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!
Python for Financial Analysis and Algorithmic Trading
16h 54m 20s
English
Paid
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
About the Author: Udemy
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.
Watch Online 112 lessons
0:00
/ #1: Introduction to Course
All Course Lessons (112)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 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.