Advances in Momentum Trading Strategies

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Hudson and Thames Quantitative Research

5:33:07

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1. Useful Resources

1. Free 1 Month MlFinLab License.html

2. Join the Reading Group.html

2. A Century of Evidence on Trend-Following Investing

1. Introduction to a Century of Evidence.mp4

01:10

2.1 A Century of Evidence on Trend-Following Investing.pdf

2. Annotated Paper A Century of Evidence on Trend-Following Investing.html

3. Types of Momentum Strategies.mp4

01:52

4. Methodology.mp4

10:14

5. Time Series Momentum.mp4

03:19

6. Performance Over a Century.mp4

02:58

7. Performance During Crisis Periods.mp4

02:44

8. Performance in Different Economic Environments.mp4

05:17

3. Momentum Turning Points

1. Introduction to Turning Points.mp4

02:46

2. Paper Momentum Turning Points.html

3. What Are Turning Points.mp4

04:13

4. Defining Slow and Fast.mp4

01:02

5. Slow and Fast Cycles.mp4

05:49

6. The Effect of Noise and Persistence (Signal).mp4

05:14

7. The Model.mp4

02:49

8. Performance.mp4

07:50

9. Beta and Alpha Decomposition.mp4

03:01

10. Dynamic Speed Selection.mp4

06:39

11. Dynamic vs. Static Strategies.mp4

04:02

4. Trending Fast and Slow

1. Paper Trending Fast and Slow.html

2. Introduction to Trending Fast and Slow.mp4

00:18

3. Theory.mp4

04:10

4. Inisghts on the speeds (window periods).mp4

04:14

5. Approaches to Risk Management.mp4

03:54

6. Signal Construction.mp4

03:53

7. Statistics of the S&P 500.mp4

02:06

8. Momentum Under Different Regimes.mp4

10:02

9. Sources of Out-Performance.mp4

05:52

10. Application to the Broader Universe.mp4

05:09

11. Conclusion.mp4

04:11

5. Position Sizing Volatility Targeting

1.1 Annotated The Impact of Volatility Targeting.pdf

1. Annotated Paper The Impact of Volatility Targeting.html

2. Notebook Practical Build your Own Backtest.html

3. Introduction to Volatility Targeting.mp4

03:34

4. Key Concepts.mp4

03:46

5. Findings and Data Sets.mp4

02:44

6. Applying Volatility Targeting (Scaling).mp4

09:12

7. Performance in Equities.mp4

11:19

8. Performance in Bonds and Other Asset Classes.mp4

07:36

9. Why Volatility Targeting Works.mp4

05:58

6. Introduction to Deep Momentum Networks (Time Series Momentum Strategies)

1. Paper Enhancing Time Series Momentum Strategies Using Deep Neural Networks.html

2.1 Github Repo.html

2. Code Deep Momentum Networks.html

3. Introduction to Deep Momentum Networks.mp4

01:38

4. Insights on Momentum Strategies.mp4

03:16

5. Landmark Paper Returns to Buying Winners and Selling Losers.html

6. Construction of Trading Signals.mp4

02:44

7. Loss Function and Architecture.mp4

02:47

8. The Data Used.mp4

02:42

9. Performance Evaluation.mp4

06:58

7. Advanced Deep Momentum Networks with Change Point Detection

1. Paper Slow Momentum with Fast Reversion.html

2. Code Advanced Deep Momentum Networks.html

3. Introduction Deep Mom Networks with Change Point Detection.mp4

02:28

4. Momentum and Mean Reversion.mp4

05:52

5. Change Point Detection.mp4

14:51

6. Methodology.mp4

05:14

7. Results.mp4

13:19

8. Paper Trading with the Momentum Transformer.html

9. Code Momentum Transformer.html

8. Cross-Sectional Momentum Strategies with Learning to Rank

1.1 Building Cross-Sectional Systematic Strategies.pdf

1. Paper Annotated Building Cross-Sectional Systematic Strategies By LTR.html

2. Introduction to Learning to Rank in Trading.mp4

01:58

3. The Oxford MAN Institute.mp4

03:42

4. Cross Sectional Momentum (CSM) Strategies.mp4

02:46

5. Anatomy of a CSM Strategy.mp4

03:55

6. Score Calculation.mp4

05:09

7. What is Learning to Rank.mp4

03:26

8. How to do it in Finance.mp4

05:29

9. Performance Results.mp4

05:23

10. Learning to build a LTR Strategy.mp4

00:57

11.1 Constructing Cross-sectional Systematic Strategies by Learning to Rank.html

11. External Lecture Constructing Cross-sectional Systematic Strategies by LTR.html

12.1 Learning to Rank by Sophie Watson.html

12. External Lecture Learning to Rank by Sophie Watson.html

13. Python Library for LambdaMart Implementation.html

9. Market Conditions that Favor Strategies

1.1 Baz et al with annotations.pdf

1. Annotated Paper Dissecting Investment Strategies in the Cross Section....html

2. Introduction to Favourable Market Conditions.mp4

01:55

3. Key Takeaways.mp4

00:58

4. Carry Strategy.mp4

06:57

5. Momentum Strategy.mp4

02:55

6. Value Strategy.mp4

14:41

7. Important - Valuable Insight! Signal Construction.mp4

02:59

8. Code Use this Code to Create the Momentum Features.html

9. Portfolio Construction.mp4

01:37

10. Results.mp4

07:56

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

1. Paper Enhancing CS Strategies by Context-Aware LTR with Self-Attention.html

2. Introduction to an Advanced LTR Method.mp4

00:57

3. Overview Paper.mp4

03:18

4. Model Overview.mp4

02:46

5. Backtest Method for CSM Strategy.mp4

02:29

6. Enhancing the Ranking.mp4

05:49

7. Context Aware Model and Encodings.mp4

07:04

8. Transformer Architecture.mp4

10:30

9. Experiment Methodology.mp4

02:06

10. Strategy Performance.mp4

04:39

Description

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.
Learn how natural language processing (NLP) is used on news sources to construct sentiment signals and how to build time series momentum strategies.
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.

Who is this for?

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.

What You Need to Know?

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.

More details

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.
Who this course is for:
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.

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Hudson and Thames Quantitative Research is a company known for its contributions to the field of quantitative finance, particularly in the development and application of advanced mathematical and statistical methods to financial data. Their work typically involves the creation of algorithms and models to facilitate investment decisions, risk management, and trading strategies.

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