the headlines are

because what I'm talking about very simple example

which hopefully will give you understanding of how machine learning

works but for deep linking deep mind and we'll go much more in that later there

is a lot of cool stuff so as I said what I just explained now happens just one

neuron and as you can see here we have a lot of different moves and those are

called layers and here when a layer is between

input and output it's called a hidden layer the idea behind that is obviously

the more layers you have and remember that so every neuron has different

weights but every layer has the same features but another layer has different

features what that gives us it can pick up different patterns from the same data

from the different neurons it can give us even more insight on patterns that we

couldn't pick up from the features we had before which means that we can fit

it better for different patterns which gives us obviously with water flairs

much more possibility of fitting it perfectly to seen and unseen data that

makes the trade-off between variance and bias I guess

easier to do but obviously we have a lot of more stuff that we can go through and

if you think about is the same thing with our own brains because in our

brains we have billions of neurons and from you know from our eyes the data to

go to our brains it goes through possibly millions or

billions of neurons which each apply some kind of filters and obviously

that's why you know our brains are so good at what they are doing

even though that for the most part we don't even realize it and here you can

see a quick example which I won't get into depth of convolutional neural

network so convolutional neural networks are a type of deep learning or is called

deep neural networks and why they are deep because they have a lot of layers

right so a shower is just you know one layer or

two layers deep is when you have a lot of layers which is kind of logical

what are they good in whatever good thing as I said image classification and

a lot of different things that pick up patterns because with them we can

actually use unsupervised warning so how it works is here you have a first pass

which is called feature learning so through those layers imagine that all of

those are layers with neurons here you have much more possibilities for

different convolutional and you layers to actually pick up features in our

example we actually make the features right we made a matrix of ones XS and x

squared here you let the machine learning algorithm itself pick up the

features that it thinks better represent the data we don't only train our weights

we also train our features and when we train our features we can actually get

the trade-off of variance and bias much better balanced and obviously get much

much more insight into the data and I'll be explaining more about that in my next

video I hope you enjoyed this video I hope it

was it was good for you you found it interesting and ask me any questions you

would like to know stay tuned to Entiversal

For more infomation >> Machine Learning Tutorial: Simple Example of Linear Regression & Neural Networks Basics - Duration: 22:18.

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Lose Weight naturally and fast with simple habits and Lower Cholesterol - Duration: 3:20.

subscribe to our Channel and press the bell icon for the latest notifications

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For more infomation >> Lose Weight naturally and fast with simple habits and Lower Cholesterol - Duration: 3:20.

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Kevin Can Wait - Kevin is a Simple Girl - Duration: 1:13.

For more infomation >> Kevin Can Wait - Kevin is a Simple Girl - Duration: 1:13.

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1 Simple Way to Improve your Listening Skills in English - Duration: 8:52.

hello and welcome back to english with real teachers my name is Harry and this

is my partner charlie and we are here to give you an unforgettable English lesson

that will not only teach you some really useful daily English expressions that

you can use in your conversations but also help you to improve your

pronunciation in British English your speaking skills and your listening

comprehension you are going to love these guys so don't go anywhere

before we start the video we wanted to tell you about a language learning

platform that Harry and I have been using heavily for the last two years

this website is called Italki and we love it not only because it's convenient

and it's user friendly but it's also super affordable but furthermore it's

connecting you with an actual native teacher so we highly recommend you check

out italki just how Charles has done as a professional football player who's just

joined Arsenal and we're gonna see how he likes italki thank you for giving

us 20 seconds of your time Charles can you quickly tell the fans why you

decided to come to italki and learn online instead of staying in the

traditional classroom yes it is quite simple really I I could not get to the

class because I am playing football all day this is a very intensive training so

online is very convenient for for me to practice my English mmm

but okay okay all right bye Thank You Charles back to the studio

yes so we've partnered up with italki and because of that we've managed to get

you ten dollars worth of italki credits once you sign up for your

lesson so enjoy this video learn all the vocabulary that we're about to give you

and then hit the link in the description box below to get your class and practice

with a native teacher couldn't be better so we hope you enjoy that enjoy the

video one of the most difficult things about learning English is understanding

British people when you talk to them you may have spent 10 years of your life

learning the language but when you come to England you might still walk away

from a conversation thinking what the hell was that person trying to say to me

and this might be due to a number of different factors maybe it's due to the

huge variety of regional accents in the UK or maybe we just mumble a bit

(mumbling noises) well whatever the reason is don't fear because me and Charlie we have the

solution did you know that if you imitate the way that someone says

something you are much more likely to be able to understand what they say in the

future well that is exactly what psychologists found at the University of

Manchester in 2010 their study consisted of a number of different groups of

people who all heard the same 100 sentences read out in an unfamiliar

accent one group repeated them aloud in their own accent bullshit I did not hit

her .. another wrote them down ..and the final group mimicked them in the

unfamiliar accent each group was then read out a different list of sentences

with the same accent but this time their listening skills were tested what they

found was really interesting the group who had mimicked the unfamiliar accent

performed much better than the other groups who were just writing down the

words or repeating them aloud in their own accent so mimicking the sound of a

person's accent is indeed a great way to improve your comprehension skills so we

should keep that in mind now we are ready to get going with the video myself

and Charlie are going to read out 25 incredibly common slang expressions that

we use here in the UK and we would like you to mimic us not just in what we say

but in the way that we say it so are you ready? Find a nice quiet place sit

yourself down relax and repeat what we say in the way that we say it. You all right?

how's it going you all right? yeah all good how're you doing

all good how you doing bloody hell are you all right?

bloody hell are you all right blimey it's busy hear isn't it I'm just

just gonna sit down here budge up, just budge up a little bit..f****ing budge up

It was so nice to see you take it easy It was really lovely seeing you take it

easy.. yeah good to see you too alright yeah take care

cheerio cheerio Oh mate I'm really in the doghouse a

girlfriend was reading my texts the other day I'm really the dog house.. she's doing what

now she's reading your text as well? That takes the biscuit god that really takes

the biscuit yeah yeah that takes the biscuit

Just cocked up massively I just cocked up so badly just now Charlie what are you up to tonight

What're you up to later? I've got to go to a work to do tonight yeah it's just a bit

of work do I saw. Do you fancy a pint? I do.. I fancy a pint. Do you fancy a pint?

come on let's go have a pint.. just one just one pint

You are a pain in the arse

you are a pain in the arse you're a knobhead that's what you are you're a knobhead

you tosser you're a tosser Charlie you're a real tosser Chaz. What do you

think of his new missus? What you what do you think of his missus .. Do you think she's fit..

yeah not bad

pretty fit.. I'd give her one. Apparently she's a bit of slag but I really

like her apparently she's a bit of slag but I really like her.. I'm gobsmacked

I'm just gobsmacked absolutely gobsmacked at that..do you fancy some grub I could do with

some grub no man I'm stuffed no I'm I'm I'm I'm sorry man I'm stuffed..mate

you you better get out of it.. they've just seen what you've done. Leg it! You've gotta f***ing leg it!

Go come on! Leg it! Get stuffed! Why are you lying? Get stuffed!

Why are you lying? Harry, what are you on about? What are you on about?

want about

For more infomation >> 1 Simple Way to Improve your Listening Skills in English - Duration: 8:52.

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I'm Trying To Destroy Myself And Simple Programmer! - Duration: 12:36.

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Hey, what's up?

John Sonmez here from simpleprogrammer.com.

I was trying to figure out what kind of video to make today.

I've got a bunch of questions on my Trello board.

I had a bunch of video ideas.

I just didn't feel like doing any of those videos.

I think part of the reason why is just because I'm not being 100% honest with where I'm at

right now.

When I'm not doing that, I'm not being genuine.

It's hard for me to genuine in anything that I'm doing, so this is sort of one of those

honesty videos where I'm going to kind of talk about some hard shit.

I want to talk about a topic that I think would probably benefit a lot of you because

we all tend to have this tendency, which is self-destruction.

You might think someone like me doesn't have a self-destructive tendency, but I guarantee

you that we all do at times.

We all try to destroy ourselves and destroy what we created and I don't know.

I don't know why we do this, but I recently got back from travel and started to get back

into my routine and everything, and I was having a really hard time doing it.

I'm still having a really hard time doing it.

I'm just not feeling very motivated and I'm noticing that—I kind of notice myself doing

things that were destructive, like trying to destroy the relationships that I have,

trying to destroy my fitness and my health that I've worked so hard to build.

Even to some degree, today and for last week, it was sort of like, "Ah, I don't really

feel like doing YouTube videos anymore and maybe I shouldn't do YouTube videos anymore.

I don't have anything good to say," right?

I was just feeling that way.

I was looking at some of my old videos.

I'm like, "Oh, man.

I used to give it.

I used to give the fire, but now it's like, "What am I doing?"

Sometimes I'm just sitting there stuttering in front of the camera and just trying to

make up some shit.

That's how I honestly felt.

I've talked about this before, but this is one of the struggles of being creator.

Even in the business, I was thinking.

"Oh, do I really want to do Simple Programmer?

Do I want to walk away from this?"

You know, I go through these phases at times, but I found myself like really trying to destroy

myself and I wonder why, like why would I do that, what would possess me to do that,

and I don't have a really good answer.

I don't have a really—to be honest with you, it's just something that I think we face

in life.

I'll tell you though.

For the most part, I have survived these.

It's not like it doesn't get hard sometimes.

I mean I got to go, especially—you know, I make it hard on myself.

I mean my life is definitely hard by my own creation.

I've got—I run 40 miles a week and lift and eat one meal a day, and I work pretty

damn hard on my stuff and do all these YouTube videos.

There's definitely a lot that's going on.

I have high expectations of myself.

There's definitely that.

There's definitely a lot pressure, but I definitely don't want to sabotage myself and destroy

myself and destroy the things that I've built.

Like I said, I have survived these before.

I thought it would be interesting to talk about how I'm surviving it now, how I've survived

it before and how you can survive it before because—or how you can survive in the future,

because I know that a lot of you probably haven't survived it, like you have gotten

to that point—I mean have you ever gotten to a point in life where things were going

good and you were really successful or more successful than you've been and then you,

for some reason, sabotage yourself, self-destructive behavior.

You do something.

Your diet is going well and you just go and eat like a pig.

Relationships going well, you sabotage that relationship.

Whatever it is, somehow you destroy.

You're going to get really good grades in school.

You decide to somehow destroy that as well or drop out of school.

Whatever it is or starting a business, or starting a project or whatever it is that

you decide that you destroy it.

Why and how do you stop that?

For me, the biggest thing is to recognize that I'm doing some self-destructive behavior.

Now, that alone doesn't change my attitude towards it.

I'm still like "Fuck it, I still want to destroy this," but at least when I'm recognizing it

then I can realize that I'm not acting in my rational mind, how I normally would think

and act.

That's the key because once I know that—because now—see, I can feel one way, but I can at

a metacognitive level think about it from the abstract and think in a different way.

Right now, I don't feel like doing this video.

I'll be totally honest with you.

I don't feel like being here.

I don't want to go for my fucking run.

I just want to eat some cake.

I don't want to like do a diet thing and do my kickboxing.

I don't care if I get fat.

All of these things, they're running through my head.

I don't care.

I just want to like play some video games and relax, and whatever and take a nap.

That's how I feel right now.

I feel like I'm stupid blabbering in front of the camera all of these things, but my

mind knows that this is not real, that this is kind of some bullshit that you're coming

up with, John.

For whatever reason it is, you're doing it because I can look at my past self and the

things that I've done in life and the achievements I've made and I can see that that's not the

normal pattern.

That's the normal John.

John is in self-destructive mode right.

John needs to kind of be baby-sat by the mind.

You see what I'm saying?

Like the emotional John needs to take a back seat.

"Okay, it's cool.

I understand that you feel this way.

It's cool.

But guess what?

You're going to fucking run, anyway.

Okay, yeah.

That's fucking and you've just got to do it.

You're going to fucking go to the gym.

You're going to eat your diet plan and whether you like it or not.

You need to do the work you need to do.

You might not have the enthusiasm that you'd like to have or that I 'd like you to have,

but that's fine.

You're still going to do it.

When that enthusiasm comes back, that will be great."

See, the thing is, you know, I've talked about this in various forms.

I did this video on holding the line.

You could equate this to the same thing.

I mean you could say that this is holding the line.

It essentially is.

What you're trying to do is you're just trying to—it's like—think of it this way.

It's like Bruce Banner turns into Hulk.

The thing is like the goal is when—when Hulk is out, don't let him destroy too much

shit in the laboratory.

You know what I mean?

Like how can we like keep him from—he's uncontrollable, like don't try and change

the Hulk, but let's try and like—like can we chain him up?

Can we put him in a box?

Can we like pad the room?

Can we prevent him from doing damage when he is the Hulk because we don't know when

he's going to transform back?

We can't necessarily control that transformation.

The same thing happens with us.

I can't necessarily control this to some degree.

Some days you're going to feel depressed.

You're going to feel like shit.

You're going to feel self-destructive and sometimes it's just going to go on for some

period of time.

Yeah, you can take some medication and say that you don't need to be on pills.

That's not a really good solution either.

You don't know when this is going to happen.

What you can do is you contain yourself when you're in that state.

I'm not going to be in this state forever.

I know it.

I know that someday when I wake up out of this coma, when I'm not the Hulk anymore and

I'm Bruce Banner again, that I'm going to have to deal with the consequences that Hulk

created.

This is like Dr. Jekyll and Mr. Hyde.

Same type of thing.

I think that story is actually a story about what I'm talking about.

I think Dr. Jekyll and Mr. Hyde is sort of about what exists in all of us.

The thing is you have to remember and I have to remind myself that, "Hey, I'm going to

wake up.

I'm going to be Bruce Banner.

When I am and I'm no longer the Hulk, I am going to either—I'm going to have one of

two reactions.

One, I'm going to be relieved that the Hulk didn't smash everything, or, two, I'm going

to be like, 'Fuck.

Now, I got to clean up all these pieces.

Man, why did I do this again?

Oh, gosh?

Why couldn't I just like—ugh.

Why couldn't I just like leave the shit intact?

Why didn't I have to go and destroy this shit?

Now, I got to start over.

Man, I'm never getting anywhere in my life because I'm always starting over and always

cleaning up after the Hulk."

You see what I'm saying?

I think I'll—if you're honest with yourself, a lot of you, maybe that's you.

Maybe that's you, is you're always—everytime you transform into the Hulk or Mr. Hyde, you

destroy all the shit you created as Bruce Banner.

You got to realize this and you got to stop doing this.

You got to start saying like, "The only way to do to stop this from happening is to do

damage control when you're the Hulk."

You can't prevent yourself from being the Hulk.

That's a ridiculous strategy because you know that that transformation is going to happen

at some period of time.

You're going to have ups and downs in life.

What you can absolutely do is you can develop the discipline.

This is where the rubber meets the road, guys.

This is really the fucking bulldog mindset.

Let's queue up the bulldog video, but this is where it really counts.

Again, it's not that you're on your fucking A game all the time.

Some days I'm on top of the world.

I am on my A game.

Now, usually, I can tell you one other thing here.

I'll side note this, which is the times when I am on my A game is when I have created a

momentum, a track record of success behind me.

Success begets success.

If you think about it, the way to get into that good state, that enthusiastic state,

that is going to be the kick-ass state, is when you're in the shit state like fucking

do what you're supposed to do anyway to make progress to the point where you can stand

on top of the mountain.

You can be like, "Yeah."

Right?

That's what's most like that they kick you out of that state and kick you into the good

state.

Ultimately, it comes down to having that discipline, having that mindset to say, "Look, no matter

what, I am not going to destroy everything I created just because I feel like shit right

now for the day, because I feel like shit for the week.

Whatever is causing me to create this self-destructive behavior, pattern or thought pattern, I'm

going to try and snap out of it.

That's great, but I'm going to make sure that the damage is controlled until I do.

If I don't, if I don't snap out of it, then okay.

Fine.

I'm at least holding this off for a week.

I'm at least holding for a week."

That's the motto.

That's the mantra that I tell myself.

I don't always perfectly do it.

I usually don't make a huge amount of forward progress when I'm in one of these self-destructive

states.

If I can just prevent myself from punching too many holes on the wall, that's a plus.

That's less walls that I have to plaster when I wake up from the coma.

You guys know what I'm saying?

Does that make sense?

Okay.

Again, I'm putting this out here for you guys because one is honest.

I kind of hate to make these videos because I'm like, "Whoa.

Is John going to make a video saying that he's having crisis every single couple of

weeks or months, or something?"

I guess so because that's how life is.

You go up and down and you have these dips and stuff.

I'm making these videos because I want you guys to realize that this is the condition.

This is a human condition.

This is not something medically wrong with you.

This is not something that is bad about you because I go through this shit.

You see it.

You see it on these videos and then you see it in—I just have to trust.

I have to trust the process.

I have to trust that I'm going to come out of this and I have to trust myself and the

discipline that I've built that it's worth it.

It's worth putting forth that effort when you don't feel like it to prevent yourself

from punching those holes on the wall.

All right.

That's all I got for you today.

I've sort of beat this horse to death, but I hope you found this useful.

Make sure you click that Subscribe button if you haven't already and click the bell

to make sure you don't miss any videos.

I'll talk to you next time.

Take care.

For more infomation >> I'm Trying To Destroy Myself And Simple Programmer! - Duration: 12:36.

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SIMPLE CALLIGRAPHY ALPHABET WITH AUTOMATIC PENS - Duration: 10:45.

Letter a

Letter b

Letter c

Letter d

Letter e

Letter f

Letter g

Letter h

Letters i & j

Letter k

Letter m

Letter n

Letters o & p

Letter q

Letter r

Letter s

Letter t

Letter u

Letter v

Letter w

Letter x

Letter y

Letter z

Letter z with flat brush

Quick lowercase Italics alphabet

Or Uncials??

#KEEPWRITING #CALLIGRAPHYMASTERS

For more infomation >> SIMPLE CALLIGRAPHY ALPHABET WITH AUTOMATIC PENS - Duration: 10:45.

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Melania's Best Friend Reveals Stunning SIMPLE Reason Trump And Melania Are Together Today - Duration: 10:00.

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Comment programmer votre télécommande CAME TOP 432 EE sur un récepteur SIMPLE UNICO? - Duration: 2:37.

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How to make designer skirt from old simple skirt ,makeover of old skirt ll new look of old skirt ll - Duration: 6:13.

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Simple Chocolate Cake Decorating Ideas | Best Chocolate Cake Decorating Ideas | Chocolate Cakes 2018 - Duration: 11:37.

Simple Chocolate Cake Decorating Ideas | Best Chocolate Cake Decorating Ideas | Chocolate Cakes 2018

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7 Simple Exercises for Sexy Legs - Canada 365 - Duration: 6:29.

7 Simple Exercises for Sexy Legs

Today, were going to recommend a series of 7 very simple exercises for sexy legs so that you can shape your figure and your legs quickly.

You can do them right at home or anywhere, and you only need a few minutes a day.

Youll notice a difference!.

Lunge jumps with leg change.

One of the first exercises that you can do to get sexy legs is a lunge jump with a leg change.

To do them, you should do a lunge so that one leg is behind the rest of your body.

In this position, you should keep your back completely straight while the opposite knee should be at a lower level.

When you have achieved this position, you should jump as high as possible with the help of your arms.

While you are in the air, change legs and land in a lunge with the other leg behind.  Do sets of 10 or 12 of this exercise.

Tuck jumps.

To do this exercise, you should slightly bend your knees and put your arms behind to help with your jump.

Once you have adopted this position, you should jump as high as possible, trying to lift your bent knees as high as possible.

This is an exercise that will require a good amount of effort so that you can do 3 sets of 10 jumps.

Leave a rest time of approximately one minute between each of the sets.

Laying down legs lifts for your outer hip.

One of the most basic and simple exercises to have sexier legs and a shapely figure is to do leg lifts while laying on your side.

To do this, you should lay down on your right side and support yourself with your arm, putting your opposite arm on the floor.

Then, you should lift and lower your leg slowly, without making any sudden movements that could cause pain or discomfort.

This exercise should be done a few times with both legs.

It also helps to shape your glutes. .

Laying down leg lifts for you inner hips.

This is a modified version of the previous exercise and consists of laying on your right side and supporting yourself with your arm, just like you did for the previous exercise.

In this exercise, you should bend your left leg and bring it to the opposite side to support your foot on the floor, holding it with your hand to stabilize your movements.

Raise and lower the other leg slowly and repeat a few sets of this exercise with both legs.

Lunges with weights.

If in addition to getting sexier legs you want to strengthen your arms, this exercise is ideal for you.

To do this, keep your back completely straight and your arms parallel with your body.

Then, you should lunge backwards, switching legs without arching or bending your back.  To get the best results from this exercise, lunge deeper, bending your legs at a straight angle.

Diagonal lunges with weight.

Another exercise that will help you get sexier legs and strengthen your body is diagonal inclinations with weight.

To do this, put your left hand on your hip while your right hand holds a weight or bottle of water.

Then, take a step to the left while also leaning forward with your back straight, slowly making your back parallel with your right leg.

In this position, touch your left ankles with your right hand.

While doing this, your right leg should be straight and the left should be bent.

Repeat this exercises a few times with both legs.

Legs lifts in a plank position.

The last exercise that we suggest to have sexy legs are leg lifts in a plank position.

Is a very simple exercise that is done by supporting yourself with your arms straight against the floor, while supporting your lower half with your right knee bent.

In this position, you should stretch your left leg, raising it as high as you can, and then lower it slowly, with gentle movements.

Repeat this exercise with both legs, many times.

For more infomation >> 7 Simple Exercises for Sexy Legs - Canada 365 - Duration: 6:29.

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10 Little Dinosaurs + More | Kids Songs | Simple Kids Songs TV - Duration: 2:05.

10 Little Dinosaurs

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Comment réduire son ventre avec un exercice simple et rapide - Duration: 7:16.

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Alliant Credit Union: Simple, reliable & smart banking - Duration: 0:54.

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