Python for Finance: Investment Fundamentals & Data Analytics

Python for Finance: Investment Fundamentals & Data Analytics
Python for Finance: Investment Fundamentals & Data Analytics
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7 Hours | 994 MB

Learn Python Programming and Conduct Real-World Financial Analysis in Python – Complete Python Training

Do you want to learn how to use Python in a working environment?

Are you a young professional interested in a career in Data Science?

Would you like to explore how Python can be applied in the world of Finance and solve portfolio optimization problems?

If so, then this is the right course for you!

We are proud to present Python for Finance: Investment Fundamentals and Data Analytics – one of the most interesting and complete courses we have created so far. It took our team slightly over four months to create this course, but now, it is ready and waiting for you.

An exciting journey from A-Z.

If you are a complete beginner and you know nothing about coding, don’t worry! We start from the very basics. 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.

Finance Fundamentals.

And it gets even better! The Finance block of this course will teach you in-demand real-world skills employers are looking for. To be a high-paid programmer, you will have to specialize in a particular area of interest. In this course, we will focus on Finance, covering many tools and techniques used by finance professionals daily:

  • Rate of return of stocks
  • Risk of stocks
  • Rate of return of stock portfolios
  • Risk of stock portfolios
  • Correlation between stocks
  • Covariance
  • Diversifiable and non-diversifiable risk
  • Regression analysis
  • Alpha and Beta coefficients
  • Measuring a regression’s explanatory power with R^2
  • Markowitz Efficient frontier calculation
  • Capital asset pricing model
  • Sharpe ratio
  • Multivariate regression analysis
  • Monte Carlo simulations
  • Using Monte Carlo in a Corporate Finance context
  • Derivatives and type of derivatives
  • Applying the Black Scholes formula
  • Using Monte Carlo for options pricing
  • Using Monte Carlo for stock pricing

Everything is included! All these topics are first explained in theory and then applied in practice using Python.

Is there a better way to reinforce what you have learned in the first part of the course?

This course is great, even if you are an experienced programmer, as we will teach you a great deal about the finance theory and mechanics you will need if you start working in a finance context.

Teaching is our passion.

Everything we teach is explained in the best way possible. Plain and clear English, relevant examples and time-efficient videos. Don’t forget to check some of our sample videos to see how easy they are to understand.

If you have questions, contact us! We enjoy communicating with our students and take pride in responding within the 1 business day. Our goal is to create high-end materials that are fun, exciting, career-enhancing, and rewarding.

What you’ll learn

  • Learn how to code in Python
  • Take your career to the next level
  • Work with Python’s conditional statements, functions, sequences, and loops
  • Work with scientific packages, like NumPy
  • Understand how to use the data analysis toolkit, Pandas
  • Plot graphs with Matplotlib
  • Use Python to solve real-world tasks
  • Get a job as a data scientist with Python
  • Acquire solid financial acumen
  • Carry out in-depth investment analysis
  • Build investment portfolios
  • Calculate risk and return of individual securities
  • Calculate risk and return of investment portfolios
  • Apply best practices when working with financial data
  • Use univariate and multivariate regression analysis
  • Understand the Capital Asset Pricing Model
  • Compare securities in terms of their Sharpe ratio
  • Perform Monte Carlo simulations
  • Learn how to price options by applying the Black Scholes formula
  • Be comfortable applying for a developer job in a financial institution
Table of Contents

Welcome! Course Introduction
1 What Does the Course Cover?
2 Download Useful Resources – Exercises and Solutions

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

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

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

Python Operators Continued
20 Comparison Operators
21 Logical and Identity Operators

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

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

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

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

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

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

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

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

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

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

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

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

111 Bonus Lecture: Next Steps