Data Driven Investing (with Python) | Financial Data Science

Data Driven Investing (with Python) | Financial Data Science

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 12h 44m | 4.27 GB

Become a Data Driven Investor. Rigorously Test & Statistically Validate Investments From Scratch | Quantitative Finance

Become a Data Driven Investor. Take the guesswork out of your investing forever. Leverage the power of Financial Data Science, Financial Analysis, Python, and Quantitative Finance to make robust investment decisions (and generate Alpha).

Discover how to use rigorous statistical techniques on Python to guide your investment decisions (even if you don’t know statistics or your math is weak).

Say hello to the most comprehensive Data Driven Investing course on the internet

What you’ll learn

  • Remove the “guesswork” from your investing forever by learning how to statistically test and validate your investment ideas rigorously on Python
  • Discover and master the systematic and scientific Data Driven Investing process that will transform the way you analyse investments forever
  • Apply everything you learn using rich, large real world data (without compromising on the mathematical and theoretical integrity of concepts)
  • Learn how to leverage incredibly powerful relationships and rigorous Financial Data Science techniques on Python to generate Alpha (seriously)
  • Understand why the math works (and why equations work the way they do) – even if your math is weak and if math freaks you out.
  • Explore evergreen concepts like Expected Returns, Asset Pricing Models, and Portfolio Construction in unique Financial Data Science settings, leveraging Pandas
  • Learn and apply powerful Quantitative Finance techniques including “sorts” to create and design portfolios, regressions to “test for alpha”, and much more
  • Discover how to quantify risk and returns of individual stocks and investment portfolios, both manually as well as on Python working with real-world data
Table of Contents

Before You Start
1 Welcome To The Course. Here’s What You’ll Master…
2 Disclaimer
3 IMPORTANT Pre-Requisites Please read before enrolling.
4 Course Pointers
5 Course FAQs

PART I INVESTMENT ANALYSIS FUNDAMENTALS
6 In This Part

Price, Risk, and Return – Definitions, Relationships, and Measurement
7 Price, Risk, and Return – Definitions & Relationships
8 What is Shorting
9 Calculating Stock Returns
10 Calculating Stock Returns II (Applied)
11 Calculating Stock Returns II (Applied) [Assignment]
12 Estimating Portfolio Returns

Estimating Expected Returns of Stocks Financial Securities
13 Expected Returns using Average (Mean) Method
14 Expected Returns using Average (Mean) Method II – Creating a Function on Python
15 Expected Returns using Average (Mean) Method [Assignment]
16 Expected Returns using State Contingent Weighted Probabilities
17 Expected Returns using State Contingent Weighted Probabilities [Assignment]
18 Expected Returns using Asset Pricing Models I
19 Expected Returns using Asset Pricing Models I (Applied)
20 Expected Returns using Asset Pricing Models II

Estimating Total Stock Risk and Portfolio Risk
21 Estimating The Total Risk of a Stock I
22 Estimating The Total Risk of a Stock II – Applied
23 Estimating The Total Risk of a Stock II – Applied [Assignment]
24 Estimating Portfolio Risk I (2 Assets)
25 Estimating Portfolio Risk II (Multiple Assets)
26 Estimating Portfolio Risk II (Multiple Assets) – Applied
27 Estimating Portfolio Risk [Assignment]

Mastery Check & Setup for the Next Part
28 Take a breather!
29 Test Guidelines [READ BEFORE YOU START THE TEST]
30 Test Towards Mastery

PART II DATA DRIVEN INVESTING FINANCIAL DATA SCIENCE
31 In This Part

Data Driven Investing and Hypothesis Design
32 Introduction to Data Driven Investing
33 Developing an Investment Idea Thesis
34 Developing an Investment Idea Thesis [Assignment]
35 Creating a Testable Hypothesis
36 Creating a Testable Hypothesis [Assignment]

Data Collection, Cleaning, & Exploratory Analysis
37 Sourcing Relevant Data
38 Sourcing Relevant Data [Assignment]
39 Extracting Stock Price Data – Generalised Approach
40 Extracting Stock Price Data (Large Sample) [Assignment]
41 Exploring Stock Price Data (Large Sample)
42 Cleaning Returns Data (Large Sample)
43 Exploring Returns Data
44 Extracting, Cleaning, & Exploring ESG Data
45 Cleaning and Exploring Data [Assignment]

Testing & Validating the Hypotheses H1, H2
46 Evaluating the Relationship Between ESG, Returns, Risk
47 Testing the Hypothesis Relationships with ESG (H1 & H2)
48 Testing the Hypothesis Relationships with ESG (H1 & H2) – Applied
49 Updating the Hypothesis Beliefs
50 Testing and Validating Hypotheses I [Assignment]

ESG Investment Portfolio Design & Construction
51 Estimating ESG Portfolio Returns
52 Estimating ESG Portfolio Returns – Applied
53 Estimating Factor Portfolio Returns – Applied [Assignment]
54 Exploring ESG Portfolio Performance

Testing & Validating the Hypotheses H3, H4
55 Testing the Hypothesis – Lower vs. Higher ESG Portfolio Returns (H3)
56 Testing the Hypothesis – Earning Alpha (H4)
57 Testing the Hypothesis – Earning Alpha (H4) – Applied
58 Testing and Validating Hypotheses II [Assignment]

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