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Python for Finance: Investment Fundamentals and Data Analytics

Python for Finance: Investment Fundamentals and Data Analytics

by 365 Careers
Publisher: Packt Publishing
Release Date: October 2018
ISBN: 9781789618976
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Video Description

Learn Python programming and conduct real-world financial analysis in Python: complete Python training

About This Video

  • Learn Python programming.
  • Learn to conduct Real - World Financial Analysis in Python.
  • A practical tutorial designed for programming beginners and aspiring data scientists.

In Detail

This course will take you on a journey where you'll learn how to code in Python. You will learn how to use Python in a real working environment and explore how Python can be applied in the world of Finance to solve portfolio optimization problems. The first part of the course is ideal for beginners and people who want to brush up on their Python skills. And then, once we have covered the basics, we will be ready to tackle financial calculations and portfolio optimization tasks. The Finance block of this course will teach you in-demand, real-world skills employers are looking for. This explains topics such as how to work with Python's conditional statements, functions, sequences, and loops, build investment portfolios, and more.

The code bundle for this video course is available at https://github.com/PacktPublishing/Python-for-Finance-Investment-Fundamentals-and-Data-Analytics

Table of Contents

  1. Chapter 1 : Welcome! Course Introduction
    1. What does the Course Cover? 00:04:13
  2. Chapter 2 : Introduction to programming with Python
    1. Programming Explained in 5 Minutes 00:05:04
    2. Why Python? 00:05:11
    3. Why Jupyter? 00:03:29
    4. Installing Python and Jupyter 00:05:45
    5. Jupyter’s Interface – the Dashboard 00:02:53
    6. Jupyter’s Interface – Prerequisites for Coding 00:06:15
    7. Python 2 vs Python 3: What's the Difference? 00:02:34
  3. Chapter 3 : Python Variables and Data Types
    1. Variables 00:04:52
    2. Numbers and Boolean Values 00:03:06
    3. Strings 00:05:44
  4. Chapter 4 : Basic Python Syntax
    1. Arithmetic Operators 00:03:24
    2. The Double Equality Sign 00:01:34
    3. Reassign Values 00:01:09
    4. Add Comments 00:01:25
    5. Line Continuation 00:00:50
    6. Indexing Elements 00:01:18
    7. Structure Your Code with Indentation 00:01:45
  5. Chapter 5 : Python Operators Continued
    1. Comparison Operators 00:02:11
    2. Logical and Identity Operators 00:05:36
  6. Chapter 6 : Conditional Statements
    1. Introduction to the IF statement 00:03:04
    2. Add an ELSE statement 00:02:39
    3. Else if, for Brief - ELIF 00:05:33
    4. A Note on Boolean values 00:02:13
  7. Chapter 7 : Python Functions
    1. Defining a Function in Python 00:02:04
    2. Creating a Function with a Parameter 00:03:49
    3. Another Way to Define a Function 00:02:35
    4. Using a Function in another Function 00:01:49
    5. Combining Conditional Statements and Functions 00:03:07
    6. Creating Functions Containing a Few Arguments 00:01:14
    7. Notable Built-in Functions in Python 00:03:56
  8. Chapter 8 : Python Sequences
    1. Lists 00:04:02
    2. Using Methods 00:03:22
    3. List Slicing 00:04:31
    4. Tuples 00:03:13
    5. Dictionaries 00:04:05
  9. Chapter 9 : Using Iterations in Python
    1. For Loops 00:02:27
    2. While Loops and Incrementing 00:02:26
    3. Create Lists with the range () Function 00:02:23
    4. Use Conditional Statements and Loops Together 00:03:06
    5. All in – Conditional Statements, Functions, and Loops 00:02:27
    6. Iterating over Dictionaries 00:03:08
  10. Chapter 10 : Advanced Python tools
    1. Object Oriented Programming 00:05:00
    2. Modules and Packages 00:01:06
    3. The Standard Library 00:02:47
    4. Importing Modules 00:04:10
    5. Must-have packages for Finance and Data Science 00:04:54
    6. Working with arrays 00:06:02
    7. Generating Random Numbers 00:02:52
    8. A Note on Using Financial Data in Python 00:02:42
    9. Sources of Financial Data 00:06:35
    10. Accessing the Notebook Files 00:01:55
    11. Importing and Organizing Data in Python – part I 00:03:46
    12. Importing and Organizing Data in Python – part II.A 00:06:21
    13. Importing and Organizing Data in Python – part II.B 00:03:47
    14. Importing and Organizing Data in Python – part III 00:04:19
    15. Changing the Index of Your Time-Series Data 00:03:17
    16. Restarting the Jupyter Kernel 00:02:17
  11. Chapter 11 : PART II FINANCE: Calculating and Comparing Rates of Return in Python
    1. Considering both risk and return 00:02:33
    2. What are we going to see next 00:02:35
    3. Calculating a security's rate of return 00:05:32
    4. Calculating a Security’s Rate of Return in Python – Simple Returns – Part I 00:05:24
    5. Calculating a Security’s Rate of Return in Python – Simple Returns – Part II 00:03:29
    6. Calculating a Security’s Return in Python – Logarithmic Returns 00:03:40
    7. What is a portfolio of securities and how to calculate its rate of return 00:02:39
    8. Calculating the Rate of Return of a Portfolio of Securities 00:08:35
    9. Popular stock indices that can help us understand financial markets 00:03:31
    10. Calculating the Rate of Return of Indices 00:05:03
  12. Chapter 12 : PART II Finance: Measuring Investment Risk
    1. How do we measure a security's risk 00:06:06
    2. Calculating a Security’s Risk in Python 00:05:56
    3. The benefits of portfolio diversification 00:03:28
    4. Calculating the covariance between securities 00:03:35
    5. Measuring the correlation between stocks 00:03:59
    6. Calculating Covariance and Correlation 00:05:00
    7. Considering the risk of multiple securities in a portfolio 00:03:20
    8. Calculating Portfolio Risk 00:02:39
    9. Understanding Systematic vs. Idiosyncratic risk 00:02:59
    10. Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio 00:04:28
  13. Chapter 13 : PART II Finance - Using Regressions for Financial Analysis
    1. The fundamentals of simple regression analysis 00:03:55
    2. Running a Regression in Python 00:06:35
    3. Are all regressions created equal? Learning how to distinguish good regressions 00:04:55
    4. Computing Alpha, Beta, and R Squared in Python 00:06:14
  14. Chapter 14 : PART II Finance - Markowitz Portfolio Optimization
    1. Markowitz Portfolio theory – One of the main pillars of modern Finance 00:06:34
    2. Obtaining the Efficient Frontier in Python – Part I 00:05:36
    3. Obtaining the Efficient Frontier in Python – Part II 00:05:19
    4. Obtaining the Efficient Frontier in Python – Part III 00:02:08
  15. Chapter 15 : Part II Finance - The Capital Asset Pricing Model
    1. The intuition behind the Capital Asset Pricing Model (CAPM) 00:04:45
    2. Understanding and calculating a security's Beta 00:04:15
    3. Calculating the Beta of a Stock 00:03:38
    4. The CAPM formula 00:04:20
    5. Calculating the Expected Return of a Stock (CAPM) 00:02:16
    6. Introducing the Sharpe ratio and the way it can be applied in practice 00:02:21
    7. Obtaining the Sharpe ratio in Python 00:01:23
    8. Measuring alpha and verifying how good (or bad) a portfolio manager is doing 00:04:13
  16. Chapter 16 : Part II Finance: Multivariate regression analysis
    1. Multivariate regression analysis - a valuable tool for finance practitioners 00:05:42
    2. Running a multivariate regression in Python 00:06:21
  17. Chapter 17 : PART II Finance - Monte Carlo simulations as a decision-making tool
    1. The essence of Monte Carlo simulations 00:02:32
    2. Monte Carlo applied in a Corporate Finance context 00:02:31
    3. Monte Carlo: Predicting Gross Profit – Part I 00:06:03
    4. Monte Carlo: Predicting Gross Profit – Part II 00:02:57
    5. Forecasting Stock Prices with a Monte Carlo Simulation 00:04:28
    6. Monte Carlo: Forecasting Stock Prices - Part I 00:03:39
    7. Monte Carlo: Forecasting Stock Prices - Part II 00:04:39
    8. Monte Carlo: Forecasting Stock Prices - Part III 00:04:18
    9. An Introduction to Derivative Contracts 00:06:33
    10. The Black Scholes Formula for Option Pricing 00:04:52
    11. Monte Carlo: Black-Scholes-Merton 00:06:01
    12. Monte Carlo: Euler Discretization - Part I 00:06:22
    13. Monte Carlo: Euler Discretization - Part II 00:01:58