Algorithmic trading in Python has revolutionized financial markets, enabling quantitative traders to execute complex strategies with efficiency. Whether you’re exploring crypto trading strategies or seeking the best algorithmic trading software in the USA, mastering NumPy is essential. This powerful library simplifies data handling, making it a cornerstone of quantitative analysis for trading in Singapore. In this video, we’ll explore NumPy fundamentals, its role in algorithmic trading, and how it powers automated strategies, including Freqtrade strategy for crypto traders.
It was the object oriented programming and I and I know that it was comprehensively covered we have covered each and every topic of objectoriented programming then we covered the advanced python which includes file handling exception handling decorators name spaces and now we’ll start with few of the important libraries which includes numai pandas and few of the data visual libraries let me know if you ready for the adventure and let’s get started Ed without a further Ado soÂ
Let’s start with the first important and fundamental library that is numpy numpy stands for numerical python in short or in a single line you can say that numpy is the fundamental package for scientific Computing and why fundamental because all of the advanced packages or the libraries or the modules somehow uses the numpy so numpy is the essential package for scientific Computing in python it provides support for large multi-dimensional arrays and matrices because we can have 1D arrays 2D arrays 3D arrays so that’s why you can say it provides support for the large multi-dimensional arrays next Library components it offers a multidimensional array object which we have seen already and it includes derived objects such as masked arrays and matrices the main highlight you can see that is the multi-dimensional array object and from that there are so many derived object such as mask arrays and matrices and it also provides a collection of routines for fast array operations because.
We know that other python sequences like list dictionaries all are slow compared to the numpy and let me tell you that why numpy was introduced in the infant stage that time initially people were using mat lab R for the machine learning operations but the issue was that the mat lbel are was a little bit tough to learn because in machine learning people were coming from the so many various Fields like from the mathematics from the science even from the Arts and Commerce so it becomes little bit hard to learn those languages right but everyone knew that python is simpler correct but the issue was that the python was so slow and the numpy solves the biggest problem of python that is the slowness it’s somehow able to Pace with the other languages in terms of speed so that’s why it was introduced and next in the array operations.
It can support a wide range of mathematical operations it can facilitates logical operations on arrays it also allows for efficient reshaping and resizing of arrays it also include functions for sorting and selecting elements than input output operations it also offers tools for performing discrete forear transforms it provides basic linear algebra operations it includes basic statistical functions and it facilitates random number generation and simulations so the core object is the ND array this is the central feature of numi the ND array object it encapsulates nend dimensional arrays of homogeneous data types because.
We know that list is a heterogeneous data type because it can contain a string integer float or any other data type but this andary encapsulates the homogeneous data types and because of that it is way faster than the python lists now let’s compare numpy arrays and python sequences python sequences includes list dictionaries sets and many more so let’s start the first point is fixed size numai arrays have a fixed size ad creation unlike python list which can grow dynamically we know that there is no limit on the list uh number of elements we can have as much as as we want so nump array have a fixed size and because of that it is faster then one more point that changing the size of an ND array creates a new array and deletes the original we cannot extend the original array in order to change the size what will happen the original array will be deleted and a new array will be create the next point is homogeneous data types.
We can have all the data types like list dictionary string integer float anything but in nump Array all elements must be of the same data type and because of that all the elements are of same size the next is efficiency in operations numpy arrays support Advanced mathematical and other operations on large data sets such operations are typically more efficient and require less code than using Python’s built-in sequences of course because of the fixed size and the homogeneous data type we know that we have to write the less code and it will be more efficient than the python sequence the last one is the wide adoption in scientific and mathematical packages many scientific and mathematical python based packages Ed numpy array these packages often support python sequence input but converts such input to numpy arrays for processing they frequently output results as numpy arrays so now as.
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Day 20: Numpy Fundamentals
