Advances in Momentum Trading Strategies

Advances in Momentum Trading Strategies offers an in-depth exploration of momentum-based trading methodologies, teaching students to identify, construct, and manage systematic momentum portfolios. The course covers theoretical foundations, practical strategy development, rigorous backtesting, risk management, and performance optimization across various market conditions.

Created by Mohsen Hassan
Last updated 05/2026
English
$29.00
$84.99
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What you'll learn

Understand the theoretical foundations of momentum trading and why it works in financial markets.
Identify high-momentum stocks and assets using technical indicators and market data.
Build and backtest momentum trading strategies using historical price data.
Apply risk management techniques to protect capital in momentum trades.
Optimize entry and exit points to maximize returns from momentum trades.
Evaluate performance metrics to measure strategy effectiveness and profitability.
Adapt momentum strategies across different market conditions and timeframes.
Implement advanced momentum techniques including factor rotation and portfolio construction.

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This course includes:

6.09 hours on-demand video
75 videos
6 documents
2.2 GB downloadable resources
Access on mobile and PC
Instant access after payment

Course content

Expand all sections
  • 1. Free 1 Month MlFinLab License
    01:00
  • 2. Join the Reading Group
    01:00
  • 1. Lecture Series by AHL on CTA Momentum Strategies
    01:00
  • 1. Introduction to a Century of Evidence
    01:10
  • 1. Introduction to a Century of Evidence
    01:00
  • 2.1 A Century of Evidence on Trend-Following Investing
    05:00
  • 2. Annotated Paper A Century of Evidence on Trend-Following Investing
    01:00
  • 3. Types of Momentum Strategies
    01:52
  • 3. Types of Momentum Strategies
    01:00
  • 4. Methodology
    10:14
  • 4. Methodology
    01:00
  • 5. Time Series Momentum
    03:20
  • 5. Time Series Momentum
    01:00
  • 6. Performance Over a Century
    02:59
  • 6. Performance Over a Century
    01:00
  • 7. Performance During Crisis Periods
    02:45
  • 7. Performance During Crisis Periods
    01:00
  • 8. Performance in Different Economic Environments
    05:17
  • 8. Performance in Different Economic Environments
    01:00
  • 1. Introducing Man AHL
    01:00
  • 2. Podcast - The AHL Story Top Traders Unplugged
    01:00
  • 1. Introduction to Turning Points
    02:46
  • 1. Introduction to Turning Points
    01:00
  • 2. Paper Momentum Turning Points
    01:00
  • 3. What Are Turning Points
    04:13
  • 3. What Are Turning Points
    01:00
  • 4. Defining Slow and Fast
    01:03
  • 4. Defining Slow and Fast
    01:00
  • 5. Slow and Fast Cycles
    05:49
  • 5. Slow and Fast Cycles
    01:00
  • 6. The Effect of Noise and Persistence (Signal)
    05:15
  • 6. The Effect of Noise and Persistence (Signal)
    01:00
  • 7. The Model
    02:50
  • 7. The Model
    01:00
  • 8. Performance
    07:50
  • 8. Performance
    01:00
  • 9. Beta and Alpha Decomposition
    03:02
  • 9. Beta and Alpha Decomposition
    01:00
  • 10. Dynamic Speed Selection
    06:40
  • 10. Dynamic Speed Selection
    01:00
  • 11. Dynamic vs. Static Strategies
    04:02
  • 11. Dynamic vs. Static Strategies
    01:00
  • 12. Case Study Why the Achilles Heel of Momentum Strategies Poses a Risk
    01:00
  • 1. Introduction to Winton Capital
    01:00
  • 2. David Harding Talks to Bloomberg
    01:00
  • 1. Paper Trending Fast and Slow
    01:00
  • 2. Introduction to Trending Fast and Slow
    00:18
  • 2. Introduction to Trending Fast and Slow
    01:00
  • 3. Theory
    04:10
  • 3. Theory
    01:00
  • 4. Inisghts on the speeds (window periods)
    04:14
  • 4. Inisghts on the speeds (window periods)
    01:00
  • 5. Approaches to Risk Management
    03:55
  • 5. Approaches to Risk Management
    01:00
  • 6. Signal Construction
    03:53
  • 6. Signal Construction
    01:00
  • 7. Statistics of the S&P 500
    02:06
  • 7. Statistics of the S&P 500
    01:00
  • 8. Momentum Under Different Regimes
    10:03
  • 8. Momentum Under Different Regimes
    01:00
  • 9. Sources of Out-Performance
    05:53
  • 9. Sources of Out-Performance
    01:00
  • 10. Application to the Broader Universe
    05:10
  • 10. Application to the Broader Universe
    01:00
  • 11. Conclusion
    04:11
  • 11. Conclusion
    01:00
  • 12.1 Paid to be Paranoid Trending Fast and Slow
    05:00
  • 12. Case Study Paid to be Paranoid Trending, Fast and Slow
    01:00
  • 1. The Hedge Fund Journal CTA and Discretionary Trader Awards 2023
    01:00
  • 1.1 Annotated The Impact of Volatility Targeting
    05:00
  • 1. Annotated Paper The Impact of Volatility Targeting
    01:00
  • 2. Notebook Practical Build your Own Backtest
    01:00
  • 3. Introduction to Volatility Targeting
    03:35
  • 3. Introduction to Volatility Targeting
    01:00
  • 4. Key Concepts
    03:46
  • 4. Key Concepts
    01:00
  • 5. Findings and Data Sets
    02:45
  • 5. Findings and Data Sets
    01:00
  • 6. Applying Volatility Targeting (Scaling)
    09:13
  • 6. Applying Volatility Targeting (Scaling)
    01:00
  • 7. Performance in Equities
    11:20
  • 7. Performance in Equities
    01:00
  • 8. Performance in Bonds and Other Asset Classes
    07:37
  • 8. Performance in Bonds and Other Asset Classes
    01:00
  • 9. Why Volatility Targeting Works
    05:58
  • 9. Why Volatility Targeting Works
    01:00
  • 10. Case Study Volatility is Back - Better to Target Returns or Target Risk
    01:00
  • 11. Case Study We See Risk Where Others May Not
    01:00
  • 1. Introduction to RavenPack
    01:00
  • 2.1 RavenPack-White-Paper-Harnessing-News-Sentiment-for-FX-Futures-Strategies_147D20D27 ANNOTATED
    05:00
  • 2. Case Study Harnessing News Sentiment for FX Futures Strategies by RavenPack
    01:00
  • 3. Introduction to News Sentiment
    01:20
  • 3. Introduction to News Sentiment
    01:00
  • 4. Methodology - Trend Following and Mean Reversion
    17:08
  • 4. Methodology - Trend Following and Mean Reversion
    01:00
  • 5. Results
    10:48
  • 5. Results
    01:00
  • 6. Conclusion
    02:20
  • 6. Conclusion
    01:00
  • 1. Introducing the Research Group and their Industry Partners
    01:00
  • 1. Paper Enhancing Time Series Momentum Strategies Using Deep Neural Networks
    01:00
  • 2.1 Github Repo
    01:00
  • 2. Code Deep Momentum Networks
    01:00
  • 3. Introduction to Deep Momentum Networks
    01:39
  • 3. Introduction to Deep Momentum Networks
    01:00
  • 4. Insights on Momentum Strategies
    03:16
  • 4. Insights on Momentum Strategies
    01:00
  • 5. Landmark Paper Returns to Buying Winners and Selling Losers
    01:00
  • 6. Construction of Trading Signals
    02:45
  • 6. Construction of Trading Signals
    01:00
  • 7. Loss Function and Architecture
    02:48
  • 7. Loss Function and Architecture
    01:00
  • 8. The Data Used
    02:42
  • 8. The Data Used
    01:00
  • 9. Performance Evaluation
    06:59
  • 9. Performance Evaluation
    01:00
  • 10. OMI Lecture on Deep Momentum Networks
    01:00
  • 1. Paper Slow Momentum with Fast Reversion
    01:00
  • 2. Code Advanced Deep Momentum Networks
    01:00
  • 3. Introduction Deep Mom Networks with Change Point Detection
    02:29
  • 3. Introduction Deep Mom Networks with Change Point Detection
    01:00
  • 4. Momentum and Mean Reversion
    05:53
  • 4. Momentum and Mean Reversion
    01:00
  • 5. Change Point Detection
    14:52
  • 5. Change Point Detection
    01:00
  • 6. Methodology
    05:14
  • 6. Methodology
    01:00
  • 7. Results
    13:20
  • 7. Results
    01:00
  • 8. Paper Trading with the Momentum Transformer
    01:00
  • 9. Code Momentum Transformer
    01:00
  • 1.1 Building Cross-Sectional Systematic Strategies
    05:00
  • 1. Paper Annotated Building Cross-Sectional Systematic Strategies By LTR
    01:00
  • 2. Transfer Ranking in Finance by Daniel Poh
    01:00
  • 3. Introduction to Learning to Rank in Trading
    01:58
  • 3. Introduction to Learning to Rank in Trading
    01:00
  • 4. The Oxford MAN Institute
    03:42
  • 4. The Oxford MAN Institute
    01:00
  • 5. Cross Sectional Momentum (CSM) Strategies
    02:47
  • 5. Cross Sectional Momentum (CSM) Strategies
    01:00
  • 6. Anatomy of a CSM Strategy
    03:56
  • 6. Anatomy of a CSM Strategy
    01:00
  • 7. Score Calculation
    05:09
  • 7. Score Calculation
    01:00
  • 8. What is Learning to Rank
    03:27
  • 8. What is Learning to Rank
    01:00
  • 9. How to do it in Finance
    05:30
  • 9. How to do it in Finance
    01:00
  • 10. Performance Results
    05:24
  • 10. Performance Results
    01:00
  • 11. Learning to build a LTR Strategy
    00:58
  • 11. Learning to build a LTR Strategy
    01:00
  • 12.1 Constructing Cross-sectional Systematic Strategies by Learning to Rank
    01:00
  • 12. External Lecture Constructing Cross-sectional Systematic Strategies by LTR
    01:00
  • 13.1 u201cLearning to Ranku201d by Sophie Watson
    01:00
  • 13. External Lecture Learning to Rank by Sophie Watson
    01:00
  • 14. Python Library for LambdaMart Implementation
    01:00
  • 1.1 Baz et al with annotations
    05:00
  • 1. Annotated Paper Dissecting Investment Strategies in the Cross Section...
    01:00
  • 2. Introduction to Favourable Market Conditions
    01:55
  • 2. Introduction to Favourable Market Conditions
    01:00
  • 3. Key Takeaways
    00:59
  • 3. Key Takeaways
    01:00
  • 4. Carry Strategy
    06:57
  • 4. Carry Strategy
    01:00
  • 5. Momentum Strategy
    02:55
  • 5. Momentum Strategy
    01:00
  • 6. Value Strategy
    14:41
  • 6. Value Strategy
    01:00
  • 7. Important - Valuable Insight! Signal Construction
    01:00
  • 7. Important - Valuable Insight! Signal Construction
    01:00
  • 8. Code Use this Code to Create the Momentum Features
    01:00
  • 9. Portfolio Construction
    01:38
  • 9. Portfolio Construction
    01:00
  • 10. Results
    07:57
  • 10. Results
    01:00
  • 1. Paper Enhancing CS Strategies by Context-Aware LTR with Self-Attention
    01:00
  • 2. Introduction to an Advanced LTR Method
    00:57
  • 2. Introduction to an Advanced LTR Method
    01:00
  • 3. Overview Paper
    03:19
  • 3. Overview Paper
    01:00
  • 4. Model Overview
    02:46
  • 4. Model Overview
    01:00
  • 5. Backtest Method for CSM Strategy
    02:30
  • 5. Backtest Method for CSM Strategy
    01:00
  • 6. Enhancing the Ranking
    05:50
  • 6. Enhancing the Ranking
    01:00
  • 7. Context Aware Model and Encodings
    07:05
  • 7. Context Aware Model and Encodings
    01:00
  • 8. Transformer Architecture
    10:31
  • 8. Transformer Architecture
    01:00
  • 9. Experiment Methodology
    02:07
  • 9. Experiment Methodology
    01:00
  • 10. Strategy Performance
    04:39
  • 10. Strategy Performance
    01:00

Requirements

  • Basic understanding of stock markets and trading concepts.
  • Familiarity with technical analysis and chart reading is helpful but not required.
  • Access to a computer with internet connection for analysis and research.
  • Interest in quantitative trading strategies and systematic approaches to investing.

Description

Advances in Momentum Trading Strategies provides a comprehensive exploration of momentum-based trading methodologies that capitalize on the tendency of assets to continue moving in their established direction. This course bridges theoretical concepts with practical implementation, guiding learners through the process of identifying, constructing, and managing momentum portfolios across various market environments.

The learning journey begins with a thorough examination of momentum as a market phenomenon. Students explore the academic research and empirical evidence supporting momentum effects, understanding why certain assets exhibit persistent price trends and how behavioral finance explains these patterns. This foundation establishes the rationale behind momentum strategies and sets the stage for practical application.

Building on theoretical knowledge, the course progresses into identification techniques for momentum opportunities. Learners discover how to screen markets for stocks and assets displaying strong relative strength, using both absolute and cross-sectional momentum measures. The curriculum covers various lookback periods, ranking methodologies, and filtering criteria that help isolate the most promising momentum candidates. Students learn to distinguish between genuine momentum signals and false patterns that lead to poor trading outcomes.

The strategy construction phase forms the core practical component of the course. Students work through the process of designing complete momentum systems, from initial concept through detailed specification. This includes defining universe selection criteria, establishing ranking mechanisms, determining portfolio concentration levels, and setting position sizing rules. The course emphasizes the importance of systematic approaches that remove emotional decision-making from the trading process.

Backtesting forms a critical element of strategy development covered extensively in the curriculum. Learners understand how to properly test momentum strategies using historical data while avoiding common pitfalls such as look-ahead bias, survivorship bias, and overfitting. The course teaches proper data handling, realistic transaction cost assumptions, and robust testing methodologies that provide reliable estimates of strategy performance. Students learn to interpret backtest results critically, understanding both the capabilities and limitations of historical simulation.

Risk management receives dedicated attention as an essential component of successful momentum trading. The course explores position-level risk controls including stop-loss placement, profit targets, and time-based exits. Portfolio-level risk management covers diversification across sectors and factors, correlation analysis, and overall exposure limits. Students learn to balance the pursuit of returns with capital preservation, understanding that consistent profitability requires disciplined risk control.

Performance evaluation and optimization techniques help traders refine their approaches over time. The curriculum covers key metrics including Sharpe ratio, maximum drawdown, win rate, and profit factor. Students learn to analyze equity curves, identify periods of underperformance, and diagnose potential issues in strategy implementation. The course emphasizes continuous improvement through systematic review and adaptation rather than constant strategy switching.

Advanced topics extend beyond basic momentum concepts into sophisticated implementation methods. The course covers factor integration, showing how momentum can be combined with value, quality, and other factors to enhance returns and reduce volatility. Students explore sector rotation strategies, relative momentum across asset classes, and dynamic portfolio weighting schemes. Time series versus cross-sectional momentum approaches are compared, helping traders understand which methodology suits different market structures.

Market regime analysis equips learners with tools to adapt momentum strategies to changing conditions. The course examines how momentum behaves differently during trending markets, range-bound periods, and high-volatility environments. Students learn to recognize regime shifts and adjust their approach accordingly, whether through parameter changes, exposure reduction, or temporary strategy suspension.

The practical implementation section addresses real-world considerations that bridge theory and actual trading. This includes broker selection, order execution strategies, tax efficiency considerations, and the psychological challenges of following systematic rules during drawdown periods. The course provides realistic expectations about strategy performance, helping traders prepare mentally for the inevitable periods of underperformance that all strategies experience.

Throughout the curriculum, emphasis is placed on developing a systematic, disciplined approach to momentum trading. Students learn to think probabilistically rather than seeking certainty, understanding that successful trading involves managing portfolios of bets with positive expected value rather than predicting individual outcomes. The course cultivates the analytical mindset and methodological rigor necessary for long-term trading success.

Who this course is for:

Advances in Momentum Trading Strategies is designed for active traders seeking systematic approaches to capitalize on price trends, quantitative analysts wanting to expand their strategy toolkit, individual investors interested in evidence-based trading methodologies, and finance professionals looking to understand momentum factor investing and its practical applications in portfolio management.

Instructor

Mohsen Hassan
Quantitative Trader and Financial Analyst
Mohsen Hassan

About Me

I am a quantitative trader and financial analyst specializing in systematic trading strategies and data-driven investment approaches. My professional journey has been dedicated to understanding market dynamics through rigorous analysis and developing robust trading methodologies that perform consistently across different market environments.

My background combines formal education in finance and mathematics with extensive practical experience in developing and implementing algorithmic trading systems. I have spent years researching factor-based investing, momentum strategies, and quantitative portfolio construction techniques. This research-driven approach forms the foundation of my trading philosophy, which emphasizes evidence-based decision making over market speculation.

Throughout my career, I have focused on bridging the gap between academic finance research and practical trading implementation. I believe that many valuable insights from financial economics remain underutilized by individual traders due to the complexity of academic literature and the challenges of translating theory into executable strategies. My work centers on making these concepts accessible and actionable for traders at various skill levels.

I have developed expertise across multiple aspects of systematic trading, including strategy backtesting, risk management, performance analysis, and market microstructure. My approach emphasizes realistic assumptions, proper statistical methodology, and awareness of the many pitfalls that can invalidate backtest results. I value intellectual honesty about the limitations of any trading approach and the importance of continuous learning and adaptation.

My trading philosophy is grounded in probabilistic thinking rather than prediction. I focus on identifying persistent market patterns supported by empirical evidence and behavioral explanations, then constructing diversified portfolios of trades with positive expected value. I believe successful trading requires discipline, systematic processes, and realistic expectations about both returns and inevitable periods of drawdown.

Beyond technical skills, I emphasize the psychological aspects of trading, understanding that even the best strategy fails without the discipline to follow it consistently. My goal is to help traders develop not just technical competence but also the mental framework necessary for long-term success in financial markets.

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