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Welcome back to the day 22 of the 100 days of hell with python algo trading today we’ll be starting with one of the most important part of the uh algo trading that is floating charts floating graphs because you know that without charts we cannot perform algo trading right so we will continue the numai series and this will be the last session on the numpy so in this session we will learn how to PLO any chart and after that we’ll learn few tricks of numpy which will be really usefulÂ
We’ll learn about some functions which might make a difference in your algo trading definitely so let’s start with plotting a first 2D plot so what we will do first we’ll create few points so okay let me first import the numpy import numpy as NP right then what we can do L NP do Lin space and in that I will take 100 e distance point between -10 and positive 10 right so -10 to positive 10 and I need 100 points right and nowÂ
We’ll plot a chart for the X = to Y and you know that what is xals to Y so what I’ll do I’ll just right y equal to X now we have the data to chart the plot but one thing is missing and that thing is charting library right so for that we can use here import mat plot lib do pyplot as PLT right we’ll also learn this completely but for now just you have to import this and after that you need to use PLT Dot Plot and inside that you have to pass both the points so X and Y and when you hit shift enter you will see that.
We have a graph for the straight line X = to Y you know that right now let’s try to plot a chart for y = x² and if you remember then let me know in the commments that yal x² is for which kind of plots so let’s take the same 100 points so what I’ll do I’ll just copy and paste this here and here instead of Y y = to X I’ll do y = x² correct and now when I hit shift enter you will see that we have a graph for Parabola because that is the equation for Parabola right y = x² please let me know if you need to understand this I’ll explain in any other videos completely right now let’s try to plot a sine wave so for that also I’ll take the same points correct and instead of yal X I’ll make it s of X that’s and when.
We hit shift enter okay we have to write NP do sign NP that is nump do sign of X and you will see that we have a sine wave correct it’s very easy I guess if you have studied this in your college or your school then should not be difficult very very basic equations right now we plot a chart for X log X so for that also we’ll keep it exactly same so X multiplied by NP do log of x right and now when we hit shift enter we’ll have a logarithm plot and last but not the least we can also plot a chart for sigmoid and in fact we can plot chart for any mathematical operation right so I’ll just STI the same value and here we will apply the sigmoid formula and we have seen the sigmo formula already and that was 1 / 1 + exponential of X right and now when we shift enter you will see that.
We have a chart for sigmoid I think we have to keep this inside the bracket and now when we shift enter yeah that is the exact chart for the sigmoid function and this is how you can plot chart for any mathematical operations you can try yourself to apply different different mathematical formulas and try to plot the chart for them now let’s start with some important uh numi functions which will be really useful in your algor tring journey right so so what is happening here you will see that you have the name of function and along with that you will be having the documentation link for that function soÂ
If you face any issue you can just click on the link and see the documentation and proceed right so let’s start with the np. sort so actually we have already seen the sort and sorted in the list but the benefit of np. sort is that you will receive a nump array right which can be quite useful sometimes okay so let’s try this now here we have two arrays the first is ohlc 1 which is at 1D array and the second is ohlc which is a 2d array right so let’s try to apply this NP do sort on these two arrays so I’ll write NP do sort np. sort and I’ll give ohlc one so when we hit shift enter you will see that we have received this array in sorted form right and so here if you want this in a descending order you have to use the slicing here and negative 1 and you will receive an descending array correct similarly let’s apply this on the uh 2D aray so np. sort and I’ll give ohlc 2 and when we hit shift enter okay now let’s try this on the 2D array so I’ll write np. sort and ohlc 2 right so when we hit shift enter you will receive that.
We have received an array and it is sorting by default rowwise so you can see that here we had the 910 which is at the last and 210 which is at the first right and if you want this column wise what you going do you can just write XIs is equals to 2 0 and it will be a column wise now you can see earlier 9875 and we have 5789 so that’s how you can achieve this and if you make it one it will be rowwise it is very simple actually if you perform some practice questions you are good to go right the next we have is np. append so here what happens it append values along the mentioned AIS at the end of the array so let’s take the same example so I’ll copy and paste it here now let’s apply this so NP do append and name of the array and the value so you can see that the 500 has been appended at the end of the array right okay now let’s apply this np. append on 2D array so how we can do that NP do upend and the name ofÂ
The array that is ohlc 2 right then we have to provide the values the array so here what we can do we can create another array with the shape 4×1 right so so np. a range and inside that we know that we need an array of 4×1 because here we have four rows and we just want to add another column so 4 by one and then we can reshape actually here we have to give the range so range I will give four and reshape I give in the 4 by 1 correct and then in the last you have to give the axis so axis will be one correct for the column so when you hit shift enter you will see that in this array and another 0 1 2 3 has been added so it becomes now 4×5 correct so that’s how you can use the np. append let’s move to the next function which is np. conc AG and with the name also it signifies that it will be somehow adding some values through the arrays right now next.
We have the np. conage and the name also signifies that it will somehow can be used for concatenation right so let’s create another two is so I’ll write C is equal to NP do a range and I’ll give a range up to let’s say 12 and then uh we’ll reshape to 4×3 correct similarly we’ll create another array which will be D let’s make it also 4×3 correct and we can make the range from in here 12 to 24 let’s see the arrays so C is this one and this is the D correct so we can simply use the function NP dot concatenate and here we’ll provide the arrays so the first is C and second is D then we have to give the AIS so AIS is equals to 1 and you will see that it has been added right let me show you first the arrays so this was the print C and print D so these were the arrays earlier and after concatenation you can see that it has been added you can say horizontally right similarly if you give here the axis as zero it will be concatenated vertically so that’s how you can use this and it will be very useful in future because we will be always using this function right next function we have is the np. unique and with the name signifies that we can find the unique values out of any array so let’s create first an array NP do array and inside that I’ll give some values like 1A 1 then 2A 2 3A 3 4A 4 5A 5 6 7 8 and now when we apply this function NP do unique to let’s say it is e is equals to and here I’ll give the value e so when we hit shift enter you will see that it will only output the unique values right so this is also useful sometimes now.
We have the next function which is np. expand dims so means we can use this function to expand the dimensions so you can read here that so we can get the expanded dimensions of an array so let’s take this example ohlc 1 and apply the shape we know that it is it is 1D right so if I want to use the np. expand dims what I can do I can just write np. expand thems and I can give the name of the array that is o one and we have to give the axis so let’s say I provide axis is equals to 1 so when you hit shift enter you will see that it has been expanded in the vertical Direction so now when you check the let’s say we apply fals to this and now when you check the shape of this F do shape it is 12a 1 earlier it was only 12 means 1D now it has become 2D by expanding one dimension similarly you can use x equal to0 and when you hit shift enter you will see that it has been expanded horizontally right and even you can see the array so let’s check this and this is the 2D array right so that’s how you can use the uh X1 Dimension function it’s very easy now.
Watch this Day 22 video tutorial
Day 22: Plotting Charts & Special NumPy Functions
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