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 Features:
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 Features:
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
Requirements:
- 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:
- Python fundamentals
- ARIMA (Auto-Regressive Integrated Moving Averages)
- EWMA (Exponentially Weighted Moving Average)
- ETS (Error-Trend-Seasonality)
- NumPy for high-speed digital processing
- Pandas for effective data analysis
- Matplotlib for data visualization
- Using Pandas-DataReader & Quandl for data ingestion
- Autocorrelation plots and biased autocorrelation plots
- Pandas time series analysis procedures
- Algorithmic exchanging with Quantopian
- Cumulative daily returns
- Stock returns analysis
- Volatility & protection risk
- Efficient frontier & Markowitz optimization
- Portfolio allocation optimization
- Sharpe ratio
- Statsmodels
- Types of funds
- Order books
- Short selling
- The capital asset pricing model
- Stock splits & returns
- Efficient business philosophy
- Futures speculation
Who this study is for:
- Python developers who want to study about Financial Analysis
Author: Jose Portilla
Language: English
Size: 2.44GB
