AlgoTrading101 Courses

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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)

1. 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
2. Programming Basics 1: Variables and Conditional
   • Basics of our coding language (MQL4)
   • Syntax, Variables, Operations and Conditional Expressions
3. 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
4. Uncommon Common Sense. Design Effective And Logical Robots
   • Overview of our Strategy Development Guide
     • Preliminary Research
     • Backtesting
     • Optimisation
     • Live Execution
   • Pros and Cons of an Algo Trading Robot
   • Mathematical Expectations of our robots’ performance
5. 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
6. Programming Basics 2: Loops
   • Learning how to code loops
   • Practice Exercises for Loops
7. Robot 2: Belinda – Utilising Volatility!
   • Our first measure of volatility (ATR)
   • Introducing Belinda, the improved version of Adeline
   • Coding and testing Belinda
8. 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
9. Robot 2A: Belinda Upgraded (No Gambler’s Ruin for Me!)
   • Implementing our bet sizing algorithm in Belinda
10. 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
11. Programming Basics 3: Functions, Time and Self-Learning
    • Learn to learn programming
    • Code errors and debugging
    • Coding Functions
    • Practice Exercises for Functions
12. Relevant Statistics 101!
    • Statistical significance and Law of Large numbers and their role in robot testing
    • Deriving suitable minimum sample size for our backtests
13. 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
14. Programming Basics 4: Arrays And Indicators
    • A look at our mentality towards Indicators
    • Math behind Indicators
    • Coding Arrays and Indicators
15. 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
16. 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
17. Robot 4: Desiree – Trade like the Turtles
    • The history of the Turtle Traders
    • Introducing and coding a simplified turtle strategy
18. 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 rules
19. Add A Twist To Your Orders – Advanced Order Management
    • Breakeven and trailing stops
    • Hiding from your broker – Creating virtual stops and take profit orders
20. Robot 5: Desiree 2.0
21. 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
22. 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
23. Robot 6: Elizabeth
24. Programming Basics 5: Clean Up Your Codes! Simple Is Fast!
    • Clean and robust coding
    • MT4 Global Variables
    • MQL4 Libraries
25. Garbage In, Garbage Out Again. Advanced Data Cleaning (Part 1)
    • Creating custom timeframes
    • Clean data, biased output
26. Excel VBA – Using Excel Magic to Improve our Trading
    • Excel trading game
    • Syntax
    • Conditional statements
    • Loops
27. 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
28. I Like Colors And Shapes – Adding Graphics

1. Creating a Dashboard: Graphics and Labels
2. Creating trendlines and levels

1. Ring Ring! Notify Yourself When Something Goes Wrong (Or Right)
   • Coding smartphone notifications
   • Notify yourself during trade or price events
2. Robot 7: Faye – Semi-Automated Trading
3. Connect with the outside world – Importing and Exporting Data out of our Trading Platform
   • Read and write information to Excel
   • Build a spread logger
4. Programming Basics 6: Trading Platform Nuances
   • Perfecting the little coding details
   • Understanding trading and backtesting nuances
5. 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
6. Walking Forward – Advanced Optimisation
   • Walk Forward Optimisation
   • Performance patterns, consistency and seasonality
   • 3D Parameter space evaluation
7. Trading CFDs
8. Looking Outwards – Trading On External Info and Alternative Data
   • Trading using volume
   • Feeding external data into MT4
   • Trade on external events
9. Robot 8: Gwen
10. 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
11. 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 asset

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