Python for Financial Analysis and Algorithmic Trading
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Python for Financial Analysis and Algorithmic Trading

Learn numpy , pandas , matplotlib , quantopian , finance , and more for algorithmic trading with Python!
Best Seller
4.7 (413 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
5,731 students enrolled
Created by Jose Portilla
Last updated 9/2017
English
English [Auto-generated]
Current price: $10 Original price: $195 Discount: 95% off
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Includes:
  • 17 hours on-demand video
  • 11 Articles
  • 2 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
Requirements
  • Some knowledge of programming (preferably Python)
  • Ability to Download Anaconda (Python) to your computer
  • Basic Statistics and Linear Algebra will be helpful
Description

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
Who is the target audience?
  • Someone familiar with Python who wants to learn about Financial Analysis!
Compare to Other Financial Trading Courses
Curriculum For This Course
118 Lectures
16:55:35
+
Course Introduction
4 Lectures 07:10

Course Overview Lecture (DON'T SKIP THIS!)
03:32

Did you skip the last lecture? Please go back and view it!
00:25

Course FAQ
01:00
+
Course Materials and Set-up
2 Lectures 09:33
Course Installation Help Notes
00:45

Course Installation Guide
08:48
+
Python Crash Course
7 Lectures 01:02:33
Welcome to the Python Crash Course
00:19

Introduction to Crash Course
01:16

Python Crash Course Part One
19:00

Python Crash Course Part Two
13:37

Python Crash Course Part Three
15:02

Python Crash Course Exercises
04:13

Python Crash Course Exercise Solutions
09:06
+
NumPy
7 Lectures 47:01
Welcome to NumPy
00:23

Introduction to NumPy
01:37

NumPy Arrays
15:47

Numpy Operations
04:19

Numpy Indexing
10:54

NumPy Review Exercise
04:10

Numpy Exercise Solutions
09:51
+
General Pandas Overview
13 Lectures 01:55:35

Introduction to Pandas
02:39

Series
06:58

DataFrames
15:34

DataFrames Part Two
16:59

DataFrames Part Three
09:01

Missing Data
06:14

Group By with Pandas
06:37

Merging, Joining, and Concatenating DataFrames
09:10

Pandas Common Operations
12:12

Data Input and Output
13:50

General Pandas Review Exercises
03:06

General Pandas Exercise Solutions
12:53
+
Visualization with Matplotlib and Pandas
11 Lectures 01:41:48
Welcome to Visualization
00:23

Introduction to Visualization in Python
01:48

Matplotlib Basics - Part One
18:45

Matplotlib Basics - Part Two
15:31

Matplotlib Part Three
11:43

Matplotlib Exercise
03:42

Matplotlib Exercise Solutions
10:08

Pandas Visualization Overview
12:07

Pandas Time Series Visualization
17:32

Pandas Visualization Exercise Overview
01:18

Pandas Visualization Exercise Solutions
08:51
+
Data Sources
3 Lectures 16:19
Introduction to Data Sources
01:21

Pandas DataReader
04:37

Quandl
10:21
+
Pandas with Time Series Data
6 Lectures 47:27
Welcome to Pandas for Time Series
00:13




Time Shifts
05:58

Pandas Rolling and Expanding
17:52
+
Capstone Stock Market Analysis Project
6 Lectures 01:02:24
Welcome to the Capstone Project!
00:30




Stock Market Analysis Project Solutions Part Three
16:52

Stock Market Analysis Project Solutions Part Four
08:23
+
Time Series Analysis
16 Lectures 01:50:56
Welcome to Time Series Analysis
00:33

Introduction to Time Series
02:51

Time Series Basics
03:58

Introduction to Statsmodels
12:29

ETS Theory
04:16

EWMA Theory
02:49

EWMA Code Along
14:24

ETS Code Along
06:24

ARIMA Theory
09:33

ACF and PACF
06:20


Quick Note on Second Milk Difference!
00:31

ARIMA Code Part Two
13:59

ARIMA Code Part Three
06:49


Discussion on choosing PDQ
00:04
3 More Sections
About the Instructor
Jose Portilla
4.5 Average rating
61,742 Reviews
291,134 Students
13 Courses
Data Scientist

Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions.