AlgoTrading101 – AT101: Algorithmic Trading Immersive Course (2nd Edition)

Description

About Course:

Course Curriculum

Chapter 1 > Here’s What You Are In For!

• Why Code for Trading? The New Mindset – Trading Ideas and Concepts (Part 1) (5:38)
• Why Code for Trading? The New Mindset – Trading Ideas and Concepts (Part 2) (5:35)
• The First Step – How To Your Journey?
• The Real Holy Grail of Trading
• Using the Course for Manual Trading
• Our Main Mental Model – TEST Trading Framework

Chapter 2 > QuantConnect Set Up – Get ed on Our Algo Trading Platform

• What is QuantConnect and Why Choose it?
• Let’s Sign Up for QuantConnect! (1:28)
• Overview of the QC Coding Area (4:26)
• Robot 1: Jarrine_A – A Sneak Peek! (9:09)

Chapter 3 > TEST Framework Step 1: Thesis – The Reason for Your Trade (Part 1)

• What is a Thesis and Why should it be Falsifiable
• When To Use Algo Trading, When to Use Manual Trading
• Simple vs Complex – Overview and Approach to Strategies in this Course
• The 2 Main Behaviours – Lagged Correlation and Cointegration (4:25)
• What is X worth? Valuation is a Social Construct
• A Trading Strategy that Wins in All Conditions? Don’t Build That
• Optional Resources for those new to finance and markets
• Finding Alpha! What Type of Strategies Should I go for?

Chapter 4 > Python Basics 1 – The First Step

• This chapter is optional for those who know Python
• Why Python over other Programming Languages
• What is Coding Really About?
• What is Jupyter Notebook? Why do we need it when we have QuantConnect
• Get the Snake – Installing Anaconda (1:45)
• 2 Ways to Open Your Jupyter Notebook
• Alternative Coding Platform: Google Colaboratory
• AlgoTrading101 Partners with Holistic Coding and Algo-Hunter
• Overview of our Research and Execution Tool – Jupyter Notebook (12:03)
• What Snakes are these? Anaconda vs Python
• The Basic Building Blocks – Variables and Expressions (6:55)
• Comparing A and B – Comparison Operators (8:11)
• Know what You Can Do – Jupyter Notebook & Python Superpower List
• Store your code well
• Resources for Learning Python

Chapter 5 > Python Basics 2 – Storing a Table of Values + If Statements

• This chapter is optional for those who know Python.
• Run all cells
• Data Types – Your Variables contain different Types of Info
• The Simplest Table – A LIST of Values (10:12)
• The Simplest Tables with Unchangeable Values – Tuples (4:16)
• Printing stuff – Formatting your texts and numbers (9:17)
• Meaning behind String Symbols
• If A happens, do B – Conditional Statements (17:11)

Chapter 6 > QuantConnect Basics 1 – Pew Pew Fire All The Orders!

• Backtesting Simplified – What Happens + Why Backtest?
• How does a Backtest Work? (2:43)
• Your “System Settings” – Understanding the Initialize () Area (4:26)
• Types of Orders
• How to Fire a Market Order (2:32)
• How to Fire a Limit Order (1:25)
• How to Fire a “Market Order” using a Limit Order (1:12)
• How to Cancel a Pending Order (0:59)
• How to Modify a Pending Order (1:17)
• How to Use Set Holdings() to Target a Certain Stock Allocation (2:37)
• Sell it all! How to Liquidate Your Portfolio (1:14)
• How to “Print” Important Info and Warnings
• How to Check Your Order Status (2:41)
• How to Get Your Order Details (1:06)
• How to use the QC Help Features and Documentation (5:54)
• Stuck at Programming? Self-Learning and Getting Help Guide
• Other ways to get help
• Save your code offline

Chapter 7 > QuantConnect Basics 2 – Getting Price Data + Fire More Interesting Orders

• How to Get Current Price
• Use self.spy instead of “SPY”
• How to Get Historical Data (Part 1) (2:38)
• How to Get Historical Data (Part 2) (2:09)
• Adjusting Prices for Stock Splits and Dividends (3:02)
• How to Get Portfolio Information (2:59)
• List all Positions (1:59)
• Coding Differences for Adding Different Asset Classes
• How to Fire an Order based on a Rule – If X Happens then Buy Y (2:46)
• Exploring the Different Types of Orders (1:30)
• Fire a Stop Loss order (1:50)
• Fire a Take Profit order (1:14)
• Time-in-Force (1:56)
• Debugging Your Code using the QC Debugger (4:18)
• Reading our Backtest Results (3:00)
• Resources for Learning QuantConnect Coding
• Need a QC paid account for Live Trading

Chapter 8 > Robot Jarrine – Understanding the Thesis and Thought Process

• General Structure of a Strategy
• Jarrine_A Trading Rules
• Robot 1: Let’s Build Our First Strategy, Jarrine_A! (Part 1) (12:15)
• Robot 1: Let’s Build Our First Strategy, Jarrine_A! (Part 2) (13:41)
• Robot 1: Let’s Build Our First Strategy, Jarrine_A! (Part 3) (9:45)
• Robot 2: Jarrine_B – Risk Measures + Faster Backtesting (Part 1) (10:23)
• Robot 2: Jarrine_B – Risk Measures + Faster Backtesting (Part 2) (5:29)
• Glimpse of future strategies that we will cover
• Does this course suck? Or is it adding value to you?

Chapter 9 > TEST Framework Step 1: Thesis – The Reason for Your Trade (Part 2)

• How to Choose What Markets/Strategies to Trade
• How many Domain Expertise do I need + My Domain Expertise
• Build Intuition – Visualisation and Manual Trading
• How to Verify Your Thesis
• How to use TradingView Charts
• Understanding Lagged Correlation and Looking for it in Charts
• Understanding Cointegration and Looking for it in Charts
• Is it Priced in?
• It is not what it is, it is what the market expects
• Freeroll Trades – Almost Free Money
• Economic Data Releases – Potential Source of Freeroll Trades
• Outwitting the Masses – Second-Order Thinking
• David vs Goliath – Can we outwit the Big Funds?
• Falsifying a Thesis using Statistics – A Dangerous Area
• How to Reverse Engineer a Thesis
• Us vs Hedge Funds: Why We Dislike Trading on Lower Timeframes
• Semi-algo Trading – A Hope for Retail Traders?
• Resources and Books

Chapter 10 > TEST Framework Step 2 : EV – The Expected Value Of Your Trade

• What is Expected Value (EV) and Why do We Care
• EV Formula for $ and % returns
• What makes a Good Trader? How to determine EV inputs?
• Decisions Points (DP). Trade to the Nearest DP
• When is Your DP Exactly? It is before the Key Event
• Short Term DP within a Long Term DP but Opposite Directions
• EV per time
• Think in Probabilities not Binary
• Estimation Errors and Lower Bound EV
• Freerolls are +EV in spite of Estimation Errors
• Conviction and Accuracy
• Poorer Entry Price, Higher EV
• Trading when P(W) is near 0
• Bubbles – EV Management When there is Potential High Upside
• Even if +EV, Volatility Can Wreck You
• 3 outcomes
• EV for Comparing Trades
• How to Determine EV Inputs for Algorithmic Strategies
• Long Term EV Calculation (6:40)

Chapter 11 > Get Data For Analysis – Getting Some Basic Data (Outside of QC)

• Why Do We Need Data Outside of QC
• Copy Others’ Code – Python Libraries and Packages
• Installing Library for Yahoo Finance API (0:35)
• Retrieving Data from Yahoo Finance API – Just a One-Liner (6:14)
• Different Ways to Install Libraries

Chapter 12 > Python Basics 3 – Doing Something Many Times with Code (Loops!)

• Do Something Many Times Using Code – For Loops (10:26)
• Loops Practice 1 – Basic For Loops
• Do Something Many Times in a Different Way – While Loops (10:04)
• Loops Practice 2 – Basic While Loops
• Looping Twice – Nested Loops (4:52)
• Loops Practice 3 – Nested Loops
• If A then B, Many Times – Loops with Conditionals (6:54)
• Loops Practice 4 – Conditional + Nested Loops
• Answers to Loops Practice 1 to 4 (11:19)
• Loops with some Control (Continue, Break and Pass) (4:34)
• Loops Practice 5 – Calculating Stock Metrics
• Answers to Loops Practice 5 (12:30)
• For Loops without the Range Method
• When to use for vs While Loops
• Get Data from CSV and TXT (10:32)
• Exporting dataframe to CSV
• Elegant Code vs Learning Trading

Chapter 13 > Python Basics 4 – A Library for Data Analysis, Pandas (Not the lazy animal!)

• Generating Random Numbers
• What is Pandas and Why Do We Need It?
• One Column Tables of Data – Series (8:34)
• Two Column Tables of Data – Dataframe (This one is important) (14:35)
• Managing Dataframes – Editing our Tables (7:24)
• Managing Dataframes 2 – Changing the Shape of our Dataframes (7:55)
• Datetime Management – Adding Dates to Dataframes (7:54)
• Pandas Exercise 1 – All You Need for Managing Dataframes
• Changing Dataframe’s Data Type
• Not-a-Number? Dealing with NaN and NaT

Chapter 14 > Python Basics 5 – Functions and OOP

• What are Functions – Our Little Factories
• User-Defined Functions – Learn to Code Your Own Factories! (20:29)
• Functions Practice 1 – Questions
• Functions Practice 1 – Solutions (Part 1) (9:02)
• Functions Practice 1 – Solutions (Part 2) (10:56)
• What are Scripts – Simple Python file (Also: How to import your own code) (10:18)
• Uses of Python Scripts vs Jupyter Notebooks
• Modules vs Libraries vs Packages – Understanding the Terminologies
• OOP Series – Object-Oriented Programming (OOP) Simplified. Objects store values and/or does stuff (5:06)
• OOP Series – Difference between Classes and Objects (2:40)
• OOP Series – Why do we need to learn OOP? Ans: We have no choice
• OOP Series – Object Variables: Storing Values (Part 1) (9:13)
• OOP Series – Object Variables: Storing Values (Part 2) (11:40)
• OOP Series – Object Functions: Doing stuff (11:18)
• Objects Practice 1 – Object Variables (Questions + Solutions)
• Objects Practice 2 – Object Functions (Questions)
• Objects Practice 2 – Object Functions (Solutions) Part 1 (5:34)
• Objects Practice 2 – Object Functions (Solutions) Part 2 (8:55)
• Naming Conventions – How to name your classes, variables etc

Chapter 15 > Practical Statistics 101 – Making Sense of Key Figures

• Statistical Significance and Law of Large Numbers – More is better (6:58)
• Minimum Sample Size and Application to Trading (10:59)
• What is an Abnormal Move – Understanding Standard Deviations
• Stock Returns Behaviour – Understanding Normal Distributions
• Statistical View on Correlation and Sensitivity/Regression
• Statistical vs Practical View on Cointegration (Part 1)
• Statistical vs Practical View on Cointegration (Part 2)
• The Real Role of Statistics in our Trading
• Optional Readings on Statistics

Chapter 16 > TEST Framework Step 3 : Sizing – Bad Sizing Breaks Good Strategies (Part 1)

• Why Bother with Position Sizing – Does it Really Matter? (7:10)
• Translating Risk per Trade to Position SizeIs there an Optimal Sizing – Do we bet more when EV goes up?
• What is the Optimal Bet Size?
• Kelly Criterion Formula
• New EV Formula -> EV with Sizing Formula
• Same EV, different P(L) different L = Different Sizing
• Freerolls! Is low L always good? Ratio Matters
• Kelly Criterion 3 Drawbacks
• Drawback 1 + Solution: Sensitive to Small Changes
• Drawback 2 + Solution: Doesn’t consider Trade Management Issues like Drawdowns and Psychology
• Drawback 3 + Solution: Only Considers 2 Outcomes (Part 1)
• Drawback 3 + Solution: Only Considers 2 Outcomes (Part 2)
• Does this course suck? Or is it adding value to you? (Part 2)

Chapter 17 > TEST Framework Step 3 : Sizing – Inversion, Diversification and other Tips (Part 2)

• What if Kelly is Negative? Do we Short? Ans: Yes
• Inversing your trade might not always work
• Don’t Lose More than 30%
• Kelly asks me to lose 30%?! That’s crazy! Yes it is. Do NOT follow it
• Slow and Steady leads to Safer Leverage leads to More Profits
• Don’t take Trades that can lead to Complete Ruin
• Larger Capital, Lower Size. Vice versa
• High Risk High Return is Leverage, Not Skill
• When you are a Beginner, Your aim is to Learn not Earn. Bonus: Fund Raising
• Longs’ Profits Compound, Shorts’ Do Not
• Understanding Diversification. Diversify then Leverage
• How to Allocate Capital into Different Strategies (Upcoming)

TEST Framework Step 4: Trade Management – What To Do When The Market Moves

• Trade Management Methodology: Repeat the First 3 Steps of TEST
• Falsifiable vs Non-Falsifiable Thesis Trade Management
• Anti-TEST Framework for Non-Falsifiable Thesis
• Semi-Falsifiable Thesis
• Managing Trading Psychology for Non-Falsifiable Thesis
• Managing Trading Psychology for Falsifiable and Semi-Falsifiable Thesis
• Survival Comes First and Defining Success
• Bonus Section – Trade Management in Investing