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In the dynamic trading realm, the edge goes to those who constantly evolve, learn and adapt. AlgoTrading101 is meticulously designed to provide traders with that edge.
Whether you’re just setting sail in the trading world or looking to sharpen your existing skills, this course offers a structured path to mastering the art and science of algorithmic trading. Don’t just trade; trade bright with AlgoTrading101.
AlgoTrading101 delivers a comprehensive, step-by-step foray into the intricate world of algorithmic trading. The course, streamlined for both beginners and intermediate traders, is partitioned into two main segments:
- AT101: Algorithmic Trading Immersive Course
- PT101: Practical Quantitative Trading with Python Masterclass
What You’ll Learn In AlgoTrading101 Courses
AT101: A Deep Dive into Algorithmic Trading
AT101 demystifies the foundational pillars of trading strategy – from its conceptualization, testing, and final execution. Here’s a sneak peek into the expansive curriculum:
- The Algo Trading Primer: Grasp the mechanics behind Algo Trading Robots, explore Forex markets, and dabble with your first robot – Adeline!
- Beyond Basics: Delve into strategy development, uncover the secrets of data management, and challenge yourself with hands-on coding exercises.
- Advanced Strategies: Meet Belinda and Clarissa, robots that teach you the nuances of volatility and time-based trading.
- Robust Trading Foundations: Arm yourself with advanced position sizing techniques, backtesting insights, and knowledge of the essential statistics to ensure you’re always one step ahead in the trading game.
- The Finishing Touches: Enhance your trading strategies with Advanced Order Management, immerse yourself in coding best practices, and understand the art and science of algorithmic trading with real money.
PT101: Quantitative Trading, Supercharged with Python
While AT101 establishes the groundwork, PT101 propels you into the cutting-edge strategies and techniques employed by today’s trading elite:
- Python for Traders: Kickstart with just enough Python to spark your journey. Design a rudimentary pair trading strategy to gauge the course’s tempo.
- The Mean Reversion Magic: Dive into concepts like synthetic assets, bond futures, and statistical arbitrage.
- Harness the Power of Data: Learn to mine web data using APIs and employ alternative data to sculpt unparalleled trading strategies.
- The Correlation Game: Decipher the statistical methodologies behind correlations and leverage them for proactive trading.
- Machine Learning Meets Trading: Venture into sentiment and text analysis. Use machine learning to extract actionable insights from vast swathes of news articles.
We don’t give you “sure-win” black boxes.
We teach you the methods to craft effective strategies.
Code Your First Robot in Three Days
Afraid of coding? Most of our members code their first (simple) robot within three days.
No prior trading or coding experience required.
If you’re still stuck, we’ll be here to drag you out of the coding rut.
Master The 6 Major Skill Sets You Need As An Algorithmic Trader
Trading Robot Design
Use market logic and statistical methods to build effective trading robots. Know why your robots work or break down.
Market Theories
Interpret and understand market behavior. No prior finance or trading knowledge required for our programme.
Coding
Learn the coding skills required from scratch. No prior coding experience required. We use Python, MQL4, MQL5 and VBA.
Data Handling
Garbage in = garbage out. Inaccurate data leads to inaccurate results. Learn appropriate data management skills to ensure accurate testing.
Risk Management
We look at market and operational risk. Maximise upsides while minimising downsides. Prevent system crashes, downtime and theft by hackers.
Live Execution
Backtesting and live trading are very different. Select proper brokers, infrastructures and evaluation procedures to manage your robot throughout its lifetime.
About Courses:
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
- Backtesting
- Optimisation
- Live Execution
- Pros and Cons of an Algo Trading Robot
- Mathematical Expectations of our robots’ performance
- Overview of our Strategy Development Guide
- 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 rules
- 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
- Syntax
- Conditional statements
- Loops
- 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 asset
