The Financial Analysis in Python
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Video Courses
Introduction to programming with Python
| Lectures | Duration |
|---|---|
| 1. Programming Explained in 5 Minutes | 5m 4s |
| 2. Why Python? | 5m 11s |
| 3. Why Jupyter? | 3m 29s |
| 4. Installing Python and Jupyter | 4m 22s |
| 5. Jupyter's Interface - the Dashboard | 3m 15s |
| 6. Jupyter's Interface - Prerequisites for Coding | 6m 15s |
1. Programming Explained in 5 Minutes
5m 4s
2. Why Python?
5m 11s
3. Why Jupyter?
3m 29s
4. Installing Python and Jupyter
4m 22s
5. Jupyter's Interface - the Dashboard
3m 15s
6. Jupyter's Interface - Prerequisites for Coding
6m 15s
Python Variables and Data Types
| Lectures | Duration |
|---|---|
| 1. Variables | 3m 41s |
| 2. Numbers and Boolean Values | 3m 5s |
| 3. Strings | 5m 43s |
1. Variables
3m 41s
2. Numbers and Boolean Values
3m 5s
3. Strings
5m 43s
Basic Python Syntax
| Lectures | Duration |
|---|---|
| 1. Arithmetic Operators | 3m 23s |
| 2. The Double Equality Sign | 1m 33s |
| 3. Reassign Values | 1m 8s |
| 4. Add Comments | 1m 25s |
| 5. Line Continuation | 50s |
| 6. Indexing Elements | 1m 18s |
| 7. Structure Your Code with Indentation | 1m 45s |
1. Arithmetic Operators
3m 23s
2. The Double Equality Sign
1m 33s
3. Reassign Values
1m 8s
4. Add Comments
1m 25s
5. Line Continuation
50s
6. Indexing Elements
1m 18s
7. Structure Your Code with Indentation
1m 45s
Python Operators Continued
| Lectures | Duration |
|---|---|
| 1. Comparison Operators | 2m 10s |
| 2. Logical and Identity Operators | 5m 36s |
1. Comparison Operators
2m 10s
2. Logical and Identity Operators
5m 36s
Conditional Statements
| Lectures | Duration |
|---|---|
| 1. Introduction to the IF statement | 3m 4s |
| 2. Add an ELSE statement | 2m 39s |
| 3. Else if, for Brief - ELIF | 5m 33s |
| 4. A Note on Boolean values | 2m 13s |
1. Introduction to the IF statement
3m 4s
2. Add an ELSE statement
2m 39s
3. Else if, for Brief - ELIF
5m 33s
4. A Note on Boolean values
2m 13s
Python Functions
| Lectures | Duration |
|---|---|
| 1. Defining a Function in Python | 2m 3s |
| 2. Creating a Function with a Parameter | 3m 49s |
| 3. Another Way to Define a Function | 2m 35s |
| 4. Using a Function in another Function | 1m 49s |
| 5. Creating Functions Containing a Few Arguments | 1m 13s |
| 6. Notable Built-in Functions in Python | 3m 56s |
1. Defining a Function in Python
2m 3s
2. Creating a Function with a Parameter
3m 49s
3. Another Way to Define a Function
2m 35s
4. Using a Function in another Function
1m 49s
5. Creating Functions Containing a Few Arguments
1m 13s
6. Notable Built-in Functions in Python
3m 56s
Python Sequences
| Lectures | Duration |
|---|---|
| 1. Lists | 4m 2s |
| 2. Using Methods | 3m 22s |
| 3. List Slicing | 4m 31s |
| 4. Tuples | 3m 13s |
| 5. Dictionaries | 4m 4s |
1. Lists
4m 2s
2. Using Methods
3m 22s
3. List Slicing
4m 31s
4. Tuples
3m 13s
5. Dictionaries
4m 4s
Using Iterations in Python
| Lectures | Duration |
|---|---|
| 1. For Loops | 2m 26s |
| 2. While Loops and Incrementing | 2m 26s |
| 3. Create Lists with the range() Function | 2m 22s |
| 4. Use Conditional Statements and Loops Together | 3m 5s |
| 5. All In - Conditional Statements, Functions, and Loops | 2m 27s |
| 6. Iterating over Dictionaries | 3m 7s |
1. For Loops
2m 26s
2. While Loops and Incrementing
2m 26s
3. Create Lists with the range() Function
2m 22s
4. Use Conditional Statements and Loops Together
3m 5s
5. All In - Conditional Statements, Functions, and Loops
2m 27s
6. Iterating over Dictionaries
3m 7s
Advanced Python tools
| Lectures | Duration |
|---|---|
| 1. Object Oriented Programming | 5m |
| 2. Modules and Packages | 1m 5s |
| 3. The Standard Library | 2m 47s |
| 4. Importing Modules | 4m 10s |
| 5. Must-have packages for Finance and Data Science | 4m 53s |
| 6. Working with arrays | 6m 2s |
| 7. Generating Random Numbers | 2m 52s |
| 8. Importing and Organizing Data in Python - part I | 3m 44s |
| 9. Importing and Organizing Data in Python - part II | 7m 1s |
| 10. Importing and Organizing Data in Python - part III | 4m 19s |
1. Object Oriented Programming
5m
2. Modules and Packages
1m 5s
3. The Standard Library
2m 47s
4. Importing Modules
4m 10s
5. Must-have packages for Finance and Data Science
4m 53s
6. Working with arrays
6m 2s
7. Generating Random Numbers
2m 52s
8. Importing and Organizing Data in Python - part I
3m 44s
9. Importing and Organizing Data in Python - part II
7m 1s
10. Importing and Organizing Data in Python - part III
4m 19s
PART II FINANCE: Calculating and Comparing Rates of Return in Python
| Lectures | Duration |
|---|---|
| 1. Considering both risk and return | 2m 19s |
| 2. What are we going to see next | 2m 34s |
| 3. Calculating a security's rate of return | 5m 31s |
| 4. Calculating a Security's Rate of Return in Python - Simple Returns - Part I | 5m 23s |
| 5. Calculating a Security's Rate of Return in Python - Simple Returns - Part II | 3m 28s |
| 6. Calculating a Security's Return in Python - Logarithmic Returns | 3m 39s |
| 7. What is a portfolio of securities and how to calculate its rate of return | 2m 39s |
| 8. Calculating the Rate of Return of a Portfolio of Securities | 8m 34s |
| 9. Popular stock indices that can help us understand financial markets | 3m 31s |
| 10. Calculating the Rate of Return of Indices | 5m 3s |
1. Considering both risk and return
2m 19s
2. What are we going to see next
2m 34s
3. Calculating a security's rate of return
5m 31s
4. Calculating a Security's Rate of Return in Python - Simple Returns - Part I
5m 23s
5. Calculating a Security's Rate of Return in Python - Simple Returns - Part II
3m 28s
6. Calculating a Security's Return in Python - Logarithmic Returns
3m 39s
7. What is a portfolio of securities and how to calculate its rate of return
2m 39s
8. Calculating the Rate of Return of a Portfolio of Securities
8m 34s
9. Popular stock indices that can help us understand financial markets
3m 31s
10. Calculating the Rate of Return of Indices
5m 3s
PART II Finance: Measuring Investment Risk
| Lectures | Duration |
|---|---|
| 1. How do we measure a security's risk | 6m 5s |
| 2. Calculating a Security's Risk in Python | 5m 56s |
| 3. The benefits of portfolio diversification | 3m 28s |
| 4. Calculating the covariance between securities | 3m 35s |
| 5. Measuring the correlation between stocks | 3m 59s |
| 6. Calculating Covariance and Correlation | 5m |
| 7. Considering the risk of multiple securities in a portfolio | 3m 19s |
| 8. Calculating Portfolio Risk | 2m 39s |
| 9. Understanding Systematic vs | 2m 58s |
| 10. Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio | 4m 28s |
1. How do we measure a security's risk
6m 5s
2. Calculating a Security's Risk in Python
5m 56s
3. The benefits of portfolio diversification
3m 28s
4. Calculating the covariance between securities
3m 35s
5. Measuring the correlation between stocks
3m 59s
6. Calculating Covariance and Correlation
5m
7. Considering the risk of multiple securities in a portfolio
3m 19s
8. Calculating Portfolio Risk
2m 39s
9. Understanding Systematic vs
2m 58s
10. Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio
4m 28s
PART II Finance - Using Regressions for Financial Analysis
| Lectures | Duration |
|---|---|
| 1. The fundamentals of simple regression analysis | 3m 55s |
| 2. Running a Regression in Python | 6m 35s |
| 3. Are all regressions created equal? Learning how to distinguish good regressions | 4m 55s |
| 4. Computing Alpha, Beta, and R Squared in Python | 6m 14s |
1. The fundamentals of simple regression analysis
3m 55s
2. Running a Regression in Python
6m 35s
3. Are all regressions created equal? Learning how to distinguish good regressions
4m 55s
4. Computing Alpha, Beta, and R Squared in Python
6m 14s
PART II Finance - Markowitz Portfolio Optimization
| Lectures | Duration |
|---|---|
| 1. Markowitz Portfolio Theory - One of the main pillars of modern Finance | 6m 34s |
| 2. Obtaining the Efficient Frontier in Python - Part I | 5m 35s |
| 3. Obtaining the Efficient Frontier in Python - Part II | 5m 18s |
| 4. Obtaining the Efficient Frontier in Python - Part III | 2m 7s |
1. Markowitz Portfolio Theory - One of the main pillars of modern Finance
6m 34s
2. Obtaining the Efficient Frontier in Python - Part I
5m 35s
3. Obtaining the Efficient Frontier in Python - Part II
5m 18s
4. Obtaining the Efficient Frontier in Python - Part III
2m 7s
Part II Finance - The Capital Asset Pricing Model
| Lectures | Duration |
|---|---|
| 1. The intuition behind the Capital Asset Pricing Model (CAPM) | 4m 45s |
| 2. Understanding and calculating a security's Beta | 4m 14s |
| 3. Calculating the Beta of a Stock | 3m 38s |
| 4. The CAPM formula | 4m 20s |
| 5. Calculating the Expected Return of a Stock (CAPM) | 2m 16s |
| 6. Introducing the Sharpe ratio and the way it can be applied in practice | 2m 21s |
| 7. Obtaining the Sharpe ratio in Python | 1m 23s |
| 8. Measuring alpha and verifying how good (or bad) a portfolio manager is doing | 4m 13s |
1. The intuition behind the Capital Asset Pricing Model (CAPM)
4m 45s
2. Understanding and calculating a security's Beta
4m 14s
3. Calculating the Beta of a Stock
3m 38s
4. The CAPM formula
4m 20s
5. Calculating the Expected Return of a Stock (CAPM)
2m 16s
6. Introducing the Sharpe ratio and the way it can be applied in practice
2m 21s
7. Obtaining the Sharpe ratio in Python
1m 23s
8. Measuring alpha and verifying how good (or bad) a portfolio manager is doing
4m 13s
Part II Finance: Multivariate regression analysis
| Lectures | Duration |
|---|---|
| 1. Multivariate regression analysis - a valuable tool for finance practitioners | 5m 42s |
| 2. Running a multivariate regression in Python | 6m 20s |
1. Multivariate regression analysis - a valuable tool for finance practitioners
5m 42s
2. Running a multivariate regression in Python
6m 20s
PART II Finance - Monte Carlo simulations as a decision-making tool
| Lectures | Duration |
|---|---|
| 1. The essence of Monte Carlo simulations | 2m 32s |
| 2. Monte Carlo applied in a Corporate Finance context | 2m 30s |
| 3. Monte Carlo: Predicting Gross Profit - Part I | 6m 3s |
| 4. Monte Carlo: Predicting Gross Profit - Part II | 2m 57s |
| 5. Forecasting Stock Prices with a Monte Carlo Simulation | 4m 27s |
| 6. Monte Carlo: Forecasting Stock Prices - Part I | 3m 39s |
| 7. Monte Carlo: Forecasting Stock Prices - Part II | 4m 38s |
| 8. Monte Carlo: Forecasting Stock Prices - Part III | 4m 17s |
| 9. An Introduction to Derivative Contracts | 6m 32s |
| 10. The Black Scholes Formula for Option Pricing | 4m 51s |
| 11. Monte Carlo: Black-Scholes-Merton | 6m |
| 12. Monte Carlo: Euler Discretization - Part I | 6m 21s |
| 13. Monte Carlo: Euler Discretization - Part II | 2m 9s |
1. The essence of Monte Carlo simulations
2m 32s
2. Monte Carlo applied in a Corporate Finance context
2m 30s
3. Monte Carlo: Predicting Gross Profit - Part I
6m 3s
4. Monte Carlo: Predicting Gross Profit - Part II
2m 57s
5. Forecasting Stock Prices with a Monte Carlo Simulation
4m 27s
6. Monte Carlo: Forecasting Stock Prices - Part I
3m 39s
7. Monte Carlo: Forecasting Stock Prices - Part II
4m 38s
8. Monte Carlo: Forecasting Stock Prices - Part III
4m 17s
9. An Introduction to Derivative Contracts
6m 32s
10. The Black Scholes Formula for Option Pricing
4m 51s
11. Monte Carlo: Black-Scholes-Merton
6m
12. Monte Carlo: Euler Discretization - Part I
6m 21s
13. Monte Carlo: Euler Discretization - Part II
2m 9s