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