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:
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
Study Topics:
01 Course Introduction
1 1.01 Introduction to Course
2 1.02 Course Overview Lecture (DON'T SKIP THIS!)
02 Course Materials and Set-up
1 2.01 Course Installation Guide
03 Python Crash Course
1 3.01 Welcome to the Python Crash Course
2 3.02 Introduction to Crash Course
3 3.03 Python Crash Course Part One
4 3.04 Python Crash Course Part Two
5 3.05 Python Crash Course Part Three
6 3.06 Python Crash Course Exercises
7 3.07 Python Crash Course Exercise Solutions
04 NumPy
1 4.01 Welcome to NumPy
2 4.02 Introduction to NumPy
3 4.03 NumPy Arrays
4 4.04 Numpy Operations
5 4.05 Numpy Indexing
6 4.06 NumPy Review Exercise
7 4.07 Numpy Exercise Solutions
05 General Pandas Overview
1 5.01 Welcome to Pandas
2 5.02 Introduction to Pandas
3 5.03 Series
4 5.04 DataFrames
5 5.05 DataFrames Part Two
6 5.06 DataFrames Part Three
7 5.07 Missing Data
8 5.08 Group By with Pandas
9 5.09 Merging, Joining, and Concatenating DataFrames
10 5.10 Pandas Common Operations
11 5.11 Data Input and Output
12 5.12 General Pandas Review Exercises
13 5.13 General Pandas Exercise Solutions
06 Visualization with Matplotlib and Pandas
1 6.01 Welcome to Visualization
2 6.02 Introduction to Visualization in Python
3 6.03 Matplotlib Basics - Part One
4 6.04 Matplotlib Basics - Part Two
5 6.05 Matplotlib Part Three
6 6.06 Matplotlib Exercise
7 6.07 Matplotlib Exercise Solutions
8 6.08 Pandas Visualization Overview
9 6.09 Pandas Time Series Visualization
10 6.10 Pandas Visualization Exercise Overview
11 6.11 Pandas Visualization Exercise Solutions
07 Data Sources
1 7.01 Introduction to Data Sources
2 7.02 Pandas DataReader
3 7.03 Quandl
08 Pandas with Time Series Data
1 8.01 Welcome to Pandas for Time Series
2 8.02 Introduction to Time Series with Pandas
3 8.03 Datetime Index
4 8.04 Time Resampling
5 8.05 Time Shifts
6 8.06 Pandas Rolling and Expanding
09 Capstone Stock Market Analysis Project
1 9.01 Welcome to the Capstone Project!
2 9.02 Stock Market Analysis Project
3 9.03 Stock Market Analysis Project Solutions Part One
4 9.04 Python Stock Market Analysis Solutions - Part Two
5 9.05 Stock Market Analysis Project Solutions Part Three
6 9.06 Stock Market Analysis Project Solutions Part Four
10 Time Series Analysis
1 10.01 Welcome to Time Series Analysis
2 10.02 Introduction to Time Series
3 10.03 Time Series Basics
4 10.04 Introduction to Statsmodels
5 10.05 ETS Theory
6 10.06 EWMA Theory
7 10.07 EWMA Code Along
8 10.08 ETS Code Along
9 10.09 ARIMA Theory
10 10.10 ACF and PACF
11 10.11 ARIMA with Statsmodels
12 10.12 ARIMA Code Part Two
13 10.13 ARIMA Code Part Three
14 10.14 ARIMA Code Part Four
11 Python Finance Fundamentals
1 11.01 Welcome to Finance Fundamentals
2 11.02 Introduction to Python Finance Fundamentals
3 11.03 Sharpe Ratio Slides
4 11.04 Portfolio Allocation Code Along Part One
5 11.05 Portfolio Allocation Code Along Part Two
6 11.06 Portfolio Optimization
7 11.07 Portfolio Optimization Code Along One
8 11.08 Portfolio Optimization Code Along Two
9 11.09 Portfolio Optimization Code Along Three
10 11.10 Key Financial Topics
11 11.11 Types of Funds
12 11.12 Order Books
13 11.13 Short Selling
14 11.14 CAPM - Capital Asset Pricing Model
15 11.15 CAPM Code Along
16 11.16 Stock Splits and Dividends
17 11.17 EMH
12 Basics of Algorithmic Trading with Quantopian
1 12.01 Welcome to the Quantopian Section
2 12.02 Introduction to Quantopian
3 12.03 Quantopian Research Basics
4 12.04 Quantopian Algorithms Basics Part One
5 12.05 Quantopian Algorithms Basics Part Two
6 12.06 First Trading Algorithm - Part One
7 12.07 First Trading Algorithm - Part Two
8 12.08 Trading Algorithm Exercise
9 12.09 Trading Algorithm Exercise Solutions Part One
10 12.10 Trading Algorithm Exercise Solutions Part Two
11 12.11 Quantopian Pipelines Factors
12 12.12 Quantopian Pipelines Filters
13 12.13 Quantopian Pipeline - Masking and Classifiers
13 Advanced Quantopian and Trading Algorithms
1 13.01 Welcome to Trading Algorithms
2 13.02 Pipeline Trading Algorithm Example - Code Along - Part One
3 13.03 Pipeline Trading Algorithm - Code Along - Part Two
4 13.04 Pipeline Trading Algorithm Code along Part Three
5 13.05 Leverage
6 13.06 Hedging
7 13.07 Hedging- Part Two
8 13.08 Portfolio Analysis with PyFolio
9 13.09 Stock Sentiment Analysis Project
10 13.10 What are Futures
11 13.11 Futures on Quantopian
12 13.12 Futures on Quantopian Part Two
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