AlgoTrading101 Courses Collection | 7.05 GB
About Courses: Course Syllabus
AlgoTrading101 offers two primary courses:

AT101: Algorithmic Trading Immersive Course
PT101: Practical Quantitative Trading with Python Masterclass
AT101: Algorithmic Trading Immersive Course
This course delves into the fundamentals of designing, testing, and executing trading strategies. It covers everything from setting up your infrastructure to creating and optimizing trading robots. The following is a chapter breakdown with key learning objectives:

Introduction to Algo Trading: Explore what an algo trading robot is, its key characteristics, and the necessary code structure. Learn what makes a successful algorithmic trader and how to navigate coding software.
Programming Basics: Start with variables and conditional statements in MQL4. Learn to code and test your first robot, Adeline, in the Forex market using past data.
Strategy Development: Understand strategy development through preliminary research, backtesting, optimization, and live execution. Learn the importance of data cleanliness and storage.
Robot 2: Belinda: Build on the fundamentals by introducing Belinda, a robot that leverages volatility indicators. Understand trade sizing and bet-sizing algorithms to prevent “Gambler’s Ruin.”
Idea Generation: Learn to generate trading ideas based on resources, goals, and strategies. Grade ideas using a systematic framework and discover ways to compete against large hedge funds.
Advanced Coding Concepts: Deep dive into loops, functions, arrays, and indicators. Apply these to create advanced robots like Clarissa, which uses time entries, and Desiree, based on Turtle Trading strategies.
Order Management: Manage multiple orders, transaction costs, and develop advanced order strategies such as breakeven and trailing stops. Create virtual stops to hide from brokers.
Robot Optimization: Minimize curve fitting through robust testing and optimization techniques. Master advanced bet sizing methods like Kelly Criterion and Anti-Martingales.
Advanced Data Cleaning: Improve your trading data through custom timeframes, error scanning, and advanced cleaning techniques.
Visualization and Notifications: Add graphics to your robots, create dashboards, and set up smartphone notifications for real-time updates on trading events.
Semi-Automated Trading: Combine manual and automated strategies with robots like Faye, and learn how to integrate external data sources to enhance trading decisions.
Operational Risk Management: Implement protocols to minimize downtime, monitor robot performance, and manage the psychological aspects of trading during drawdowns.
Final Project: Execute a fully optimized trading robot in real market conditions, with ongoing performance review and strategic adjustments.
PT101: Practical Quantitative Trading with Python Masterclass
This course focuses on modern quantitative trading strategies using Python. It covers everything from basic Python programming to advanced strategies such as statistical arbitrage and sentiment analysis.

Introduction to Python: Learn the basics of Python programming to build and test simple pair trading strategies.
Advanced Strategies: Explore strategies like bond futures calendar spreads, market-making using proxy assets, and trading using alternative data such as credit card and location data.
Web Scraping and Sentiment Analysis: Collect data from websites using Web APIs and perform sentiment analysis on news articles to gauge market sentiment.
Machine Learning Applications: Leverage machine learning techniques to analyze text and news sentiment, and develop strategies based on statistical correlations.
This syllabus provides a comprehensive roadmap for both beginners and advanced traders to master algorithmic trading and quantitative strategies with hands-on practical applications.

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