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Development Programming Languages Finance Fundamentals

Python for Financial Analysis and Algorithmic Trading

Learn numpy , pandas , matplotlib , quantopian , finance , and more for algorithmic trading with Python!
Bestseller
Rating: 4.5 out of 54.5 (15,016 ratings)
100,796 students
Created by Jose Portilla
Last updated 12/2020
English
English [Auto], French [Auto], 
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

14 sections • 120 lectures • 16h 38m total length

  • Preview02:12
  • 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
    00:58

  • Note on yml File
    00:16
  • Course Installation Guide
    08:48

  • 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

  • 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

  • Preview00:22
  • 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

  • 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

  • Introduction to Data Sources
    01:21
  • Note on Pandas Datareader
    00:09
  • Pandas DataReader
    04:37
  • Quandl
    10:21

  • Welcome to Pandas for Time Series
    00:13
  • Introduction to Time Series with Pandas
    Preview00:58
  • Preview09:38
  • Time Resampling
    Preview12:48
  • Time Shifts
    05:58
  • Pandas Rolling and Expanding
    17:52

  • Welcome to the Capstone Project!
    00:30
  • Preview06:38
  • Preview20:25
  • Preview09:36
  • Stock Market Analysis Project Solutions Part Three
    16:52
  • Stock Market Analysis Project Solutions Part Four
    08:23

  • 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
  • Preview11:42
  • Quick Note on Second Milk Difference!
    00:31
  • ARIMA Code Part Two
    13:59
  • ARIMA Code Part Three
    06:49
  • Preview14:14
  • Discussion on choosing PDQ
    00:08

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 this course is for:

  • Someone familiar with Python who wants to learn about Financial Analysis!

Instructor

Jose Portilla
Head of Data Science, Pierian Data Inc.
Jose Portilla
  • 4.6 Instructor Rating
  • 720,351 Reviews
  • 2,204,076 Students
  • 32 Courses

  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, McKinsey and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.

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