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

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

Hello and Thanks | Feedback Channel

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)

Preview

Robot 1: Jarrine_A – A Sneak Peek! (9:09)

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

Preview

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 SetHoldings() 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 Size

Preview

Is 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)

Upcoming Chapters

13 Upcoming Chapters

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