Applications of Machine Learning in Trading

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Hudson and Thames Quantitative Research

4:41:08

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1. Course Introduction

1.1 AI Pioneers in Investment Management.pdf

1. Course Introduction.mp4

02:52

2. Introduction

1.1 Intro to Machine Learning Article - Annotated notes.pdf

1.2 Introduction to Machine Learning-Slides-compressed.pdf

1. Introduction to Machine Learning.mp4

24:05

2.1 Data Handling and Preprocessing -Slides.pdf

2. Lecture 2 Data Handling and Preprocessing.mp4

18:23

3. Introduction to Machine Learning Quiz.html

4. Data handling and preprocessing Quiz.html

3. Generating Signals for Quant Models with Machine Learning

1.1 Leveraging machine learning to separate noise from news - Slides.pdf

1.2 Noise from News - annotated paper.pdf

1. Case Study On Separating Noise from News.mp4

26:30

2.1 Assessment of Trading Signal Quality - Slides.pdf

2. Assessment of Trading Signal Quality.mp4

17:16

3.1 Generating Trading Signals by ML Algorithms or Time Series -Slides.pdf

3.2 Time Series vs ML - Annotated Paper.pdf

3. Generating Trading Signals by ML algorithms or time series ones.mp4

19:15

4. Refining Equity Trading Volume Prediction with Deep Learning

1.1 Analysis of trading volume - Slides.pdf

1. Introduction to Volume Prediction.mp4

12:13

2.1 Volume Prediction with neural networks- Slides.pdf

2.2 Volume predictions with NN - annotated.pdf

2. Volume Prediction with Neural Networks.mp4

22:56

3.1 Stock Trading Volume Prediction with Dual-Process Meta-Learning - Slides.pdf

3.2 Volume Prediction with Meta Learning - Annotated.pdf

3. Stock Trading Volume Prediction with Dual-Process Meta Learning.mp4

19:40

5. Sentiment Analysis

1.1 Intro to Sentiment Analysis - Slides.pdf

1. Introduction to Sentiment Analysis.mp4

18:28

2.1 Harnessing News Sentiment for FX Futures Strategies - Annotated notes.pdf

2.2 Harnessing News Sentiment for FX Futures Strategies - Slides.pdf

2. Harnessing News Sentiment.mp4

31:36

3.1 Alternative Data Case Study Bloomberg News - Annotated.pdf

3.2 Bloomberg News & Social Sentiment Data - Slides (1).pdf

3. Bloomberg News Sentiment Case Study.mp4

12:55

6. Leveraging AIAlternative Data Analysis

1.1 A Survey on Alternative Data in Finance and Business.pdf

1. AI in Finance Introduction.mp4

17:21

2.1 Statistical arbitrage powered by Explainable Artificial Intelligence - Annotated.pdf

2.2 Statistical arbitrage powered by Explainable Artificial Intelligence-Slides.pdf

2. Statistical arbitrage powered by Explainable Artificial Intelligence.mp4

37:38

Description

Reading Group -- Early Access --

What You'll Learn?

Understand Core Machine Learning Concepts and Theories
Gain a comprehensive understanding of the mathematical principles underlying machine learning algorithms
Explore the Frontiers of Machine Learning Theory: Students will engage with the latest research and theoretical advancements in machine learning
Ethical Considerations and Theoretical Limitations

Who is this for?

Tailored for beginners in quantitative finance who wish to gain a good overview in Machine Learning

Any graduate or professional with a mathematical background seeking to learn Machine Learning techniques

What You Need to Know?

Statistics and Calculus background will be beneficial but not necessary

No Machine Learning knowledge needed

More details

Description
Dive into the essence of machine learning, not through mere tool usage, but by unraveling its core principles via the lens of quantitative finance case studies. This course is meticulously crafted toÂ establish a solid foundation in the theory and mathematical underpinnings of machine learning. With this theoretical groundwork in place, we then transition into a series of detailed research papers, each carefully selected to enrich your understanding and illustrate the practical applications of these concepts within the realm of quantitative finance.
The course isÂ designed to first impart a solid understanding of the theory and mathematical foundations underpinning each section.Â Following this theoretical grounding, we delve into case studies and research papers to enrich your comprehension, illustrating the practical application of these concepts in quantitative finance.
This approach ensures a robust grasp of both the abstract and practical aspects of machine learning, providing you with a comprehensive insight into its deployment in the financial domain. Through detailed case studies, we'll explore the nuances of algorithmic trading, risk management, asset pricing, and portfolio optimization, demonstrating how machine learning can uncover insights from vast datasets and drive decision-making.

This blend of theory, case study analysis, and interactive learning equips you with not just knowledge, but the confidence to apply machine learning innovations in quantitative finance.

Whether you're a financial professional seeking to leverage machine learning for strategic decision-making, a mathematician curious about the financial applications of these algorithms, or someone entirely new to either field, this course is designed to equip you with the knowledge, skills, and insight to navigate and excel in the intersection of machine learning and finance.
Who this course is for:
Tailored for beginners in quantitative finance who wish to gain a good overview in Machine Learning
Any graduate or professional with a mathematical background seeking to learn Machine Learning techniques

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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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