Investment Analysis Portfolio Management with Python

Financial Analysis Done Right – Rigorously Analyse Investments & Manage Portfolios using Python for Finance / Investing

4.2/5

Investment Analysis Portfolio Management with Python

What you’ll learn

Calculate stock returns manually as well as on Python, using real world data obtained from free sources.
Extensively work with a variety of Python libraries including Pandas, NumPy, SciPy, Matplotlib, to name a few.
Understand why the math works, and what the equations mean – even if your math is weak and if math freaks you out.
Witness the power of diversification and how the risk of your portfolio can be lower than the individual assets that make up the portfolio!
Estimate the Expected Returns of Stocks using the Mean Method, State Contingent Weighted Probabilities, as well as Asset Pricing Models.
Calculate the total risk, market risk, and firm specific risk of stocks from scratch, and explore how the different risks interact.
Measure your investment portfolio’s performance by calculating portfolio returns and risks.
Create custom functions to automate your Investment Analysis & Portfolio Management techniques, leveraging the power of Python.
Explore computations from scratch, so you understand how Python works behind the scenes.

Requirements

Coding knowledge is REQUIRED. You don’t need to be an ‘expert’ in Python, but you DO need to know how to code.
At a minimum, we assume you know what lists, dictionaries, and tuples are; and you know the difference between strings, integers, and floats.
This is a Finance course which uses Python. It is NOT a Python course about Finance.
No prior knowledge of Finance is required nor assumed.
It’s okay if math freaks you out. Seriously. Every single equation is explained one variable at a time. We rip it apart to its core, and show you how simple it really is.
Knowledge of basic statistical analysis is useful but NOT essential.
You’ll need a calculator, pen and paper (seriously), and your development environment (e.g. Jupyter Notebooks, Text Editors)
We work with Jupyter Notebooks in the course, but .py versions of all Python code is available for download.

Overview

Section 1: Before You Start…

Lecture 1 Welcome to the Course. Here’s What You’re Going To Master.

Lecture 2 Disclaimer

Lecture 4 Course FAQs

Lecture 5 Important: Course Pointers

Lecture 6 Accessing Financial Data

Section 2: Understanding Price, Risk, and Return Relationships & Calculating Returns

Lecture 7 Price, Risk, and Return – Definitions & Relationships

Lecture 8 What is Shorting?

Lecture 9 Calculating Stock Returns

Lecture 10 Calculating Stock Returns II (Applied)

Lecture 11 Variable Notations & Descriptions Cheat Sheet

Section 3: Estimating Expected Returns

Lecture 13 Expected Returns using Average (Mean) Method

Lecture 14 Expected Returns using Average (Mean) Method II – Creating a Function on Python

Lecture 15 Expected Returns using State Contingent Weighted Probabilities

Lecture 16 Expected Returns using Asset Pricing Models I

Lecture 17 Expected Returns using Asset Pricing Models I (Applied)

Lecture 18 Expected Returns using Asset Pricing Models II

Section 4: Understanding and Measuring Risk & Relationships

Lecture 20 Estimating The Total Risk of a Stock I

Lecture 21 Estimating The Total Risk of a Stock II – Applied

Lecture 22 Estimating The Market Risk of a Stock I

Lecture 23 Estimating The Market Risk of a Stock II – Applied

Lecture 24 Estimating Firm Specific Risk

Section 5: Measuring Portfolio Returns and Risk

Lecture 25 Estimating Portfolio Returns

Lecture 26 Estimating Portfolio Risk I (2 Assets)

Lecture 27 Estimating Portfolio Risk II (Multiple Assets)

Lecture 28 Estimating Portfolio Risk II (Multiple Assets) – Applied

Section 6: Mastery Check

Lecture 30 Take a breather!

Lecture 31 Test Guidelines [READ BEFORE YOU START THE TEST]

Section 7: Exploring The Effects of Diversification & Optimisation

Lecture 33 Reducing Portfolio Risk by Diversification

Lecture 34 Optimal Diversification – Number of Securities to Hold

Lecture 35 Optimising Weights To Achieve A Target Return I

Lecture 36 Optimising Weights To Achieve A Target Return II – Applied

Lecture 37 Minimising Portfolio Risk – 2 Assets

Lecture 38 Minimising Portfolio Risk – Multiple Assets, Applied

Section 8: Decomposing Diversification – Investigating Why It Works

Lecture 40 A Bit Puzzling?

Lecture 41 Correlation of Securities

Lecture 42 Estimating Correlation – Applied

Lecture 43 Correlation and Risk

Lecture 44 Correlation, Risk, and Returns

Lecture 45 Solving the “Puzzles”

Section 9: BONUS: Continue Your Journey On Mastering Finance

Lecture 46 What would you like to learn next?

Lecture 47 Bonus: Explore Our Other Courses

Ivy League / Russell Group University students looking to increase their competitive advantage and enhance their skills.,Finance Managers keen on applying conceptual techniques including portfolio design using Python.,Investors wanting to work with techniques that are rigorously grounded in academic and practitioner literature.,Analysts, and aspiring Investment Bankers wanting to gain a solid foundation in investment analysis.,Anyone who wants to learn investment analysis and portfolio management with Python!

Course Information:

Udemy | English | 9h 6m | 3.58 GB
Created by: Fervent #LearnWithDistinction

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