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Courses
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Lessons
379
Enrolled
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Video Overview
From 0 to 1: Bond Theory & Valuation
$49 Value
From 0 to 1: Investments & Portfolio Theory
$49 Value
Case Studies in Macro-Economics
$49 Value
Corporate Finance 101: Equity Valuation
$49 Value
Corporate Finance 101: Financial Statement Analysis & Ratios
$49 Value
Economics: Game Theory, Competition, Elasticity
$49 Value
Case Studies: Facebook, Twitter, LinkedIn, Apple
$49 Value
Learn By Example: Statistics and Data Science in R
$99 Value
Quant Trading Using Machine Learning
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Access
Lifetime
Content
4 hours
Lessons
28
From 0 to 1: Bond Theory & Valuation
Analyze The Value of Bonds & Learn How to Make Smarter Investments
By Loonycorn | in Online Courses
Bonds are relatively safe investments with a steady stream of income, often issued by governments and corporations as a means of borrowing money. They are fairly complex things, however, as there are a variety of attributes that contribute to how a bond is ultimately valued. This course will elucidate the entire concept of bonds, including why they are issued, and what sort of risks they present.
- Access 28 lectures & 4 hours of content 24/7
- Understand the specs of bonds & the relationship between each attribute
- Learn how to compute yield, duration, & price, along w/ fixed & floating interest rates
- Compare two bonds based on ratings & features & decide which is better
- Explore convexity, bond risks, & bond options
Loonycorn is comprised of four individuals—Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh—who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.
Details & Requirements
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Compatibility
- Internet required
Course Outline
- You, This Course and Us
- You, This Course and Us (1:47)
- Credit and Basics of Bond
- What is Credit? (16:41)
- The Origination of Bonds (12:35)
- The Specific features of Bonds (7:15)
- The Yield Curve (14:42)
- Pre-requisite Knowledge
- Bond Yield and an example (16:47)
- Connection between bond Price, Par Value, interest and Yield (11:39)
- Risks relating to bond investment
- Coupon Rates and Reinvestment Risk (9:58)
- Interest Rate Risk and types of bonds (7:00)
- A teaser into Convexity (5:29)
- Various options attached bonds (12:11)
- Series of Examples
- Interest Rates, bond prices, maturity amount (7:35)
- Accrued interest, Clean Price, floating interest (6:18)
- Bond Rating, comparing two bonds (5:53)
- Bond Duration, Value of Call and Put Options (13:05)
- Up one level with the series of examples
- Present Value of Bonds (8:03)
- Negative Relationship between Yield and Price (5:33)
- Modified Duration and Macauley Duration (13:30)
- Duration of Zero Coupon Bonds, Error Margins (11:18)
- Bond Convexity
- Deriving Bond Convexity (7:39)
- Previous examples using convexity (6:43)
- Effective Convexity and Effective Duration (14:41)
- Primer on Net Present Value
- Compound interest and NPV (10:28)
- NPV and Price (7:28)
- A Simple PV Example (4:35)
- Future Value of Present Cash Flow (1:53)
- Semi Annual Compounding (4:12)
- Continuous Compounding (3:43)
- NPV of Stream of Cash Flows (5:00)
Access
Lifetime
Content
4.50 hours
Lessons
21
From 0 to 1: Investments & Portfolio Theory
Explore the Interconnectedness of Risk, Return, & Tradeoffs to Build An Outstanding Investment Portfolio
By Loonycorn | in Online Courses
Investments and Portfolio Theory are a function of the risk, return, and tradeoffs associated between them. In this course, you'll explore how these factors are interconnected, and how to analyze the risk-return tradeoff to make safe and smart investments. By course's end, you'll be ready to construct a diverse, powerful investment portfolio.
- Access 21 lectures & 4.5 hours of content 24/7
- Understand investment, risk, return, tradeoffs, & portfolio theories
- Calculate return & risk of assets & portfolios using the Markowitz Modern Portfolio Theory
- Assess the CAPM Required rate of return & make investment decisions
- Learn how to measure risk & return, & diversify your investment
Loonycorn is comprised of four individuals—Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh—who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.
Details & Requirements
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Compatibility
- Internet required
Course Outline
- You, This Course and Us
- You, This Course and Us (1:52)
- Why to Invest? Where to Invest?
- Investment and factors deciding return on investments (12:21)
- Investment assets: Term Deposits (12:38)
- Investment assets: Bonds (10:31)
- Investment assets: Stocks (13:34)
- The Markowitz Modern Portfolio Theory
- The Markowitz Modern Portfolio Theory (9:47)
- Measuring historical return of individual assets (17:27)
- Measuring historical and expected return of portfolios (19:22)
- Measuring risk using standard variance (17:41)
- Efficient Portfolio and Risk
- Standard deviation of portfolios and covariance between individual assets (18:00)
- Correlation and portfolio risk (17:09)
- Efficient Frontier, Investor Utility, Types of Risk (15:16)
- Systematic risk, diversification of investment, market portfolio (18:06)
- Section 5
- Risk Free Assets and the CAPM (19:49)
- Calculating CAPM Return and Investment Decision (19:42)
- Sneak Peak into Equity Valuation using CAPM and Betas
- Discounting Risky Cash Flows (11:06)
- Risk Return Models (11:17)
- Equity Valuation with CAPM (16:53)
- Weighted Average Cost of Capital (6:49)
- Beta - Top-Down or Bottoms-Up? (5:57)
- Market Beta or Total Beta? (3:34)
- Levering and Unlevering Betas (2:55)
Access
Lifetime
Content
4 hours
Lessons
21
Case Studies in Macro-Economics
Take An Eagle's Eye View of Global Economies & Learn How Big Economic Stimulants Affect Your Personal Decisions
By Loonycorn | in Online Courses
Macroeconomics is the "big-picture" of the economy - a view of how, why, and in what way people, governments, and central banks react to economic events. In this course, you'll treat macroeconomics as a story, analyzing historical events, and discussing how major economic stimulants can affect personal financial decisions. You'll gain a holistic view of how global economies work, and how to act as an individual within them.
- Access 21 lectures & 4 hours of content 24/7
- Understand what can lead to inflation & when the markets might flourish
- Assess when interest rates may rise & when the right time to invest is
- Decode success stories of GDP growth & recognize when an economy may boom
- Recognize how oil price changes may impact foreign earnings & expenses
- Determine how government decisions may impact your salary
Loonycorn is comprised of four individuals—Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh—who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.
Details & Requirements
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Compatibility
- Internet required
Course Outline
- You, This Course and Us
- You, This Course and Us (2:44)
- Recession of 1970's
- Inheritance of troubled Economy (6:26)
- Unemployment and the story of Sam (9:47)
- Inflation demystified (8:47)
- Unemployment and inflation connect into Stagflation (13:03)
- Taxes and Deficit Financing (11:14)
- Monetary Policy and controlling Money Supply (8:33)
- Gross Domestic Product
- Components of GDP in Minion Land (19:07)
- Real, Nominal GDP and the Price Index (17:25)
- Income and the connection of Savings and Consumption (10:55)
- The GDP Growth Stories
- The Story of the miraculous Japanese Growth (8:25)
- The Exponential Growth of China decoded (12:48)
- The success story of friendly Singapore (8:26)
- Supply of Money
- Central Bank's tool kit for controlling money supply (14:41)
- The sub-prime mortgages and CDO (11:41)
- The burst: 2008 Global Economic Recession (12:03)
- Balance of Payments
- Current Account Transactions (14:55)
- Capital Account Transactions (13:14)
- Foreign Exchange Rates and Oil Prices
- Foreign Exchange rates, basket of goods and Purchasing Power Parity (13:17)
- PPP different from Real Exchange Rates (7:29)
- Oil and the repercussions of change in prices (8:50)
- Everything connected (1:22)
Access
Lifetime
Content
6 hours
Lessons
49
Corporate Finance 101: Equity Valuation
Explore Important Finance Concepts & Create Models Like an MBA
By Loonycorn | in Online Courses
Equity valuation can be a complex and confusing concept, but it's an essential one for entrepreneurs and anybody who works in a small business or startup. This course will explore equity valuation from both wide and narrow scopes before connecting the dots between the two to give you a full-fledged understanding of this important financial concept.
- Access 49 lectures & 6 hours of content 24/7
- Get introduced to equity valuation topics like intrinsic value, price, valuation & market capitalization
- Discover absolute valuation techniques
- Explore Net Present Value & discounting cash flows
- Apply important types of models, such as Dividend-Discount models, Free Cash Flow models & Relative-Value models
- Calculate the cost of capital to a company
Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.
Details & Requirements
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Compatibility
- Internet required
Course Outline
- You, Us & This Course
- You, Us & This Course
- Price, Value and Valuation
- Intrinsic Value (2:42)
- Valuation Models (1:38)
- Valuation and Market Cap (4:38)
- A Taxonomy of Valuation Methods (9:09)
- NPV and Discounting Cash Flows
- Absolute Valuation Models and NPV (4:15)
- Compound Interest and NPV (10:28)
- NPV and Price (7:28)
- A Simple NPV Example (4:35)
- Future Value of a Present Cash Flow (1:53)
- Semi-Annual Compounding (4:12)
- Continuous Compounding (3:43)
- NPV of a Stream of Cash Flows (5:00)
- Valuing Uncertain Cash Flows
- Discounting Risky Cash Flows (11:06)
- Risk Return Models (11:17)
- The Capital Asset Pricing Model (16:53)
- WACC: The Weighted Average Cost of Capital (6:49)
- Tax adjusting the cost of debt (6:43)
- WACC for consistency (2:21)
- Beta: Top-down or bottoms-up? (5:57)
- Market Beta or Total Beta? (3:34)
- Levering and Unlevering Betas (2:55)
- Debt and Operating Leases (7:00)
- Cost of Debt: Some additional factors (1:41)
- Dividend Discount Models
- Dividend Discount Models (9:10)
- Present Value, Future Value and Capital Appreciation (5:39)
- Modeling Future Dividends (6:25)
- Cash Cows: Constant Dividends and Growth Opportunities (9:34)
- Sustainable Growth Rate of Equity (10:55)
- Gordon Growth Model (5:27)
- The h-Model (4:38)
- Free Cash Flow Models
- Introducing FCF Valuation (13:59)
- FCFF and FCFE Details
- Introducing FCFF and FCFE (10:17)
- FCFF from CFO (10:17)
- FCFE from FCFF (3:23)
- FCFF or FCFE? Also, the APV Method (5:39)
- Why not Net Income or EBITDA? (6:57)
- FCFF from Net Income or EBITDA (8:26)
- Tying Up Loose Ends (9:29)
- Relative Valuation
- Introducing Relative Valuation Models (6:36)
- The P/E Ratio: Pros and Cons (6:05)
- Mechanics of calculating the P/E ratio (8:38)
- Market P/E and Macro-economics (4:31)
- Other Valuation Ratios: P/B and EV/EBITDA (5:53)
- Capital Structure and the M-M Propositions
- Capital Structure Introduced (17:28)
- Leverage, and the second M-M proposition (16:21)
- No Free Borrowed Lunch: The First M-M Proposition (13:14)
- Behind the Numbers: The Intuition Behind M-M (7:53)
- The Inevitability of Taxes (9:09)
- Wrapping up MM in a world with taxes (11:41)
Access
Lifetime
Content
7.50 hours
Lessons
39
Corporate Finance 101: Financial Statement Analysis & Ratios
Explore Important Finance Concepts & Create Models Like an MBA
By Loonycorn | in Online Courses
As any American can tell you, corporate finance is an incredibly complex and dicey subject. This course, however, will give you a comprehensive, big picture and small details overview of corporate finance. You'll dive into financial statements, ratios, Dupont's Identity, and more essential topics so you'll have a complete grasp of how money really works.
- Access 39 lectures & 7.5 hours of content 24/7
- Interpret financial statements, like balance sheets, income statements & statements of cash flows
- Parse SEC filings like the 10K & 10Q to understand the business model of any company entirely from its investor filings
- Calculate ratios in all major categories: liquidity, leverage, turnover, profitability & valuation
- Apply Dupont's Identity to see whether a company's stock returns are driven by operational efficiency, asset efficiency, or leverage
- Calculate the sustainable rate of growth at which a company can grow w/o external financing
Loonycorn is comprised of four individuals—Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh—who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.
Details & Requirements
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Compatibility
- Internet required
Course Outline
- You, Us & This Course
- You, Us & This Course (2:08)
- The Enterprise
- Sole Proprietorship (9:45)
- Partnership (6:09)
- The Corporation (13:47)
- Public and Private (6:37)
- Agency Problems and Corporate Governance (12:37)
- The Balance Sheet
- The Balance Sheet (5:33)
- Assets (14:54)
- LIabilities (3:51)
- Shareholder's Equity (10:10)
- Balance Sheet Case Studies: Facebook, Twitter, LinkedIn (13:10)
- The Income Statement
- Income Statement (16:18)
- The Net Income Waterfall (13:07)
- Statement of Comprehensive Income (9:45)
- Income Statement Case Studies (6:30)
- The Statement of Cash Flows
- Statement of Cash Flows (12:34)
- The Direct and Indirect Methods (11:58)
- Cash Flow Statement Case Studies (3:28)
- Ratios
- Ratios Introduced (5:00)
- Liquidity, Leverage and Efficiency (12:47)
- Profitability and Valuation (5:13)
- Some Advanced Topics
- Dupont's Identity (9:32)
- External Financing & The Sustainable Rate of Growth (10:55)
- Common Accounting Shenanigans (10:06)
- Case Studies
- Facebook (8:41)
- LinkedIn (12:29)
- Twitter (17:51)
- EPS
- Introducing EPS (13:30)
- Basic EPS (13:14)
- Diluted EPS (16:04)
- Diluted EPS (continued) (16:54)
- Inventories
- Inventory Valuation (15:19)
- Understanding Inventories (17:07)
- More on Assets
- Fixed Assets (11:31)
- Capitalisation Decisions (13:29)
- Depreciation Methodologies (12:52)
- Implications of Depreciation (13:10)
- Leases
- Leases introduced (10:17)
- NPV of Lease Payments (8:22)
- Operating lease versus financial lease (12:28)
- Lease Example (13:13)
Access
Lifetime
Content
6 hours
Lessons
42
Economics: Game Theory, Competition, Elasticity
Learn the Microeconomic Forces & Theories That Drive Everyday Decisions
By Loonycorn | in Online Courses
Microeconomics is a field packed with everyday applications, from economic to social transactions. This course explores the competitive theories of economics, giving you a crash course in how to think about markets, finance, and the economic drivers that guide all of our decisions.
- Access 42 lectures & 6 hours of content 24/7
- Apply Game Theory to decide whether to be adversarial or cooperative in real-life situations
- Determine how best to price products or services you are selling
- Decide the kind of cost structure a firm or enterprise should have, relative to its competitive landscape
- Model demand, supply & the effects of income, government regulations & technology
Loonycorn is comprised of four individuals—Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh—who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.
Details & Requirements
- Length of time users can access this course: lifetime
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Compatibility
- Internet required
Course Outline
- You, Us & This Course
- You, Us & This Course (2:28)
- Game Theory
- Discounting & Loyalty (16:20)
- The Rivalry Game (10:12)
- Trust and Honor in Organized Crime (11:04)
- Winner-Takes-All Games and Sports in Society (14:12)
- Demand
- The Law of Demand (6:55)
- Examples of the Law of Demand (9:12)
- Veblen Goods (6:34)
- Giffen Goods (6:35)
- Income Effects on Demand (7:21)
- Complements and substitutes (8:16)
- Supply
- The Law of Supply (6:07)
- Examples of the Law of Supply (5:59)
- Inflation, Technology and Government (12:50)
- Market Equilibrium (8:59)
- Elasticity
- Elasticity and Price Sensitivity (11:06)
- Horizontal and Vertical Demand Curves (8:30)
- Revenue Maximisation (7:05)
- Elasticity of Veblen and Giffen Goods (3:31)
- Income and Cross-Elasticities (7:40)
- Elasticity and Linear Demand Curves (9:06)
- Applications of Elasticity
- Taxes (10:26)
- Agriculture (8:18)
- Minimum Wages (7:24)
- Price Controls (7:20)
- Utility
- Utility and Diminishing Marginal Utility (14:54)
- The Paradox of Value (10:13)
- Indifference Curves and Consumer Equilibrium (14:15)
- Deriving Demand Curves and Income Effects from Indifference Curves (10:33)
- Consumer Surplus (4:39)
- Firms
- Factors of Production (11:44)
- Total and Marginal Product (10:01)
- The Least Cost Principle (4:49)
- Returns to Scale (5:04)
- Costs
- Total and Marginal Costs (8:45)
- Average and Marginal Costs (8:08)
- Types of Supply Curves (8:27)
- Competition
- Long Run, Short Run (5:46)
- Perfect Competition (10:27)
- Profit Maximisation in Perfect Competition (6:56)
- Monopoly (10:04)
- Monopolistic Competition (3:40)
Access
Lifetime
Content
4 hours
Lessons
33
Case Studies: Facebook, Twitter, LinkedIn, Apple
Learn From the Business Successes & Failures of Top Tech Companies
By Loonycorn | in Online Courses
This course gets straight to business (quite literally), outlining the hows and whys of top tech companies' successes and failures. Dive into 4 in-depth case studies of tech titans Apple, Facebook, Twitter, and LinkedIn, gaining insight as to each company's business model, finances, and more. You'll walk away with valuable insights you can apply to your own company to optimize for its long-term success.
- Dive into case studies on the successes & failures of top tech companies w/ 4 hours of content
- Walk through in-depth analyses of featured companies' business models, financial statements, investor filings, product choices & competitive analyses
- See how Apple has iterated upon its product offerings to great financial effect
- Understand how & why Twitter struggles to monetize its content
- Learn how Facebook has turned its massive user base into a revenue generating machine
- Apply these insights to your own business endeavors
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.
Details & Requirements
- Length of time users can access this course: lifetime access
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Course Outline
- The Business of Technology
- The Business of Technology
- Case Study: Apple
- The best reason to study Apple
- How Important is Apple?
- Perfect Competition: The Exceptions Prove the Rule
- The Digital Hub: iTunes + iPod
- On Tim's Watch: Apple Watch and Apple Pay
- How Big, How Far
- The Best of the Best
- Need Help Counting the Money?
- A Job for Jobs
- Apple Before the Second Coming
- Unbeaten, Unbeatable
- Case Study: Twitter
- A Tale of two Twitters
- An Important Dichotomy
- Engagement Precedes Monetisation
- How does Twitter manage to lose so much money?
- How Big, How Far
- Twitter's Strange Life So Far
- The Characters in the Drama
- Toe-to-Toe
- Case Study: LinkedIn
- LinkedIn: Network or Destination?
- Sum of its Parts
- Not a One-Trick Pony
- A Rare Success In China
- Drama-Free
- Social Network to Content Destination
- In Its Own Little Niches
- Case Study: Facebook
- Facebook Rising
- Rockstar du jour
- A Profit-Machine Buys Growth
- Monetising in the US, Engaging in Asia
- Learning from mistakes: own and others
- Goldilocks
Access
Lifetime
Content
9 hours
Lessons
82
Learn By Example: Statistics and Data Science in R
Use Real-Life Examples & Case Studies to Understand the R Programming Language
By Loonycorn | in Online Courses
R is a programming language and software environment for statistical computing and graphics that is widely used among statisticians and data miners for data analysis. In this course, you'll get a thorough run-through of how R works and how it's applied to data science. Before you know it, you'll be crunching numbers like a pro, and be better qualified for many lucrative careers.
- Access 82 lectures & 9 hours of content 24/7
- Cover basic statistical principles like mean, median, range, etc.
- Learn theoretical aspects of statistical concepts
- Discover datatypes & data structures in R, vectors, arrays, matrices & more
- Understand Linear Regression
- Visualize data in R using a variety of charts & graphs
- Delve into descriptive & inferential statistics
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.
Details & Requirements
- Length of time users can access this course: lifetime access
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels
Compatibility
- Internet required
Course Outline
- Introduction
- You, This course and Us (2:32)
- Top Down vs Bottoms Up : The Google vs McKinsey way of looking at data (12:58)
- R and RStudio installed (5:10)
- The 10 second answer : Descriptive Statistics
- Descriptive Statistics : Mean, Median, Mode (10:07)
- Our first foray into R : Frequency Distributions (6:07)
- Draw your first plot : A Histogram (3:11)
- Computing Mean, Median, Mode in R (2:21)
- What is IQR (Inter-quartile Range)? (8:08)
- Box and Whisker Plots (3:11)
- The Standard Deviation (10:24)
- Computing IQR and Standard Deviation in R (6:06)
- Inferential Statistics
- Drawing inferences from data (3:25)
- Random Variables are ubiquitous (16:54)
- The Normal Probability Distribution (9:31)
- Sampling is like fishing (6:14)
- Sample Statistics and Sampling Distributions (9:25)
- Case studies in Inferential Statistics
- Case Study 1 : Football Players (Estimating Population Mean from a Sample) (6:49)
- Case Study 2 : Election Polling (Estimating Population Proportion from a Sample) (7:51)
- Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean) (13:53)
- Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion) (9:49)
- Case Study 5: A/B Testing (Comparing the means of two populations) (17:18)
- Case Study 6: Customer Analysis (Comparing the proportions of 2 populations) (11:50)
- Diving into R
- Harnessing the power of R (7:26)
- Assigning Variables (8:48)
- Printing an output (13:03)
- Numbers are of type numeric (5:25)
- Characters and Dates (7:30)
- Logicals (3:24)
- Vectors
- Data Structures are the building blocks of R (8:24)
- Creating a Vector (2:23)
- The Mode of a Vector (4:18)
- Vectors are Atomic (2:24)
- Doing something with each element of a Vector (3:09)
- Aggregating Vectors (1:28)
- Operations between vectors of the same length (5:39)
- Operations between vectors of different length (5:30)
- Generating Sequences (6:25)
- Using conditions with Vectors (2:04)
- Find the lengths of multiple strings using Vectors (2:22)
- Generate a complex sequence (using recycling) (2:49)
- Vector Indexing (using numbers) (6:56)
- Vector Indexing (using conditions) (6:18)
- Vector Indexing (using names) (2:27)
- Arrays
- Creating an Array (11:36)
- Indexing an Array (7:38)
- Operations between 2 Arrays (2:09)
- Operations between an Array and a Vector (2:45)
- Outer Products (6:23)
- Matrices
- A Matrix is a 2-Dimensional Array (7:59)
- Creating a Matrix (2:00)
- Matrix Multiplication (2:49)
- Merging Matrices (2:06)
- Solving a set of linear equations (2:06)
- Factors
- What is a factor? (6:48)
- Find the distinct values in a dataset (using factors) (1:28)
- Replace the levels of a factor (2:18)
- Aggregate factors with table() (1:40)
- Aggregate factors with tapply() (5:07)
- Lists and Data Frames
- Introducing Lists (5:11)
- Introducing Data Frames (4:28)
- Reading Data from files (4:52)
- Indexing a Data Frame (5:38)
- Aggregating and Sorting a Data Frame (6:28)
- Merging Data Frames (3:30)
- Regression quantifies relationships between variables
- Introducing Regression (12:22)
- What is Linear Regression? (16:06)
- A Regression Case Study : The Capital Asset Pricing Model (CAPM) (6:34)
- Linear Regression in Excel
- Linear Regression in Excel : Preparing the data (9:53)
- Linear Regression in Excel : Using LINEST() (16:48)
- Linear Regression in R
- Linear Regression in R : Preparing the data (13:05)
- Linear Regression in R : lm() and summary() (16:04)
- Multiple Linear Regression (12:16)
- Adding Categorical Variables to a linear model (7:44)
- Robust Regression in R : rlm() (3:14)
- Parsing Regression Diagnostic Plots (12:10)
- Data Visualization in R
- Data Visualization (6:23)
- The plot() function in R (3:42)
- Control color palettes with RColorbrewer (4:15)
- Drawing barplots (5:25)
- Drawing a heatmap (2:52)
- Drawing a Scatterplot Matrix (3:41)
- Plot a line chart with ggplot2 (8:19)
Access
Lifetime
Content
11 hours
Lessons
64
Quant Trading Using Machine Learning
Play the Markets Like a Pro After 11 Hours of Integrating Machine Learning into Your Investment Strategies
By Loonycorn | in Online Courses
Financial markets are fickle beasts that can be extremely difficult to navigate for the average investor. This course will introduce you to machine learning, a field of study that gives computers the ability to learn without being explicitly programmed, while teaching you how to apply these techniques to quantitative trading. Using Python libraries, you'll discover how to build sophisticated financial models that will better inform your investing decisions. Ideally, this one will buy itself back and then some!
- Access 64 lectures & 11 hours of content 24/7
- Get a crash course in quantitative trading from stocks & indices to momentum investing & backtesting
- Discover machine learning principles like decision trees, ensemble learning, random forests & more
- Set up a historical price database in MySQL using Python
- Learn Python libraries like Pandas, Scikit-Learn, XGBoost & Hyperopt
- Access source code any time as a continuing resource
Instructor
Loonycorn is comprised of four individuals--Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh--who have honed their tech expertises at Google and Flipkart. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students. For more details on the course and instructor, click here.Important Details
- Length of time users can access this course: lifetime access
- Access options: web streaming, mobile streaming
- Certification of completion not included
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: all levels, but working knowledge of Python would be helpful
Requirements
- Internet required
Course Outline
- You, This Course and Us
- You, This Course and Us
- Setting up your Development Environment
- Installing Anaconda for Python
- Installing Pycharm - a Python IDE
- MySQL Introduced and Installed (Mac OS X)
- MySQL Server Configuration and MySQL Workbench (Mac OS X)
- MySQL Installation (Windows)
- Introduction to Quant Trading
- Financial Markets - Who are the players?
- What is a Stock Market Index?
- The Mechanics of Trading - Long vs Short positions
- Futures Contracts
- Evaluating Trading Strategies - Risk And Return
- Evaluating Trading Strategies - The Sharpe Ratio
- The 2 Step process - Modeling and Backtesting
- Developing Trading Strategies in Excel
- Are markets efficient or inefficient?
- Momentum Investing
- Mean Reversion
- Developing a Trading Strategy in Excel
- Setting up a Price Database
- Programmatically Downloading Historical Price Data
- CodeAlong - Dowloading Price data from Yahoo Finance
- CodeAlong - Downloading a URL in Python
- CodeAlong - Downloading Price data from the NSE
- CodeAlong - Unzip and process the downloaded files
- CodeAlong - Download Historical Data for 10 years
- Inserting the Downloaded files into a Database
- CodeAlong - Bulk loading downloaded files into MySQL tables
- Data Preparation
- CodeAlong - Data Preparation
- Adjusting for Corporate Actions
- CodeAlong - Adjusting for Corporate Actions 1
- CodeAlong - Adjusting for Corporate Actions 2
- CodeAlong - Inserting Index prices into MySQL
- CodeAlong = Constructing a Calendar Features table in MySQL
- Decision Trees, Ensemble Learning and Random Forests
- Planting the seed - What are Decision Trees?
- Growing the Tree - Decision Tree Learning
- Branching out - Information Gain
- Decision Tree Algorithms
- Overfitting - The Bane of Machine Learning
- Overfitting Continued
- Cross Validation
- Regularization
- The Wisdom Of Crowds - Ensemble Learning
- Ensemble Learning continued - Bagging, Boosting and Stacking
- Random Forests - Much more than trees
- A Trading Strategy as Machine Learning Classification
- Defining the problem - Machine Learning Classification
- Feature Engineering
- Know the basics - A Pandas tutorial
- CodeAlong - Fetching Data from MySQL
- CodeAlong - Constructing some simple features
- CodeAlong - Constructing a Momentum Feature
- CodeAlong - Constructing a Jump Feature
- CodeAlong - Assigning Labels
- CodeAlong - Putting it all together
- CodeAlong - Include support features from other tickers
- Engineering a Complex Feature - A Categorical Variable with Past Trends
- Engineering a Categorical Variable
- CodeAlong - Engineering a Categorical Variable
- Building a Machine Learning Classifier in Python
- Introducing Scikit-Learn
- Introducing RandomForestClassifier
- Training and Testing a Machine Learning Classifier
- Compare Results from different Strategies
- Using Class probabilities for predictions
- Nearest Neighbors Classifier
- A Nearest Neighbors Classifier
- CodeAlong - A nearest neighbors Classifier
- Gradient Boosted Trees
- What are Gradient Boosted Trees?
- Introducing XGBoost - A python library for GBT
- CodeAlong - Parameter Tuning for Gradient Boosted Classifiers
Terms
- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
