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Python for Finance: Financial Analysis for Investing
Introduction
One Question (2:18)
Welcome to this course (7:18)
Setup
Introduction (0:30)
Download Anaconda (Includes Python and Jupyter notebook) (1:32)
Resources and Environment (4:24)
Jupyter Notebook guide
Introduction (0:55)
Jupyter Notebook Cheat Sheet
Jupyter Notebook: The Dashboard (3:23)
Jupyter Notebook: Run and restart a Notebook (3:04)
Jupyter Notebook: Coppy and Reorganize Code (2:01)
Jupyter Notebook: Comments and Markdowns (2:21)
Jupyter Notebook: Tab + Tab + Shift & Tab (6:14)
What did we learn? (0:54)
Python Crash Course
Introduction (1:07)
Variables and Types (11:43)
Print statement (2:53)
Boolean expressions (6:16)
If-statements (5:04)
Python Lists (5:19)
For-loops (4:38)
While-loops (2:32)
Python Dictionaries (dict) (4:05)
Other data types (3:45)
Python Functions (4:20)
Lambda Functions (7:41)
Exercises (5:07)
Solutions (12:49)
New to Python? We have all been there. (3:15)
What did we learn? (1:13)
Lemonade Stand
Introduction (1:19)
Intrinsic Value (3:35)
Introduction to the Lemonade Stand (5:28)
The Lemonade Stand - the easy to understand example (5:01)
Jupyter Notebook: The Lemonade Stand (15:58)
Shares (4:35)
Shares a story - Understand what they really are (6:08)
Jupyter Notebook: Shares (13:05)
Dividend (4:16)
Dividend a story - an easy way to understand dividend (5:36)
Jupyter Notebook: Dividend (14:13)
What did we learn? (2:30)
Pandas
Introduction (6:21)
Introduction to Pandas - a small demonstration (11:13)
Series (12:07)
DataFrames - Part I (12:09)
DataFrames - Part II (7:13)
DataFrames - Part III (7:55)
DataFrames - Part IV (7:14)
DataFrames - Part V (7:39)
Read and Write with Pandas - Part I (11:24)
Read and Write with Pandas - Part II (10:56)
Read and Write with Pandas - Part III (10:41)
Merge - Join - Concat - Part I (8:46)
Merge - Join - Concatenate - Part II (5:04)
Transpose and Clean (7:38)
Views (5:47)
Useful methods (8:33)
Apply - An awesome method to master (6:09)
Exercises (5:19)
Solutions (10:42)
What did we learn? (2:07)
Intrinsic Value
Introduction (1:13)
Outcome of section (6:52)
Understand Risk - Part I (4:20)
Understand Risk - Part II (3:08)
Understand Risk - Part III (2:58)
Understand Risk - All put together (2:39)
Evaluate Leadership (9:37)
Debt to Equity ratio - Evaluation (4:21)
Jupyter Notebook: Dept-to-equity ratio (19:34)
Current ratio - Evaluation (3:25)
Jupyter Notebook: Current ratio (10:34)
Stable and predictable (8:06)
Return of Investment (ROI) - Evaluation (5:04)
Jupyter Notebook: Return of Investment (9:39)
Revenue - Evaluation (4:38)
Jupyter Notebook: Revenue (16:48)
Earnings Per Share (EPS) - Evaluation (2:04)
Jupyter Notebook: Earnings Per Share (EPS) (9:01)
Book Value - Evaluation (3:52)
Jupyter Notebook: Book Value (11:43)
Free Cash Flow (FCF) - Evaluation (1:48)
Jupyter Notebook: Free Cash Flow (FCF) (4:49)
Combine all data (3:11)
Jupyter Notebook: Combine all data (10:46)
Calculate a Fair Price (Intrinsic Value) (6:50)
Price-to_earnings (PE) ratio (2:38)
Jupyter Notebook: Price-to-Earnings (PE) ratio (5:32)
Jupyter Notebook: Calculate a Fair Price (Intrinsic Value) (10:27)
Compare it with Current Price (9:38)
What did we learn? (4:29)
Matplotlib (Visualization)
Introduction (0:57)
Overview of Section (5:06)
Jupyter Notebook: Matplotlib basics (8:58)
Jupyter Notebook: Work with Axis (9:04)
Jupyter Notebook: Title and Labels (9:07)
Jupyter Notebook: Matplotlib and pandas (8:04)
Jupyter Notebook: pandas and data structures (11:42)
Jupyter Notebokk: Bar plots (10:00)
Exercises (5:35)
Solutions (14:47)
What did we learn? (1:25)
Visualization and Excel Export of Financial Data
Introduction (1:29)
Matplotlib - Part I (15:56)
Matplotlib - Part II (14:03)
Export to Excel- Part I (11:27)
Export to Excel - Part II (19:36)
Export to Excel - Part III (8:55)
What did we learn? (1:59)
Data Sources
Introduction (0:55)
What will we learn? (3:08)
Pandas Datareader - Remote Data Access (1:37)
Jupyter Notebook: Pandas Datareader - Part I (18:27)
Jupyter Notebook: Pandas Datareader - Part II (9:28)
Yahoo! Finance API - read Financial Statements (2:34)
Jupyter Nobtebook: Yahoo! Finance API (13:11)
Web Scraping (2:43)
Jupyter Notebook: Web Scraping (15:35)
Exercises (3:50)
Solutions (11:45)
What did we learn? (1:08)
Time Series Data
Introduction (3:34)
Rate of Return, Percentage Change, and Normalization (5:55)
Jupyter Notebook: Rate of Return, Percentage Change, and Normalization (10:19)
CAGR (2:02)
Jupyter Notebook: CAGR (8:30)
Jupyter Notebook: Multiple Time Frames (7:46)
Case Study: DOW Theory (15:26)
Jupyter Notebook: Case Study: DOW Theory (14:28)
What did we learn? (1:19)
Technical Indicators
Introduction (2:36)
What is a Technical Indicator and Types of Indicators (7:09)
Indicator: Moving Average (5:02)
Jupyter Notebook: Simple Moving Average (MA) (14:31)
Jupyter Notebook: Exponential Moving Average (EMA) (7:26)
Indicator: MACD (4:25)
Jupyter Notebook: MACD (11:56)
Indicator: Stochastic Oscillator (3:47)
Jupyter Notebook: Stochastic Oscillator (12:46)
Jupyter Notebook: Exporting to Excel (17:27)
Jupyter Notebook: Using our Excel Sheet (10:27)
Exercises (6:03)
Solutions (11:02)
What did we learn? (0:50)
NumPy
Introduction (5:48)
Jupyter Notebook: Introduction to NumPy (13:17)
Jupyter Notebook: Index, Slicing, and Views (10:35)
Jupyter Notebook: DataFrames and Series with NumPy (13:51)
Jupyter Notebook: Vectorization with NumPy (10:46)
Jupyter Notebook: Matplotlib and NumPy (9:23)
Jupyter Notebook: Dot product and Transpose (11:36)
Exercises (6:31)
Solutions (11:08)
What did we learn? (2:13)
Correlation and Linear Regression
Introduction (1:30)
Adjusted Close (2:20)
Volatility of a Stock (5:13)
Jupyter Notebook: Volatility Calculations (20:00)
Correlation Between Securities (2:11)
Jupyter Notebook: Correlation Calculations (7:54)
Linear Regression (3:29)
Jupyter Notebook: Linear Regression (14:13)
Beta Calculations (2:05)
Jupyter Notebook: Beta Calculations (8:58)
CAPM (4:14)
Jupyter Notebook: CAPM Calculations (8:00)
Exercises (4:00)
Solutions (8:32)
What did we learn? (1:47)
Working with Portfolios and Monte Carlo Simulations
Introduction (1:28)
Portfolios (1:21)
Jupyter Notebook: Portfolio (10:40)
Sharpe Ratio (2:38)
Jupyter Notebook: Sharpe Ratio Calculations (11:05)
Monte Carlo Simulations (3:45)
Jupyter Notebook: Monte Carlo Simulations - Introduction (13:45)
Jupyter Notebook: Portfolios and Monte Carlo Simulations (13:58)
Jupyter Notebook: The Efficient Frontier (4:49)
Exercises (4:35)
Solutions (13:33)
What did we learn? (1:28)
Finish Line
Introduction (0:54)
3 Books to Read (11:40)
Goodbye (2:01)
Variables and Types
In this lecture we will learn about.
Variables in Python.
The types of variables.
Complete and Continue
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