Python
Most Essential Python Code Snippets
In this article, we are going to share with you the most essential Python code snippets that you’ll ever need to write.
Python
Tutorial
How to Create Your Own Arbitrage Bot Using Python
In this article, we have created our own Arbitrage Bot Using Python Programming Language And CCXT Library.
Tutorial
How To Create Your Own Python HFT Trading Bot
If you want to create your own high-frequency trading bot, here is a simple guide using Python. You will need a few libraries to get started. The first is the Python Bloomberg API. You can find the installation instructions here. The second is the Python Alpaca API. You can find
Tutorial
Python Alpaca API: Installation Instructions
In order to use the Python Alpaca API, you will need to install the Alpaca Python SDK. The Alpaca Python SDK can be installed via pip: pip install alpaca-trade-api Alternatively, if you prefer, you can clone the GitHub repository and install the SDK manually: git clone https://github.com/alpacahq/
Tutorial
Python Bloomberg API Installation Instructions
This Python Bloomberg API installation guide will show you how to install the Python Bloomberg API on your computer. The Python Bloomberg API is a wrapper around the C++ Bloomberg API. It allows you to access Bloomberg data through Python code. The Python Bloomberg API is compatible with Python 2.
Hackerrank
Hackerrank Mean, Var, and Std Solution
mean The mean tool computes the arithmetic mean along the specified axis. import numpy my_array = numpy.array([ [1, 2], [3, 4] ]) print numpy.mean(my_array, axis = 0) #Output : [ 2. 3.] print numpy.mean(my_array, axis = 1) #Output : [ 1.5 3.5] print numpy.mean(my_array, axis
Hackerrank
Hackerrank Polynomials Solution
poly The poly tool returns the coefficients of a polynomial with the given sequence of roots. print numpy.poly([-1, 1, 1, 10]) #Output : [ 1 -11 9 11 -10] roots The roots tool returns the roots of a polynomial with the given coefficients. print numpy.roots([1, 0, -1]) #Output
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Hackerrank Linear Algebra Solution
The NumPy module also comes with a number of built-in routines for linear algebra calculations. These can be found in the sub-module linalg. linalg.det The linalg.det tool computes the determinant of an array. print numpy.linalg.det([[1 , 2], [2, 1]]) #Output : -3.0 linalg.eig The linalg.
Hackerrank
Hackerrank Eye and Identity Solution
Solution in python3 Approach 1. import numpy print(numpy.eye(*map(int,input().split()))) Approach 2. import numpy N, M = map(int, input().split()) print(numpy.eye(N, M)) Solution in python import numpy N,M = map(int,raw_input().split()) print numpy.eye(N,M)
Hackerrank
Hackerrank Zeros and Ones Solution
Solution in python3 Approach 1. import numpy N = list(map(int, input().split())) print(numpy.zeros(N, int)) print(numpy.ones(N, int)) Approach 2. import numpy size = tuple(map(int,input().strip().split())) print( numpy.zeros(size, int) ) print( numpy.ones(size, int) ) Approach 3. import numpy N = [int(
Hackerrank
Hackerrank Floor, Ceil and Rint Solution
Solution in python3 Approach 1. import numpy a = numpy.array(input().split(),float) print(numpy.floor(a),numpy.ceil(a),numpy.rint(a),sep="\n") Approach 2. from numpy import * A = array([float(x) for x in input().split()],float) print(floor(A),ceil(A),rint(A),sep
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Hackerrank Array Mathematics Solution
Basic mathematical functions operate element-wise on arrays. They are available both as operator overloads and as functions in the NumPy module. import numpy a = numpy.array([1,2,3,4], float) b = numpy.array([5,6,7,8], float) print a + b #[ 6. 8. 10. 12.] print numpy.add(a,