AlgoTrading101 Courses

Courses Overview: AlgoTrading101 Course Syllabus

AlgoTrading101 comprises two primary courses:

1. AT101: Immersive Algorithmic Trading Course
2. PT101: Masterclass in Practical Quantitative Trading with Python

AT101 concentrates on the basics of designing, testing, and executing trading strategies.

PT101 delves into modern and advanced strategies, including:

• Exploring niche markets like Canadian bond STIR futures
• Implementing multi-asset strategies
• Utilizing alternative data
• Conducting web scraping
• Integrating machine learning

AT101: Immersive Algorithmic Trading Course

List of chapters (including learning objectives for each chapter)

Embark on an Exciting Journey!

Understanding Algo Trading Robots, their key features, and code structures Deciphering the traits of a successful Algo Trader Setting up and navigating coding infrastructure/software Programming Fundamentals 1: Variables and Conditional Statements Grasping the basics of our coding language (MQL4) Syntax, Variables, Operations, and Conditional Expressions Building Robot 1: Adeline – Our Inaugural Creation! Comprehending Forex markets, chart interpretation, and basic indicators Collaborative coding of Adeline Testing Adeline using historical data Brief overview of modeling quality Applying Uncommon Common Sense. Crafting Effective and Logical Robots Surveying our Strategy Development Guide Preliminary Research Backtesting Optimization Live Execution Pros and Cons of an Algo Trading Robot Analyzing the mathematical expectations of our robots’ performance Garbage In, Garbage Out. Grasping Data Essentials Exploring data sources and storage Emphasizing the significance of data cleanliness Basic data cleaning procedures Addressing issues like bad ticks, inaccurate testing, and market trickery Programming Fundamentals 2: Loops Learning the art of coding loops Practicing exercises for mastering loops Building Robot 2: Belinda – Embracing Volatility! Introduction to measuring volatility (ATR) Introducing Belinda, an enhanced version of Adeline Coding and testing Belinda Determining Position Sizing and Money Management Understanding trade/bet size (position size) through a coin flip game Designing a bet sizing algorithm based on account size Coding the bet sizing algorithm Upgrading Robot 2A: Belinda (No Gambler’s Ruin for Me!) Implementing the bet sizing algorithm in Belinda Navigating Idea Generation and Expectations Setting expectations for our robots based on resources, personality, skills, lifestyle, and goals Grasping the essence of a trading idea – Proxies and Relationships Exploring sources of trading ideas Surveying different types of strategies Introducing our framework for vetting ideas Strategies to counter big hedge funds Programming Fundamentals 3: Functions, Time, and Self-Learning Mastering the art of learning programming Understanding code errors and debugging Coding functions Practicing exercises for functions Diving into Relevant Statistics 101! Understanding statistical significance and the Law of Large Numbers in robot testing Deriving a suitable minimum sample size for backtests Understanding Robot Behavior and Robustness: Backtesting! Ensuring code accuracy Analyzing types of market conditions Testing for Robustness in various contexts (Period, Timeframe, Seasonal, Instrument) Testing robots across intended and unintended periods Stress testing robots during black swan events Addressing backtest bias via start point selection Evaluating robot performance Programming Fundamentals 4: Arrays and Indicators Examining our mindset towards indicators Understanding the mathematics behind indicators Coding arrays and indicators Building Robot 3: Clarissa – Playing with Time Understanding the Datetime data type Coding rules related to date and time manipulation Introducing and coding Clarissa – our time-centric robot Managing Trades, Orders, and Positions Navigating order limitations imposed by brokers Coding a customized order function Managing multiple orders Modeling transaction costs, spreads, and slippage Building Robot 4: Desiree – Trading like the Turtles Exploring the history of the Turtle Traders Introducing and coding a simplified turtle strategy Design Theories I – Enhancing Robots Through Time, Entries, and Exits Manipulation Analyzing profitability in different timeframes Deriving optimal stop-loss levels Comparing the significance of entries vs exits Analyzing asymmetrical long and short rules Adding a Twist to Orders – Advanced Order Management Exploring breakeven and trailing stops Implementing virtual stops and take profit orders to evade broker scrutiny Building Robot 5: Desiree 2.0 Bolstering Your Robot Responsibly – Optimizing Without Overfitting Understanding objective functions, robustness, and curve fitting Minimizing curve fitting through 10 different approaches Exploring degrees of freedom and parameter robustness Conducting in and out-of-sample testing Evaluating optimization methods Perfecting Bet Sizing – Advanced Position Sizing Techniques Understanding the relationship between sizing and trading frequency Adjusting sizing based on volatility Navigating the Impossible Trinity of Sizing – Leveraging, % Risked, and Stop Loss Relationship Building sizing algorithms based on first principles Exploring other sizing methods – Kelly Criterion, Martingales, and Anti-Martingales Building Robot 6: Elizabeth Programming Fundamentals 5: Simplify Your Codes! Speed Is Key! Emphasizing clean and robust coding Understanding MT4 Global Variables Leveraging MQL4 Libraries Garbage In, Garbage Out Again. Advanced Data Cleaning (Part 1) Creating custom timeframes Ensuring data cleanliness and mitigating biased output Exploring Excel VBA – Utilizing Excel Magic to Enhance Trading Participating in an Excel trading game Coding syntax, conditional statements, and loops in Excel VBA Garbage In, Garbage Out Again. Advanced Data Cleaning (Part 2) Manipulating data time zones Defining criteria for “clean enough” data Scanning for errors in data Implementing advanced data cleaning methodologies Adding Colors And Shapes – Incorporating Graphics Creating a dashboard with graphics and labels Building trendlines and levels visually Ring Ring! Notify Yourself When Something Goes Wrong (Or Right) Coding smartphone notifications Receiving notifications during trade or price events Building Robot 7: Faye – Semi-Automated Trading Connecting with the outside world – Importing and Exporting Data from our Trading Platform Reading and writing information to Excel Building a spread logger Programming Fundamentals 6: Trading Platform Nuances Mastering the finer coding details Understanding trading and backtesting nuances Design Theories II – The “Secret Sauce” Prudence-Behavioral 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 Behavior – Outsmarting the Masses Walking Forward – Advanced Optimization Conducting Walk Forward Optimization Analyzing performance patterns, consistency, and seasonality Evaluating the 3D Parameter space Trading CFDs Looking Outwards – Trading On External Info and Alternative Data Trading using volume data Incorporating external data into MT4 Executing trades based on external events Building Robot 8: Gwen Cash Is King! – Executing Robots With Real Money Contrasting paper versus live trading Determining minimum capital requirements Selecting a suitable broker Setting up Virtual Private Servers Implementing Downtime Prevention Protocols Addressing hedging issues Implementing a Strategy Monitor – Regularly updating our robots Conducting live walk-forward optimization Navigating the Investor Marketplace Monitoring Your Robot(s) Diligently Practicing Operational Risk Management Monitoring robot performance Deciding when to intervene manually Reviewing overall performance Understanding Trading Psychology – Managing Emotions during Drawdowns

PT101: Practical Quantitative Trading with Python Masterclass

(In progress, additional content being added)

Strategies for Modern Markets

Basic Python and Test Strategies

Essential Python skills to kickstart your journey (advanced Python techniques covered later) Developing a simple pair trading test strategy for initial exposure to trading concepts Cointegration (Mean reversion: Betting on the reversion when A and B diverge) (CURRENT FOCUS)

(Concept) Synthetic assets (creating composite assets from different ones) (Strategy) Bond futures calendar spreads and structures (forming composite assets using bond futures) (Strategy) Market making with a proxy asset (entering and exiting trades at bid and ask prices) (Strategy) Statistical Arbitrage. Trading hundreds of stocks in a mean reversion manner. Sentiment Analysis and Web API (Collecting data from websites via special “links”)

(Concept) Using Web API to gather data (e.g., analyzing Google trends for search traffic) (Strategy) Scanning numerous stocks for sudden increases in search traffic volume Alternative Data (Non-price data like Credit card, Location data, etc.)

(Strategy) Utilizing paid alternative data from vendors to analyze stocks (Strategy) Creating a unique index by combining different alternative data (e.g., combining retail receipts + foot traffic + search traffic to predict retail stock prices. Live example: MongoDB tracker, Crypto Tracker) (Strategy) Creatively extracting data from websites and scraping them to predict market moves Correlation (If A moves, trade B)

(Concept) Grasping statistical methods for testing correlations (Strategy) Using Google search data, job listings, and other scraped data to predict stock and spread movements (Strategy) Using synthetic assets to predict other synthetic assets Sentiment and Text Analysis (Machine Learning)

(Concept) Evaluating the sentiment of a particular phrase, sentence, paragraph, or article (Strategy) Analyzing numerous news articles in different languages to gauge market sentiment toward an asset

More courses from this author: AlgoTrading101