Python For Finance: Investment Fundamentals & Data Analytics

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

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

Requirements

You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course

All software and data used in the course is free

Description

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.
  An exciting journey from Beginner to Pro.   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 part 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 pricingEverything is included! All these topics are first explained in theory and then applied in practice using Python. This is the best way to reinforce what you have learned.    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 lessons. 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 very soon. Our goal is to create high-end materials that are fun, exciting, career-enhancing, and rewarding.     What makes this training different from the rest of the Programming and Finance courses out there?   This course will teach you how to code in Python and apply these skills in the world of Finance. It is both a Programming and a Finance course.High-quality production – HD video and animations (this isn’t a collection of boring lectures!)Knowledgeable instructors. Martin is a quant geek fascinated by the world of Data Science, and Ned is a finance practitioner with several years of experience who loves explaining Finance topics in real life and on Udemy.Complete training – we will cover all the major topics you need to understand to start coding in Python and solving the financial topics introduced in this course (and they are many!)Extensive Case Studies that will help you reinforce everything you’ve learned.Course Challenge: Solve our exercises and make this course an interactive experience.Excellent support: If you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day.Dynamic: We don’t want to waste your time! The instructors set a very good pace throughout the whole course.Please don’t forget that the course comes with Udemy’s 30-day unconditional, money-back-in-full guarantee. And why not give such a guarantee, when we are convinced the course will provide a ton of value for you?Click 'Buy now' to start your learning journey today. We will be happy to see you inside the course.

Overview

Section 1: Welcome! Course Introduction

Lecture 1 What Does the Course Cover?

Lecture 2 Download Useful Resources - Exercises and Solutions

Section 2: Introduction to programming with Python

Lecture 3 Programming Explained in 5 Minutes

Lecture 4 Why Python?

Lecture 5 Why Jupyter?

Lecture 6 Installing Python and Jupyter

Lecture 7 Jupyter’s Interface – the Dashboard

Lecture 8 Jupyter’s Interface – Prerequisites for Coding

Lecture 9 Python 2 vs Python 3: What's the Difference?

Section 3: Python Variables and Data Types

Lecture 10 Variables

Lecture 11 Numbers and Boolean Values

Lecture 12 Strings

Section 4: Basic Python Syntax

Lecture 13 Arithmetic Operators

Lecture 14 The Double Equality Sign

Lecture 15 Reassign Values

Lecture 16 Add Comments

Lecture 17 Line Continuation

Lecture 18 Indexing Elements

Lecture 19 Structure Your Code with Indentation

Section 5: Python Operators Continued

Lecture 20 Comparison Operators

Lecture 21 Logical and Identity Operators

Section 6: Conditional Statements

Lecture 22 Introduction to the IF statement

Lecture 23 Add an ELSE statement

Lecture 24 Else if, for Brief – ELIF

Lecture 25 A Note on Boolean Values

Section 7: Python Functions

Lecture 26 Defining a Function in Python

Lecture 27 Creating a Function with a Parameter

Lecture 28 Another Way to Define a Function

Lecture 29 Using a Function in another Function

Lecture 30 Combining Conditional Statements and Functions

Lecture 31 Creating Functions Containing a Few Arguments

Lecture 32 Notable Built-in Functions in Python

Section 8: Python Sequences

Lecture 33 Lists

Lecture 34 Using Methods

Lecture 35 List Slicing

Lecture 36 Tuples

Lecture 37 Dictionaries

Section 9: Using Iterations in Python

Lecture 38 For Loops

Lecture 39 While Loops and Incrementing

Lecture 40 Create Lists with the range() Function

Lecture 41 Use Conditional Statements and Loops Together

Lecture 42 All In – Conditional Statements, Functions, and Loops

Lecture 43 Iterating over Dictionaries

Section 10: Advanced Python tools

Lecture 44 Object Oriented Programming

Lecture 45 Modules and Packages

Lecture 46 The Standard Library

Lecture 47 Importing Modules

Lecture 48 Must-have packages for Finance and Data Science

Lecture 49 Working with arrays

Lecture 50 Generating Random Numbers

Lecture 51 A Note on Using Financial Data in Python

Lecture 52 Sources of Financial Data

Lecture 53 Accessing the Notebook Files

Lecture 54 Importing and Organizing Data in Python – part I

Lecture 55 Importing and Organizing Data in Python – part II.A

Lecture 56 Importing and Organizing Data in Python – part II.B

Lecture 57 Importing and Organizing Data in Python – part III

Lecture 58 Changing the Index of Your Time-Series Data

Lecture 59 Restarting the Jupyter Kernel

Section 11: PART II FINANCE: Calculating and Comparing Rates of Return in Python

Lecture 60 Considering both risk and return

Lecture 61 What are we going to see next?

Lecture 62 Calculating a security's rate of return

Lecture 63 Calculating a Security’s Rate of Return in Python – Simple Returns – Part I

Lecture 64 Calculating a Security’s Rate of Return in Python – Simple Returns – Part II

Lecture 65 Calculating a Security’s Return in Python – Logarithmic Returns

Lecture 66 What is a portfolio of securities and how to calculate its rate of return

Lecture 67 Calculating a Portfolio of Securities' Rate of Return

Lecture 68 Popular stock indices that can help us understand financial markets

Lecture 69 Calculating the Indices' Rate of Return

Section 12: PART II Finance: Measuring Investment Risk

Lecture 70 How do we measure a security's risk?

Lecture 71 Calculating a Security’s Risk in Python

Lecture 72 The benefits of portfolio diversification

Lecture 73 Calculating the covariance between securities

Lecture 74 Measuring the correlation between stocks

Lecture 75 Calculating Covariance and Correlation

Lecture 76 Considering the risk of multiple securities in a portfolio

Lecture 77 Calculating Portfolio Risk

Lecture 78 Understanding Systematic vs. Idiosyncratic risk

Lecture 79 Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio

Section 13: PART II Finance - Using Regressions for Financial Analysis

Lecture 80 The fundamentals of simple regression analysis

Lecture 81 Running a Regression in Python

Lecture 82 Are all regressions created equal? Learning how to distinguish good regressions

Lecture 83 Computing Alpha, Beta, and R Squared in Python

Section 14: PART II Finance - Markowitz Portfolio Optimization

Lecture 84 Markowitz Portfolio Theory - One of the main pillars of modern Finance

Lecture 85 Obtaining the Efficient Frontier in Python – Part I

Lecture 86 Obtaining the Efficient Frontier in Python – Part II

Lecture 87 Obtaining the Efficient Frontier in Python – Part III

Section 15: Part II Finance - The Capital Asset Pricing Model

Lecture 88 The intuition behind the Capital Asset Pricing Model (CAPM)

Lecture 89 Understanding and calculating a security's Beta

Lecture 90 Calculating the Beta of a Stock

Lecture 91 The CAPM formula

Lecture 92 Calculating the Expected Return of a Stock (CAPM)

Lecture 93 Introducing the Sharpe ratio and how to put it into practice

Lecture 94 Obtaining the Sharpe ratio in Python

Lecture 95 Measuring alpha and verifying how good (or bad) a portfolio manager is doing

Section 16: Part II Finance: Multivariate regression analysis

Lecture 96 Multivariate regression analysis - a valuable tool for finance practitioners

Lecture 97 Running a multivariate regression in Python

Section 17: PART II Finance - Monte Carlo simulations as a decision-making tool

Lecture 98 The essence of Monte Carlo simulations

Lecture 99 Monte Carlo applied in a Corporate Finance context

Lecture 100 Monte Carlo: Predicting Gross Profit – Part I

Lecture 101 Monte Carlo: Predicting Gross Profit – Part II

Lecture 102 Forecasting Stock Prices with a Monte Carlo Simulation

Lecture 103 Monte Carlo: Forecasting Stock Prices - Part I

Lecture 104 Monte Carlo: Forecasting Stock Prices - Part II

Lecture 105 Monte Carlo: Forecasting Stock Prices - Part III

Lecture 106 An Introduction to Derivative Contracts

Lecture 107 The Black Scholes Formula for Option Pricing

Lecture 108 Monte Carlo: Black-Scholes-Merton

Lecture 109 Monte Carlo: Euler Discretization - Part I

Lecture 110 Monte Carlo: Euler Discretization - Part II

Section 18: APPENDIX - pandas Fundamentals

Lecture 111 pandas Series - Introduction

Lecture 112 pandas - Working with Methods - Part I

Lecture 113 pandas - Working with Methods - Part II

Lecture 114 pandas - Using Parameters and Arguments

Lecture 115 pandas Series - .unique() and .nunique()

Lecture 116 pandas Series - .sort_values()

Lecture 117 pandas DataFrames - Introduction - Part I

Lecture 118 pandas DataFrames - Introduction - Part II

Lecture 119 pandas DataFrames - Common Attributes

Lecture 120 pandas DataFrames - Data Selection

Lecture 121 pandas DataFrames - Data Selection with .iloc[]

Lecture 122 pandas DataFrames - Data Selection with .loc[]

Section 19: APPENDIX - Technical Analysis

Lecture 123 Technical Analysis - Principles, Applications, Assumptions

Lecture 124 Charts Used in Technical Analysis

Lecture 125 Other Tools Used in Technical Analysis

Lecture 126 Trend, Support and Resistance Lines

Lecture 127 Common Chart Patterns

Lecture 128 Price Indicators

Lecture 129 Momentum Oscillators

Lecture 130 Non-price Based Indicators

Lecture 131 Technical Analysis - Cycles

Lecture 132 Intermarket Analysis

Section 20: BONUS LECTURE

Lecture 133 Bonus Lecture: Next Steps

Aspiring data scientists,Programming beginners,People interested in finance and investments,Programmers who want to specialize in finance,Everyone who wants to learn how to code and apply their skills in practice,Finance graduates and professionals who need to better apply their knowledge in Python