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AlgoTrading101 Courses

AlgoTrading101 Courses
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The Psychology Of Clicking "Buy Now"


The Psychology Of Clicking "Buy Now"
✅ AlgoTrading101 Course Syllabus
AlgoTrading101 consists of 2 main courses:
AT101: Algorithmic Trading Immersive Course
PT101: Practical Quantitative Trading with Python Masterclass
✅ AT101 focuses on the fundamentals of trading strategy design, testing and execution.
PT101 focuses on modern and more advanced strategies such as:
Obscure markets like Canadian bond STIR futures
Multi-asset strategies
Alternative data
Web scraping
Machine learning
✅ AT101: Algorithmic Trading Immersive Course
Chapter list (along with learning objectives for each chapter)
AT101: Algorithmic Trading Immersive Course
✅ Chapter list (along with learning objectives for each chapter)
Here’s What You Are In For!
What is an Algo Trading Robot, its key traits and code structure
What makes a successful Algo Trader
How to set up and navigate your infrastructure/coding software
Programming Basics 1: Variables and Conditional
Basics of our coding language (MQL4)
Syntax, Variables, Operations and Conditional Expressions
Robot 1: Adeline – Our First Robot!
Background to Forex markets, chart reading, basic indicators
Coding Adeline together
Testing Adeline using past data
Brief look at modelling quality
Uncommon Common Sense. Design Effective And Logical Robots
Overview of our Strategy Development Guide
Preliminary Research
Live Execution
Pros and Cons of an Algo Trading Robot
Mathematical Expectations of our robots’ performance
Garbage In, Garbage Out. Understanding Data
Data Sources and Storage
A look at the importance of data cleanliness
Cleaning data (basic)
Bad ticks, inaccurate testing and market tricksters
Programming Basics 2: Loops
Learning how to code loops
Practice Exercises for Loops
Robot 2: Belinda – Utilising Volatility!
Our first measure of volatility (ATR)
Introducing Belinda, the improved version of Adeline
Coding and testing Belinda
To Buy Big or Small? Position Sizing and Money Management
Understanding trade/bet size (how much to trade per position) using a coin flip game
Designing a bet sizing algorithm based on account size
Coding our bet sizing algorithm
Robot 2A: Belinda Upgraded (No Gambler’s Ruin for Me!)
Implementing our bet sizing algorithm in Belinda
Where To Start? Idea Generation and Expectations
Setting expectations for our robots based on our resources, personality, skill set, lifestyle and goals
Understand the essence of a trading idea – Proxies and Relationships
Sources of trading ideas
A look at the different types of strategies
Grading ideas – Introducing our framework for vetting ideas
How to fight against big hedge funds
Programming Basics 3: Functions, Time and Self-Learning
Learn to learn programming
Code errors and debugging
Coding Functions
Practice Exercises for Functions
Relevant Statistics 101!
Statistical significance and Law of Large numbers and their role in robot testing
Deriving suitable minimum sample size for our backtests
Understanding Robot Behaviour and Robustness: Backtesting!
Ensuring code accuracy
Types of market condition
Testing for Robustness
Period Robustness
Timeframe Robustness
Seasonal Robustness
Instrument Robustness
Testing our robots through intended and unintended periods
Stress testing our robots through black swans
The butterfly Effect – Backtest bias via start point selection
Grading the performance of our robots
Programming Basics 4: Arrays And Indicators
A look at our mentality towards Indicators
Math behind Indicators
Coding Arrays and Indicators
Robot 3: Clarissa – Playing with Time
Understanding the Datetime data type
Coding rules revolving date and time manipulation
Introducing and coding Clarissa – our robot that uses time entries
What A Mess – Managing Trades, Orders and Positions
Order limitations by your brokers
Coding our customised order function
Multiple order management
Modelling transaction cost, spreads and slippage
Robot 4: Desiree – Trade like the Turtles
The history of the Turtle Traders
Introducing and coding a simplified turtle strategy
Design Theories I – Improving Robots By Manipulating Time, Entries and Exits
Profitability in different timeframes
Deriving optimal stop loss levels
Comparing the importance of entries vs exits
Analysing asymmetrical long and short rulesThe Psychology Of Clicking "Buy Now"
Add A Twist To Your Orders – Advanced Order Management
Breakeven and trailing stops
Hiding from your broker – Creating virtual stops and take profit orders
Robot 5: Desiree 2.0
Buff Up Your Robot Responsibly – Optimisation Without Curve Fitting
Objective Functions, Robustness and Curve Fitting
10 Ways to minimise curve fitting (overfitting)
Degrees of Freedom
Parameter Robustness
In and out-of-sample testing
Optimisation Evaluation
Perfect Your Bet Sizing – Advanced Position Sizing Methods
Relationship between sizing and trading frequency
Gearing up and down with volatility
Impossible Trinity of Sizing – Relationship between Leverage, % Risked and Stop Loss
First Principles of sizing – Building customised sizing algorithms
Other types of sizing – Kelly Criterion, Martingales and Anti-Martingales
Robot 6: Elizabeth
Programming Basics 5: Clean Up Your Codes! Simple Is Fast!
Clean and robust coding
MT4 Global Variables
MQL4 Libraries
Garbage In, Garbage Out Again. Advanced Data Cleaning (Part 1)
Creating custom timeframes
Clean data, biased output
Excel VBA – Using Excel Magic to Improve our Trading
Excel trading game
Conditional statements
Garbage In, Garbage Out Again. Advanced Data Cleaning (Part 2)
Data time zone manipulation
Defining “clean enough” data
Scanning for errors
Advanced data cleaning methodologies
I Like Colors And Shapes – Adding Graphics
Creating a Dashboard: Graphics and Labels
Creating trendlines and levels
Ring Ring! Notify Yourself When Something Goes Wrong (Or Right)
Coding smartphone notifications
Notify yourself during trade or price events
Robot 7: Faye – Semi-Automated Trading
Connect with the outside world – Importing and Exporting Data out of our Trading Platform
Read and write information to Excel
Build a spread logger
Programming Basics 6: Trading Platform Nuances
Perfecting the little coding details
Understanding trading and backtesting nuances
Design Theories II – The “Secret Sauce”
Prudence-Behavioural Framework
Alpha 1: Data
Alpha 2: Global Macro
Alpha 3: High-Frequency Trading
Alpha 4: Market Microstructure
Hybrid Model – Semi-Algorithmic Trading
5 Realities of Algorithmic Trading
Crowd Behaviour – Outwitting the Masses
Walking Forward – Advanced Optimisation
Walk Forward Optimisation
Performance patterns, consistency and seasonality
3D Parameter space evaluation
Trading CFDs
Looking Outwards – Trading On External Info and Alternative Data
Trading using volume
Feeding external data into MT4
Trade on external events
Robot 8: Gwen
Cash Is King! – Running Robots With Real Money
Paper versus Live trading
Minimum Capital Determination
Broker Selection
Virtual Private Servers
Downtime Prevention Protocol
Hedging issues
Strategy Monitor – Updating our robots regularly
Live walk-forward optimisation
Investor Marketplace
Watch Her Well – Monitoring Your Robot(s)
Operational Risk Management
Monitoring our robots
When to manually intervene
Reviewing performance
Understanding Trading Psychology – Emotions during drawdowns
✅ PT101: Practical Quantitative Trading with Python Masterclass
(In progress, we are still adding content)
Practical Strategies for Modern Markets
✅ Basic Python and Test Strategies
Just enough Python to get you started (we will learn more advanced Python techniques in the later part of the course)
Designing a simple pair trading test strategy to whet your appetite and give you an rough sense of what to expect
✅ Cointegration (Mean reversion: When A and B moves apart, we bet they will revert) (WE ARE HERE NOW)
(Concept) Synthetic assets (ranging assets that are made by combining different assets)
(Strategy) Bond futures calendar spreads and structures (creating ranging assets using bond futures)
(Strategy) Market making using a proxy asset (entering and exit trades at the bid and ask prices)
(Strategy) Statistical Arbitrage. Trading hundreds of stocks in a mean reversion manner.
✅ Sentiment Analysis and Web API (Collect data from websites via special “links”)
(Concept) Use Web API to collect data (eg. Google trends to analyse search traffic)
(Strategy) Scour tons of stocks to see which stocks have sudden increase in search traffic volume
✅ Alternative Data (Non-price data like Credit card, Location data etc)
(Strategy) Use paid alternative data from vendors to analyse stocks
(Strategy) Create your own special index by combining different alternative data (eg. combine retail receipts + foot traffic + search traffic to create a special index to predict retail stock prices. Live eg: MongoDB tracker, Crypto Tracker)
(Strategy) Creatively find data on websites and scrape them to predict market moves
✅ Correlation (If A moves, trade B)
(Concept) Understand the statistical methods to test correlations
(Strategy) Use Google search data, job listings and other scrapped data to predict stock and spread movements
(Strategy) Use synthetic assets to predict other synthetic assets
✅ Sentiment and Text analysis (Machine Learning)
(Concept) Evaluate the sentiment of a particular phrase, sentence, paragraph or article
(Strategy) Analyse tons of news articles in different language to find out the market sentiment towards an assetThe Psychology Of Clicking "Buy Now"

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