Real world Quantitative Trading with Python – Momentum and Mean Reversion models – Jupyter Notebooks included
What you will learn
Understand the concept of market efficiency and some behavioral biases that explain some market inefficiencies.
Build different algorithms to trade in the markets. You will learn both, momentum and mean reversion strategies and also a factor model.
Separate the data in two sets: In sample data and out of the sample data to build backtesting methodologies.
Learn optimization techniques to find the best parameters for your models.
Why take this course?
🚀 Algorithmic Trading with Python: Real-World Quantitative Trading Techniques
Are you ready to dive into the world of quantitative trading and harness the power of Python to build robust algorithmic trading models? Our Algorithmic Trading with Python course is tailored for traders and investors with a quantitative bent, looking to master momentum and mean reversion models. 📊
Course Overview:
This isn’t your average algorithmic trading course. While others focus on basic technical indicators like MACD or Bollinger Bands, our program is designed to equip you with successful real-world trading models. If you’re comfortable with Python and eager to elevate your trading strategy, click the enroll button now and embark on a journey to master quantitative trading.
Course Curriculum:
🎓 Topic 1: Understanding Market Efficiency & Behavioral Biases
- Explore the concept of market efficiency and its limitations.
- Identify behavioral biases that affect market outcomes.
- Perform a simple test to assess market efficiency.
🚀 Topic 2: First Momentum Model – Alexander’s Filter
- Learn about the trend-following system introduced by an MIT professor, later refined by professional traders.
- Conduct Python optimizations to fine-tune model parameters for optimal performance.
- Implement Alexander’s Filter and understand its implications in trading.
🐢 Topic 3: Second Momentum Model – Breakout Model
- Inspired by the highly profitable “Turtle Trader” rule, this break out model uses a logistic regression to predict market trends.
- Discover how to apply logistic regression for effective trend prediction.
- Apply the breakout model in Python to gain insight into market direction.
📈 Topic 4: Mean Reversion Model – Pairs Trading
- Delve into pairs trading, a mean reverting strategy that exploits pricing anomalies between related assets.
- Learn how to select and pair assets for a robust mean reversion strategy.
🔬 Topic 5: Factor Model Approach
- Understand the process of selecting factors that can drive successful trading strategies, as used in high-frequency trading.
- Apply your knowledge to build a factor model with Python.
- Learn how to integrate explanatory variables into a comprehensive trading strategy.
🎢 Topic 6: Final Remarks & Practical Insights
- Explore the Kelly Criterion for determining optimal trade size.
- Review backtesting methods and understand their importance in refining trading algorithms.
What’s Included:
- Expert Instruction: Learn from industry professionals with real-world trading experience.
- Hands-On Learning: Implement models using Jupyter Notebooks included with the course.
- Real-World Application: Build models that can be applied directly to markets.
- Community Access: Join a community of like-minded traders and investors.
- Lifetime Access: Revisit lectures and resources anytime, anywhere.
Don’t miss this opportunity to transform your trading with Python! Enroll today and unlock the full potential of algorithmic trading. 🌟