Python for Finance: Investment Fundamentals and Data Analytics

Welcome! Course Introduction
1 What does the Course Cover

Introduction to programming with Python
2 Programming Explained in 5 Minutes
3 Why Python
4 Why Jupyter
5 Installing Python and Jupyter
6 Jupyter’s Interface – the Dashboard
7 Jupyter’s Interface – Prerequisites for Coding
8 Python 2 vs Python 3 – What’s the Difference

Python Variables and Data Types
9 Variables
10 Numbers and Boolean Values
11 Strings

Basic Python Syntax
12 Arithmetic Operators
13 The Double Equality Sign
14 Reassign Values
15 Add Comments
16 Line Continuation
17 Indexing Elements
18 Structure Your Code with Indentation

Python Operators Continued
19 Comparison Operators
20 Logical and Identity Operators

Conditional Statements
21 Introduction to the IF statement
22 Add an ELSE statement
23 Else if, for Brief – ELIF
24 A Note on Boolean values

Python Functions
25 Defining a Function in Python
26 Creating a Function with a Parameter
27 Another Way to Define a Function
28 Using a Function in another Function
29 Combining Conditional Statements and Functions
30 Creating Functions Containing a Few Arguments
31 Notable Built-in Functions in Python

Python Sequences
32 Lists
33 Using Methods
34 List Slicing
35 Tuples
36 Dictionaries

Using Iterations in Python
37 For Loops
38 While Loops and Incrementing
39 Create Lists with the range () Function
40 Use Conditional Statements and Loops Together
41 All in – Conditional Statements, Functions, and Loops
42 Iterating over Dictionaries

Advanced Python tools
43 Object Oriented Programming
44 Modules and Packages
45 The Standard Library
46 Importing Modules
47 Must-have packages for Finance and Data Science
48 Working with arrays
49 Generating Random Numbers
50 A Note on Using Financial Data in Python
51 Sources of Financial Data
52 Accessing the Notebook Files
53 Importing and Organizing Data in Python – part I
54 Importing and Organizing Data in Python – part II.A
55 Importing and Organizing Data in Python – part II.B
56 Importing and Organizing Data in Python – part III
57 Changing the Index of Your Time-Series Data
58 Restarting the Jupyter Kernel

PART II FINANCE – Calculating and Comparing Rates of Return in Python
59 Considering both risk and return
60 What are we going to see next
61 Calculating a security’s rate of return
62 Calculating a Security’s Rate of Return in Python – Simple Returns – Part I
63 Calculating a Security’s Rate of Return in Python – Simple Returns – Part II
64 Calculating a Security’s Return in Python – Logarithmic Returns
65 What is a portfolio of securities and how to calculate its rate of return Calculating the Rate of Return of a Portfolio of Secur
66 Calculating the Rate of Return of a Portfolio of Securities
67 Popular stock indices that can help us understand financial markets
68 Calculating the Rate of Return of Indices

PART II Finance – Measuring Investment Risk
69 How do we measure a security’s risk
70 Calculating a Security’s Risk in Python
71 The benefits of portfolio diversification
72 Calculating the covariance between securities
73 Measuring the correlation between stocks
74 Calculating Covariance and Correlation
75 Considering the risk of multiple securities in a portfolio
76 Calculating Portfolio Risk
77 Understanding Systematic vs. Idiosyncratic risk
78 Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio

PART II Finance – Using Regressions for Financial Analysis
79 The fundamentals of simple regression analysis
80 Running a Regression in Python
81 Are all regressions created equal Learning how to distinguish good regressions
82 Computing Alpha, Beta, and R Squared in Python

PART II Finance – Markowitz Portfolio Optimization
83 Markowitz Portfolio theory – One of the main pillars of modern Finance
84 Obtaining the Efficient Frontier in Python – Part I
85 Obtaining the Efficient Frontier in Python – Part II
86 Obtaining the Efficient Frontier in Python – Part III

Part II Finance – The Capital Asset Pricing Model
87 The intuition behind the Capital Asset Pricing Model (CAPM)
88 Understanding and calculating a security’s Beta
89 Calculating the Beta of a Stock
90 The CAPM formula
91 Calculating the Expected Return of a Stock (CAPM)
92 Introducing the Sharpe ratio and the way it can be applied in practice
93 Obtaining the Sharpe ratio in Python
94 Measuring alpha and verifying how good (or bad) a portfolio manager is doing

Part II Finance – Multivariate regression analysis
95 Multivariate regression analysis – a valuable tool for finance practitioners
96 Running a multivariate regression in Python

PART II Finance – Monte Carlo simulations as a decision-making tool
97 The essence of Monte Carlo simulations
98 Monte Carlo applied in a Corporate Finance context
99 Monte Carlo – Predicting Gross Profit – Part I
100 Monte Carlo – Predicting Gross Profit – Part II
101 Forecasting Stock Prices with a Monte Carlo Simulation
102 Monte Carlo – Forecasting Stock Prices – Part I
103 Monte Carlo – Forecasting Stock Prices – Part II
104 Monte Carlo – Forecasting Stock Prices – Part III
105 An Introduction to Derivative Contracts
106 The Black Scholes Formula for Option Pricing
107 Monte Carlo – Black-Scholes-Merton
108 Monte Carlo – Euler Discretization – Part I
109 Monte Carlo – Euler Discretization – Part II