Advances In Momentum Trading Strategies

Delve Deep into the World of Advanced Momentum Trading

What you'll learn

Master Momentum Profits: Explore a century of profitable trend-following strategies and their evolution.

Unlock Momentum Turning Points: Learn to detect and profit from key market changes.

Exploit different volatility regimes, to dynamically swap between fast and slow parameters, to increase profits and improve the sharpe ratio.

Smart Position Sizing: Use Volatility Targeting to enhance Sharpe Ratio and returns across assets.

Deep Momentum Strategies: Discover advanced time-series tactics using deep learning.

Rank with Precision: Apply Learning to Rank algorithms for superior cross-sectional momentum strategies.

Forecast with Insight: Integrate crucial features into ML models for more accurate market predictions.

Requirements

Python Proficiency: Comfort with Python programming is key, as it's our primary tool for analysis and strategy development.

Market Savvy: A solid understanding of financial markets and trading principles will help you navigate the course content more effectively.

Mathematical Fluency: A strong ability to read and understand mathematical equations is crucial for grasping advanced concepts.

Foundation in Math & Statistics: Robust skills in linear algebra and statistics are essential, as they form the backbone of our trading strategies.

Description

Advances in Momentum Trading Strategies is a comprehensive and in-depth course designed for graduate-level students and seasoned professionals. This course offers a unique blend of theory, practical application, and cutting-edge research, enabling participants to master the intricacies of momentum trading across various market conditions.Course Sections:A Century of Evidence on Trend-Following Investing: Explore the historical performance and methodology of trend-following strategies over a century, including during crises and different economic environments.Momentum Turning Points: Unravel the concept of turning points in momentum trading. Learn about dynamic versus static strategies, and the impact of noise and persistence on signal quality.Trending Fast and Slow: Delve into the theory and application of varying speed (window periods) in trend analysis. Discover the role of risk management and the statistics of S&P 500 in momentum strategies.Position Sizing: Volatility Targeting: Understand the impact of volatility targeting on position sizing across asset classes, and why this approach is effective.Deep Momentum Networks (Time Series Momentum Strategies): Learn about enhancing time-series momentum strategies using deep neural networks, including the construction of trading signals and performance evaluation.Advanced Deep Momentum Networks with Change Point Detection: Explore the integration of change point detection in deep momentum networks, examining methodology and results.Cross-Sectional Momentum Strategies with Learning to Rank: Gain insights into building cross-sectional systematic strategies using Learning to Rank (LTR), including Python library implementation for LambdaMart.Market Conditions that Favor Strategies: Analyze various investment strategies like carry, momentum, and value in different market conditions. Learn about signal and portfolio construction.Enhancing Cross-Sectional Strategies by Context-Aware LTR with Self-Attention: Understand how to enhance ranking in cross-sectional momentum strategies using context-aware models and transformer architecture.Why This Course?Whether you're a graduate student specializing in financial engineering, machine learning, applied mathematics, or a professional quant trader or analyst, this course will elevate your understanding and application of momentum trading strategies. It's not just a course; it's an investment in your future in the dynamic world of trading.

Overview

Section 1: Useful Resources

Lecture 1 Free 1 Month: MlFinLab License

Lecture 2 Join the Reading Group

Section 2: A Century of Evidence on Trend-Following Investing

Lecture 3 Introduction to a Century of Evidence

Lecture 4 Annotated Paper: A Century of Evidence on Trend-Following Investing

Lecture 5 Types of Momentum Strategies

Lecture 6 Methodology

Lecture 7 Time Series Momentum

Lecture 8 Performance Over a Century

Lecture 9 Performance During Crisis Periods

Lecture 10 Performance in Different Economic Environments

Section 3: Momentum Turning Points

Lecture 11 Introduction to Turning Points

Lecture 12 Paper: Momentum Turning Points

Lecture 13 What Are Turning Points?

Lecture 14 Defining Slow and Fast

Lecture 15 Slow and Fast Cycles

Lecture 16 The Effect of Noise and Persistence (Signal)

Lecture 17 The Model

Lecture 18 Performance

Lecture 19 Beta and Alpha Decomposition

Lecture 20 Dynamic Speed Selection

Lecture 21 Dynamic vs. Static Strategies

Section 4: Trending Fast and Slow

Lecture 22 Paper: Trending Fast and Slow

Lecture 23 Introduction to Trending Fast and Slow

Lecture 24 Theory

Lecture 25 Inisghts on the speeds (window periods)

Lecture 26 Approaches to Risk Management

Lecture 27 Signal Construction

Lecture 28 Statistics of the S&P 500

Lecture 29 Momentum Under Different Regimes

Lecture 30 Sources of Out-Performance

Lecture 31 Application to the Broader Universe

Lecture 32 Conclusion

Section 5: Position Sizing: Volatility Targeting

Lecture 33 Annotated Paper: The Impact of Volatility Targeting

Lecture 34 Notebook Practical: Build your Own Backtest

Lecture 35 Introduction to Volatility Targeting

Lecture 36 Key Concepts

Lecture 37 Findings and Data Sets

Lecture 38 Applying Volatility Targeting (Scaling)

Lecture 39 Performance in Equities

Lecture 40 Performance in Bonds and Other Asset Classes

Lecture 41 Why Volatility Targeting Works

Section 6: Introduction to Deep Momentum Networks (Time Series Momentum Strategies)

Lecture 42 Paper: Enhancing Time Series Momentum Strategies Using Deep Neural Networks

Lecture 43 Code: Deep Momentum Networks

Lecture 44 Introduction to Deep Momentum Networks

Lecture 45 Insights on Momentum Strategies

Lecture 46 Landmark Paper: Returns to Buying Winners and Selling Losers

Lecture 47 Construction of Trading Signals

Lecture 48 Loss Function and Architecture

Lecture 49 The Data Used

Lecture 50 Performance Evaluation

Section 7: Advanced Deep Momentum Networks with Change Point Detection

Lecture 51 Paper: Slow Momentum with Fast Reversion

Lecture 52 Code: Advanced Deep Momentum Networks

Lecture 53 Introduction: Deep Mom Networks with Change Point Detection

Lecture 54 Momentum and Mean Reversion

Lecture 55 Change Point Detection

Lecture 56 Methodology

Lecture 57 Results

Lecture 58 Paper: Trading with the Momentum Transformer

Lecture 59 Code: Momentum Transformer

Section 8: Cross-Sectional Momentum Strategies with Learning to Rank

Lecture 60 Paper Annotated: Building Cross-Sectional Systematic Strategies By LTR

Lecture 61 Introduction to Learning to Rank in Trading

Lecture 62 The Oxford MAN Institute

Lecture 63 Cross Sectional Momentum (CSM) Strategies

Lecture 64 Anatomy of a CSM Strategy

Lecture 65 Score Calculation

Lecture 66 What is Learning to Rank?

Lecture 67 How to do it in Finance?

Lecture 68 Performance Results

Lecture 69 Learning to build a LTR Strategy

Lecture 70 External Lecture: Constructing Cross-sectional Systematic Strategies by LTR

Lecture 71 External Lecture: Learning to Rank by Sophie Watson

Lecture 72 Python Library for LambdaMart Implementation

Section 9: Market Conditions that Favor Strategies

Lecture 73 Annotated Paper: Dissecting Investment Strategies in the Cross Section…

Lecture 74 Introduction to Favourable Market Conditions

Lecture 75 Key Takeaways

Lecture 76 Carry Strategy

Lecture 77 Momentum Strategy

Lecture 78 Value Strategy

Lecture 79 Important - Valuable Insight! Signal Construction

Lecture 80 Code: Use this Code to Create the Momentum Features

Lecture 81 Portfolio Construction

Lecture 82 Results

Section 10: Enhancing Cross-Sectional Strategies by Context-Aware LTR with Self-Attention

Lecture 83 Paper: Enhancing CS Strategies by Context-Aware LTR with Self-Attention

Lecture 84 Introduction to an Advanced LTR Method

Lecture 85 Overview Paper

Lecture 86 Model Overview

Lecture 87 Backtest Method for CSM Strategy

Lecture 88 Enhancing the Ranking

Lecture 89 Context Aware Model and Encodings

Lecture 90 Transformer Architecture

Lecture 91 Experiment Methodology

Lecture 92 Strategy Performance

This course is NOT for beginners! Its an advanced course aimed at graduate level students and industry professionals.,Ambitious Graduate Students: Particularly those in Machine Learning, Applied Mathematics, Financial Engineering, and Computer Science, looking for a challenge.,Aspiring Quant Traders and Analysts: If you're eager to craft your own momentum-based trading strategies, this course is your launchpad.,Experienced Traders: Enhance your skill set with in-depth knowledge of cross-sectional and time-series momentum strategies.