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Hi, everyone, and welcome in this new video and this video, we will begin to import and reprocess
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some data.
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First, we will pre-processed the data, which come from medicine to the front because it's the most
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important.
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So here we just have installed why plans to import that data from Yahoo Finance in the next video,
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then I have all on this site libraries and this code is not necessary in your case.
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So first, we need to import the data.
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In all, case, I have just input manually the data from makers all five.
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But.
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In the next chapter, I will show you how to import some data using Python and data which come from
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metaphor or frame.
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But.
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I think it's very important to begin by the easiest parts.
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So, for example, you can take the CSB, file it, so one of the things we find.
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Of the zip file attached to this video, this find contain nearly 10 currency data.
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OK.
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And you can take.
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The data that you'll then you just have to drag and drop here.
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In Google.
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OK.
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If you walk in Jupiter on a book, you need to have your notebooks and you see the file in the same
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folder.
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It's very important because a lot of you doesn't walk with Google Club and.
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So if you work with Jupyter Notebook, but you don't really muster it, you will have a problem then
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problem.
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OK, so if you or move very comfortable with Python and Jupiter on a book, I only advise you to work
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with Google.
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So first, we need to import the data to improve the data.
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I will create a new variable def for data.
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And I use the really as function from ponds to import the CSP file.
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So we need to put the name of the CSB find.
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And then we can enforce it and show what this fight is.
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OK, I have just four kids to run this, so.
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And now, as we can see, we have a little problem, OK?
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This problem is that.
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The three metre between each value is backslash T.
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OK, as we can see between each value we have.
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But that's OK because.
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The delimiter isn't a stone cold.
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And then and this is not able to.
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Find it automatically.
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Needed to specify the daily meter in the function as input.
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So backslash key.
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OK.
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I'll just put it here.
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And now, as we can see, we have a much better results.
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But we.
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Still wants to make some modification.
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First, we don't want the volume and spread color, so we needed to remove it.
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We need to remove sensory.
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And to do it.
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We will just create a little function to do all all necessary modification.
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So the input of the pension is the name of the CSB fight then.
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We want to remove this two columns, so to do it.
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We just.
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You, ADF, when you look, we take all the role and we take all of the rule from zero to minus two.
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So if you're not comfortable with this, I would advise you to read again.
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The BBB is talking about pandas in the chapter Python for that science.
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And then if.
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I use this function.
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I can see that.
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The last column as being removed.
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But we still want to make some modification.
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First, we want that dates.
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Is the index OK, because financial data?
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Or.
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Time series and soon we want the dates as index and to do it.
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We just need to put heat index quote equal.
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Date.
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So if we use like this, we will have dates as indexed, but pundits.
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Doesn't understand that this dates or date.
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OK.
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It will.
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Take this column as a story.
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OK, so you need to specify porous.
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Date Eagle.
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So now the last thing that we need to do and is one of the most important is to rename the column and
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it's very, very important one because.
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And you have only two we can use data which come from several sources.
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And if I take data which come from Methods four or five manually, you will have opened with capital
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letter.
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OK.
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If I inputs the same data using pattern.
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OK, so the data which comes from is a little fun.
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OK.
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You will have.
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Open a local and te volume in knots in capital later.
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OK, so.
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The name of the killer.
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Can change very often.
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OK.
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And so if you create a trading system that demand the close price, for example, and you call growth
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in capitalism, OK, the next dataframe will have.
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One time close in.
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Capitalism.
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One thing close, for example, like this one time like this, one time like this,
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this execution.
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OK, so.
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If each time you had another notation, each time you will need to do some much of the modification
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in your trading strategy.
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And it's a really big problem because the goal of the ultimate ization is that we don't need it to.
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Modifying something every time.
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OK.
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So to avoid you have this problem.
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We will use.
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We will rename the column.
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So first, we need to rename the name of the column, so personally, I use this syntax.
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OK.
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But in your own protest, you can use the syntax that you want.
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OK.
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The only main point is to keep it all right.
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So open a close and the thick volume.
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OK will be named volume, but we know that is not really the volume, but you can take it to create
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some indicators that demand the volume.
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OK, but it's not.
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Really a good method to do it.
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OK, but sometimes if you don't have the volume and you want to try something using the volume, you
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can take this color OK.
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But I don't really advise you to do it if you don't really muster what you do.
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So and then I just rename the name of the index.
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This is not really mandatory, but.
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I think it's a good practice.
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And then we have the same.
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Data from OK, so we are speaking with.
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This data, OK?
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And we have this data, so we I think we have very much.
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Preprocessing, well, the data now I will show you how to process the data, which comes from why finance?
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OK?
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To show you the importance to process the data using the same syntax for data which come from several
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sources.
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Now I will show you how to import some data, which come from make a total of five manually.
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OK, so.
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I don't have show you how to do it automatically.
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OK at this step, because for both of you which have a map, it's a little bit more complicated for
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all you colleagues which work with Windows because you need to have a Windows device to use the Method
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four or five library in Python.
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OK.
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But I will show you how to install Windows on your Mac using camp or an application like Perl Desktop
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or.
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It's a VP's Exeter.
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OK, so I will give you some.
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Possibility, OK.
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To fix this issue.
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So don't worry.
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But I think is not the time to fix this issue because.
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Were puts your algorithm in life, shooting is the last thing to do.
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OK.
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After before that, you needed to have a profitable strategy, and all the work is to find the profitable
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strategy.
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Once you have most profitable strategy, put it in life, writing is really the last of your.
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Issues, OK.
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So to import some data, it's very simple.
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OK, you need to install the metal frame.
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Application, OK.
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It's available for Mac and Windows.
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Then you give him her you.
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Go him on bomb or ticks and you imports the assets that you want.
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OK.
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You can go check for some crypto exit to exit two.
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If you choose one.
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You choose the time frame.
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OK, you choose the date and then you do you make your request?
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I just want to.
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I like that point is that in my case, I have to put just one time on request.
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Sometimes you need to put several time.
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Okay, so for example.
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If I put daily, OK, it's possible that I need to.
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Puts multiple time on request, so not this time, but if you have to put multiple time on request,
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don't worry, it's normal, OK?
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And then you export the raw, all the chips and you will have your data, so it's very easy to do it.
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