Python Financial Analysis and Algorithmic Trading:
This study covers Python financial analysis and algorithmic trading. You will study Python financial analysis by practicing NumPy, Matplotlib, Pandas, Finance, Quantopian, and much more for algorithmic trading with Python. This study will conduct you through everything you need to know to use Python for finance and algorithmic trading. We will start by mastering the fundamentals of Python, and then advance to learn about the numerous core libraries used in the Py-Finance Ecosystem, including Pandas, NumPy, Jupyter, Matplotlib, Quantopian, Zipline, Statsmodels, and much more. Are you fascinated by how people use Python to administer meticulous business analysis and persevere algorithmic speculation, then this is the right course is for you.
NumPy is the elemental bundle for scientific computing with Python, a library for the Python, combining support for large, multi-dimensional arrays and matrices, with a comprehensive collection of high-level arithmetical functions to operate on certain arrays.
A compelling N-dimensional array object
Tools for integrating C/C++ and Fortran code
Sophisticated (broadcasting) functions
Helpful linear algebra, Fourier transform, & random number capabilities
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python and its digital arithmetic extension NumPy. It renders an object-oriented API for embedding plots into applications utilizing general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. Matplotlib makes easy things easy and hard things possible.
Create: Develop publication-quality plots with just a few lines of code, use interactive figures that can zoom, pan, update.
Customize: Take full control of line styles, font properties, axes attributes, export, and embed to a number of file formats and interactive environments.
Extend: Explore tailored functionality provided by third-party packages, learn more about Matplotlib through the various external education resources.
What you’ll learn:
Exercise ARIMA models on time series statistics
Use NumPy to promptly work with numerical data
Calculate financial statistics, such as daily returns, volatility, cumulative returns
Use Pandas to interpret and visualize data
Practice exponentially weighted starting averages
Learn how to use Statsmodels for time series analysis
Determine the Sharpe ratio
Optimize portfolio allocations
Use Matplotlib to generate custom plots
Learn the capital asset pricing model
Conduct algorithmic exchanging on Quantopian
Study about the effective business hypothesis
Basic understanding of Python programming language
Fundamental Statistics and Linear Algebra will be applicable
Ability to download Anaconda (Python) to your computer
We will incorporate the subsequent topics adopted by financial specialists: