CF

Python for Financial Analysis and Algorithmic Trading

16h 54m 20s
English
Paid

Python for Financial Analysis and Algorithmic Trading is a 112-lesson 16 hours 54 minutes self-paced course by Udemy. Welcome to Python for Financial Analysis and Algorithmic Trading!

Course facts

Lessons
112
Duration
16 hours 54 minutes
Level
All levels
Language
English
Updated
Instructor
Udemy
Price
Premium

Welcome to Python for Financial Analysis and Algorithmic Trading! Are you interested in how people use Python to conduct rigorous financial analysis and pursue algorithmic trading, then this is the right course for you! This course will guide you through everything you need to know to use Python for Finance and Algorithmic Trading! We'll start off by learning the fundamentals of Python, and then proceed to learn about the various core libraries used in the Py-Finance Ecosystem, including jupyter, numpy, pandas, matplotlib, statsmodels, zipline, Quantopian, and much more!

We'll cover the following topics used by financial professionals:

  • Python Fundamentals
  • NumPy for High Speed Numerical Processing
  • Pandas for Efficient Data Analysis
  • Matplotlib for Data Visualization
  • Using pandas-datareader and Quandl for data ingestion
  • Pandas Time Series Analysis Techniques
  • Stock Returns Analysis
  • Cumulative Daily Returns
  • Volatility and Securities Risk
  • EWMA (Exponentially Weighted Moving Average)
  • Statsmodels
  • ETS (Error-Trend-Seasonality)
  • ARIMA (Auto-regressive Integrated Moving Averages)
  • Auto Correlation Plots and Partial Auto Correlation Plots
  • Sharpe Ratio
  • Portfolio Allocation Optimization 
  • Efficient Frontier and Markowitz Optimization
  • Types of Funds
  • Order Books
  • Short Selling
  • Capital Asset Pricing Model
  • Stock Splits and Dividends
  • Efficient Market Hypothesis
  • Algorithmic Trading with Quantopian
  • Futures Trading
Requirements:
  • Some knowledge of programming (preferably Python)
  • Ability to Download Anaconda (Python) to your computer
  • Basic Statistics and Linear Algebra will be helpful
Who this course is for:
  • Someone familiar with Python who wants to learn about Financial Analysis!

What you'll learn:

  • Use NumPy to quickly work with Numerical Data
  • Use Pandas for Analyze and Visualize Data
  • Use Matplotlib to create custom plots
  • Learn how to use statsmodels for Time Series Analysis
  • Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc..
  • Use Exponentially Weighted Moving Averages
  • Use ARIMA models on Time Series Data
  • Calculate the Sharpe Ratio
  • Optimize Portfolio Allocations
  • Understand the Capital Asset Pricing Model
  • Learn about the Efficient Market Hypothesis
  • Conduct algorithmic Trading on Quantopian

Who teaches Python for Financial Analysis and Algorithmic Trading? Udemy

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Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

What lessons are included in Python for Financial Analysis and Algorithmic Trading?

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#1: Introduction to Course
All Course Lessons (112)
#Lesson TitleDurationAccess
1
Introduction to Course Demo
02:13
2
Course Overview Lecture (DON'T SKIP THIS!)
03:33
3
Course Installation Guide
08:49
4
Welcome to the Python Crash Course
00:20
5
Introduction to Crash Course
01:17
6
Python Crash Course Part One
19:01
7
Python Crash Course Part Two
13:38
8
Python Crash Course Part Three
15:03
9
Python Crash Course Exercises
04:14
10
Python Crash Course Exercise Solutions
09:07
11
Welcome to NumPy
00:24
12
Introduction to NumPy
01:38
13
NumPy Arrays
15:49
14
Numpy Operations
04:20
15
Numpy Indexing
10:55
16
NumPy Review Exercise
04:11
17
Numpy Exercise Solutions
09:53
18
Welcome to Pandas
00:23
19
Introduction to Pandas
02:40
20
Series
06:59
21
DataFrames
15:35
22
DataFrames Part Two
17:00
23
DataFrames Part Three
09:03
24
Missing Data
06:15
25
Group By with Pandas
06:38
26
Merging, Joining, and Concatenating DataFrames
09:11
27
Pandas Common Operations
12:13
28
Data Input and Output
13:51
29
General Pandas Review Exercises
03:07
30
General Pandas Exercise Solutions
12:54
31
Welcome to Visualization
00:24
32
Introduction to Visualization in Python
01:49
33
Matplotlib Basics - Part One
18:46
34
Matplotlib Basics - Part Two
15:32
35
Matplotlib Part Three
11:44
36
Matplotlib Exercise
03:43
37
Matplotlib Exercise Solutions
10:09
38
Pandas Visualization Overview
12:08
39
Pandas Time Series Visualization
17:33
40
Pandas Visualization Exercise Overview
01:19
41
Pandas Visualization Exercise Solutions
08:52
42
Introduction to Data Sources
01:22
43
Pandas DataReader
04:38
44
Quandl
10:22
45
Welcome to Pandas for Time Series
00:14
46
Introduction to Time Series with Pandas
00:59
47
Datetime Index
09:40
48
Time Resampling
12:49
49
Time Shifts
05:59
50
Pandas Rolling and Expanding
17:54
51
Welcome to the Capstone Project!
00:31
52
Stock Market Analysis Project
06:39
53
Stock Market Analysis Project Solutions Part One
20:26
54
Python Stock Market Analysis Solutions - Part Two
09:37
55
Stock Market Analysis Project Solutions Part Three
16:54
56
Stock Market Analysis Project Solutions Part Four
08:24
57
Welcome to Time Series Analysis
00:35
58
Introduction to Time Series
02:52
59
Time Series Basics
04:00
60
Introduction to Statsmodels
12:30
61
ETS Theory
04:17
62
EWMA Theory
02:50
63
EWMA Code Along
14:25
64
ETS Code Along
06:25
65
ARIMA Theory
09:34
66
ACF and PACF
06:21
67
ARIMA with Statsmodels
11:43
68
ARIMA Code Part Two
14:00
69
ARIMA Code Part Three
06:50
70
ARIMA Code Part Four
14:15
71
Welcome to Finance Fundamentals
00:37
72
Introduction to Python Finance Fundamentals
00:50
73
Sharpe Ratio Slides
07:17
74
Portfolio Allocation Code Along Part One
15:32
75
Portfolio Allocation Code Along Part Two
06:45
76
Portfolio Optimization
05:15
77
Portfolio Optimization Code Along One
14:45
78
Portfolio Optimization Code Along Two
07:47
79
Portfolio Optimization Code Along Three
16:33
80
Key Financial Topics
01:08
81
Types of Funds
06:10
82
Order Books
14:36
83
Short Selling
02:36
84
CAPM - Capital Asset Pricing Model
05:20
85
CAPM Code Along
12:11
86
Stock Splits and Dividends
03:17
87
EMH
02:01
88
Welcome to the Quantopian Section
00:25
89
Introduction to Quantopian
09:28
90
Quantopian Research Basics
16:47
91
Quantopian Algorithms Basics Part One
16:18
92
Quantopian Algorithms Basics Part Two
17:18
93
First Trading Algorithm - Part One
16:48
94
First Trading Algorithm - Part Two
16:45
95
Trading Algorithm Exercise
04:51
96
Trading Algorithm Exercise Solutions Part One
12:37
97
Trading Algorithm Exercise Solutions Part Two
02:39
98
Quantopian Pipelines Factors
17:00
99
Quantopian Pipelines Filters
05:59
100
Quantopian Pipeline - Masking and Classifiers
09:19
101
Welcome to Trading Algorithms
00:49
102
Pipeline Trading Algorithm Example - Code Along - Part One
13:35
103
Pipeline Trading Algorithm - Code Along - Part Two
10:28
104
Pipeline Trading Algorithm Code along Part Three
19:29
105
Leverage
12:49
106
Hedging
14:19
107
Hedging- Part Two
14:55
108
Portfolio Analysis with PyFolio
15:21
109
Stock Sentiment Analysis Project
16:24
110
What are Futures?
09:03
111
Futures on Quantopian
18:21
112
Futures on Quantopian Part Two
20:35
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Frequently asked questions

What prerequisites should I have before enrolling in the course?
Before enrolling, you should have a basic understanding of programming concepts. The course starts with a Python Crash Course, so no prior knowledge of Python is required. However, familiarity with fundamental programming logic and structures will be beneficial.
What projects will I build during the course?
The course includes a Stock Market Analysis Project as part of the capstone project section. This project will involve using libraries like pandas and statsmodels to analyze stock market data and perform time series analysis.
Who is the target audience for this course?
This course is designed for individuals interested in using Python for financial analysis and algorithmic trading. It is suitable for both beginners who want to learn Python and intermediate learners who wish to apply it in finance.
How does the depth of this course compare to other Python courses?
This course offers a specialized focus on Python's application in finance and trading, covering libraries like numpy, pandas, matplotlib, and statsmodels. Unlike general Python courses, it includes specific lessons on financial data analysis and visualization, providing targeted skills for financial applications.
Which specific tools and platforms are covered?
The course covers several core libraries and tools within the Python ecosystem, including jupyter for interactive computing, numpy for numerical operations, pandas for data manipulation, matplotlib for visualization, and statsmodels for statistical analysis. It also introduces platforms like Zipline and Quantopian for algorithmic trading.
What topics are not covered in the course?
The course does not cover advanced machine learning techniques or deep learning models. It is focused primarily on financial analysis and algorithmic trading using Python, without diving into broader data science or AI topics.
How much time should I expect to commit to this course?
With 112 lessons included, the course is comprehensive. While the total runtime is not specified, students should expect to spend several weeks completing lessons, exercises, and the capstone project, depending on their pace and familiarity with the material.