What you'll learn
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This course includes:
Course content
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1. Free 1 Month MlFinLab License01:00
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2. Join the Reading Group01:00
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1. Lecture Series by AHL on CTA Momentum Strategies01:00
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1. Introduction to a Century of Evidence01:10
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1. Introduction to a Century of Evidence01:00
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2.1 A Century of Evidence on Trend-Following Investing05:00
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2. Annotated Paper A Century of Evidence on Trend-Following Investing01:00
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3. Types of Momentum Strategies01:52
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3. Types of Momentum Strategies01:00
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4. Methodology10:14
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4. Methodology01:00
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5. Time Series Momentum03:20
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5. Time Series Momentum01:00
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6. Performance Over a Century02:59
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6. Performance Over a Century01:00
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7. Performance During Crisis Periods02:45
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7. Performance During Crisis Periods01:00
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8. Performance in Different Economic Environments05:17
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8. Performance in Different Economic Environments01:00
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1. Introducing Man AHL01:00
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2. Podcast - The AHL Story Top Traders Unplugged01:00
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1. Introduction to Turning Points02:46
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1. Introduction to Turning Points01:00
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2. Paper Momentum Turning Points01:00
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3. What Are Turning Points04:13
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3. What Are Turning Points01:00
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4. Defining Slow and Fast01:03
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4. Defining Slow and Fast01:00
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5. Slow and Fast Cycles05:49
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5. Slow and Fast Cycles01:00
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6. The Effect of Noise and Persistence (Signal)05:15
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6. The Effect of Noise and Persistence (Signal)01:00
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7. The Model02:50
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7. The Model01:00
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8. Performance07:50
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8. Performance01:00
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9. Beta and Alpha Decomposition03:02
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9. Beta and Alpha Decomposition01:00
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10. Dynamic Speed Selection06:40
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10. Dynamic Speed Selection01:00
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11. Dynamic vs. Static Strategies04:02
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11. Dynamic vs. Static Strategies01:00
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12. Case Study Why the Achilles Heel of Momentum Strategies Poses a Risk01:00
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1. Introduction to Winton Capital01:00
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2. David Harding Talks to Bloomberg01:00
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1. Paper Trending Fast and Slow01:00
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2. Introduction to Trending Fast and Slow00:18
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2. Introduction to Trending Fast and Slow01:00
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3. Theory04:10
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3. Theory01:00
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4. Inisghts on the speeds (window periods)04:14
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4. Inisghts on the speeds (window periods)01:00
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5. Approaches to Risk Management03:55
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5. Approaches to Risk Management01:00
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6. Signal Construction03:53
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6. Signal Construction01:00
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7. Statistics of the S&P 50002:06
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7. Statistics of the S&P 50001:00
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8. Momentum Under Different Regimes10:03
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8. Momentum Under Different Regimes01:00
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9. Sources of Out-Performance05:53
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9. Sources of Out-Performance01:00
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10. Application to the Broader Universe05:10
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10. Application to the Broader Universe01:00
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11. Conclusion04:11
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11. Conclusion01:00
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12.1 Paid to be Paranoid Trending Fast and Slow05:00
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12. Case Study Paid to be Paranoid Trending, Fast and Slow01:00
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1. The Hedge Fund Journal CTA and Discretionary Trader Awards 202301:00
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1.1 Annotated The Impact of Volatility Targeting05:00
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1. Annotated Paper The Impact of Volatility Targeting01:00
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2. Notebook Practical Build your Own Backtest01:00
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3. Introduction to Volatility Targeting03:35
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3. Introduction to Volatility Targeting01:00
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4. Key Concepts03:46
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4. Key Concepts01:00
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5. Findings and Data Sets02:45
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5. Findings and Data Sets01:00
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6. Applying Volatility Targeting (Scaling)09:13
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6. Applying Volatility Targeting (Scaling)01:00
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7. Performance in Equities11:20
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7. Performance in Equities01:00
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8. Performance in Bonds and Other Asset Classes07:37
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8. Performance in Bonds and Other Asset Classes01:00
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9. Why Volatility Targeting Works05:58
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9. Why Volatility Targeting Works01:00
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10. Case Study Volatility is Back - Better to Target Returns or Target Risk01:00
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11. Case Study We See Risk Where Others May Not01:00
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1. Introduction to RavenPack01:00
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2.1 RavenPack-White-Paper-Harnessing-News-Sentiment-for-FX-Futures-Strategies_147D20D27 ANNOTATED05:00
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2. Case Study Harnessing News Sentiment for FX Futures Strategies by RavenPack01:00
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3. Introduction to News Sentiment01:20
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3. Introduction to News Sentiment01:00
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4. Methodology - Trend Following and Mean Reversion17:08
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4. Methodology - Trend Following and Mean Reversion01:00
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5. Results10:48
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5. Results01:00
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6. Conclusion02:20
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6. Conclusion01:00
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1. Introducing the Research Group and their Industry Partners01:00
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1. Paper Enhancing Time Series Momentum Strategies Using Deep Neural Networks01:00
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2.1 Github Repo01:00
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2. Code Deep Momentum Networks01:00
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3. Introduction to Deep Momentum Networks01:39
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3. Introduction to Deep Momentum Networks01:00
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4. Insights on Momentum Strategies03:16
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4. Insights on Momentum Strategies01:00
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5. Landmark Paper Returns to Buying Winners and Selling Losers01:00
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6. Construction of Trading Signals02:45
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6. Construction of Trading Signals01:00
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7. Loss Function and Architecture02:48
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7. Loss Function and Architecture01:00
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8. The Data Used02:42
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8. The Data Used01:00
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9. Performance Evaluation06:59
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9. Performance Evaluation01:00
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10. OMI Lecture on Deep Momentum Networks01:00
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1. Paper Slow Momentum with Fast Reversion01:00
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2. Code Advanced Deep Momentum Networks01:00
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3. Introduction Deep Mom Networks with Change Point Detection02:29
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3. Introduction Deep Mom Networks with Change Point Detection01:00
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4. Momentum and Mean Reversion05:53
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4. Momentum and Mean Reversion01:00
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5. Change Point Detection14:52
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5. Change Point Detection01:00
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6. Methodology05:14
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6. Methodology01:00
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7. Results13:20
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7. Results01:00
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8. Paper Trading with the Momentum Transformer01:00
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9. Code Momentum Transformer01:00
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1.1 Building Cross-Sectional Systematic Strategies05:00
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1. Paper Annotated Building Cross-Sectional Systematic Strategies By LTR01:00
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2. Transfer Ranking in Finance by Daniel Poh01:00
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3. Introduction to Learning to Rank in Trading01:58
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3. Introduction to Learning to Rank in Trading01:00
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4. The Oxford MAN Institute03:42
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4. The Oxford MAN Institute01:00
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5. Cross Sectional Momentum (CSM) Strategies02:47
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5. Cross Sectional Momentum (CSM) Strategies01:00
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6. Anatomy of a CSM Strategy03:56
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6. Anatomy of a CSM Strategy01:00
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7. Score Calculation05:09
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7. Score Calculation01:00
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8. What is Learning to Rank03:27
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8. What is Learning to Rank01:00
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9. How to do it in Finance05:30
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9. How to do it in Finance01:00
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10. Performance Results05:24
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10. Performance Results01:00
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11. Learning to build a LTR Strategy00:58
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11. Learning to build a LTR Strategy01:00
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12.1 Constructing Cross-sectional Systematic Strategies by Learning to Rank01:00
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12. External Lecture Constructing Cross-sectional Systematic Strategies by LTR01:00
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13.1 u201cLearning to Ranku201d by Sophie Watson01:00
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13. External Lecture Learning to Rank by Sophie Watson01:00
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14. Python Library for LambdaMart Implementation01:00
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1.1 Baz et al with annotations05:00
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1. Annotated Paper Dissecting Investment Strategies in the Cross Section...01:00
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2. Introduction to Favourable Market Conditions01:55
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2. Introduction to Favourable Market Conditions01:00
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3. Key Takeaways00:59
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3. Key Takeaways01:00
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4. Carry Strategy06:57
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4. Carry Strategy01:00
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5. Momentum Strategy02:55
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5. Momentum Strategy01:00
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6. Value Strategy14:41
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6. Value Strategy01:00
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7. Important - Valuable Insight! Signal Construction01:00
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7. Important - Valuable Insight! Signal Construction01:00
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8. Code Use this Code to Create the Momentum Features01:00
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9. Portfolio Construction01:38
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9. Portfolio Construction01:00
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10. Results07:57
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10. Results01:00
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1. Paper Enhancing CS Strategies by Context-Aware LTR with Self-Attention01:00
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2. Introduction to an Advanced LTR Method00:57
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2. Introduction to an Advanced LTR Method01:00
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3. Overview Paper03:19
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3. Overview Paper01:00
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4. Model Overview02:46
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4. Model Overview01:00
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5. Backtest Method for CSM Strategy02:30
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5. Backtest Method for CSM Strategy01:00
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6. Enhancing the Ranking05:50
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6. Enhancing the Ranking01:00
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7. Context Aware Model and Encodings07:05
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7. Context Aware Model and Encodings01:00
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8. Transformer Architecture10:31
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8. Transformer Architecture01:00
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9. Experiment Methodology02:07
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9. Experiment Methodology01:00
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10. Strategy Performance04:39
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10. Strategy Performance01:00
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
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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