Machine Learning In Algorithmic Trading

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Ziad Francis

8:49:28

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1. Introduction

1. Introduction.mp4

03:06

2. Course Content.mp4

02:27

2. Machine Learning Introduction

1. What Is Machine Learning.mp4

02:41

2. Understanding The Basics.mp4

03:33

3. Types Of Machine Learning Models.mp4

05:31

4. Building Blocks And The Machine Learning Process.mp4

07:33

5. About Applications Using Machine Learning.mp4

06:54

3. Supervised Learning

1. Supervised Learning.mp4

01:08

2. Supervised Learning Key Concepts.mp4

01:57

3. Supervised Learning Regression By Example.mp4

06:34

4. Supervised Learning Classification.mp4

05:09

5. Supervised Learning Training Process.mp4

05:38

4. Unsupervised Learning

1. Unsupervised Learning.mp4

02:44

2. Unsupervised Learning Key Concepts.mp4

04:44

3. Unsupervised Learning Process Pipeline.mp4

04:20

4. Unsupervised Learning Challenges And Best Practices Discussion.mp4

05:18

5. Unsupervised Learning Importance And Closing Notes.mp4

01:33

5. Data Splitting Methods Overfitting And Underfitting

1. Data Splitting For Machine Learning.mp4

04:23

2. Data Splitting Techniques.mp4

14:51

3. Overfitting Underfitting And Generalization.mp4

11:01

4.1 03 data splitting.zip

4. Splitting Python Examples.mp4

12:57

6. Classification Algorithms

1. Classifiers Introduction.mp4

02:43

2. K Nearest Neighbors.mp4

07:56

3. K Nearest Neighbors Python Example.mp4

09:15

4. K means.mp4

06:39

5. K means Python Example.mp4

07:05

6. Decision Trees.mp4

08:18

7. Decision Trees Python Example.mp4

05:41

8. Random Forests.mp4

12:16

9. Random Forests Python Example.mp4

04:16

10. Logistic Regression.mp4

10:27

11. Logistic Regression Explained Example.mp4

02:30

12. Logistic Regression Python Example.mp4

02:29

13. Naive Bayes Classifier.mp4

08:44

14. Naive Bayes Gaussian Classifier.mp4

05:48

15. Naive Bayes Python Example.mp4

03:16

16. Support Vector Machines.mp4

06:10

17. Support Vector Machines Kernels.mp4

08:47

18. Support Vector Machines Python Example.mp4

05:34

Files.zip

7. Evaluating Classifiers

1. Accuracy, Confusion Matrix And Classification Report.mp4

12:33

2. ROC-AUC And PR-AUC.mp4

11:17

3. Confusion Matrix Python Example.mp4

03:30

4. ROC-AUC Python Example.mp4

05:41

5. PR-AUC Python Example.mp4

01:41

Files.zip

8. Data Analysis And Labelling Financial Data

1. About Data Sources And Labeling.mp4

04:10

2. Downloading Historical Data.mp4

09:59

3. Visualizing And Inspecting Indicators.mp4

15:33

4. Visualization Examples.mp4

12:22

5. Data Labeling.mp4

02:41

6. Fixed Time Horizon Method.mp4

05:50

7. Fixed Time Horizon Python Example.mp4

05:15

8. Improved Time Horizon Method.mp4

04:07

9. Improved Time Horizon Python Example.mp4

07:08

10. Triple Barrier Method.mp4

02:10

11. Triple Barrier Python Example.mp4

12:04

12. Strategy Specific Dynamic Labeling.mp4

02:59

13. Strategy Specific Dynamic Labeling Python Example.mp4

11:03

14. Strategy Thresholds Optimization.mp4

01:43

15. Strategy Example For Optimization.mp4

02:45

16. Thresholds Optimization Python Example.mp4

12:05

Files.zip

9. Feature Engineering

1. Processing Technical Indicators.mp4

09:41

2. Processing Technical Indicators Python Examples.mp4

13:57

3. Features Enhancement And Dimensionality Reduction.mp4

09:55

4. Features Enhancement And Dimensionality Reduction Python Examples.mp4

12:51

5. Standardization Normalization And One Hot Encoding.mp4

15:14

6. Standardization Normalization And One Hot Encoding Python Examples.mp4

05:52

Files.zip

10. Training Machine Learning Models

1. Fitting Classifiers.mp4

23:50

2. Fitting Classifiers Without Data Leakage.mp4

21:50

3. XGBoost.mp4

04:02

4. Neural Networks Classifier.mp4

05:26

5. XGBoost And Neural Networks Python Example.mp4

12:30

Files.zip

11. Financial Backtesting Of Machine Learning Strategies In Python

1.1 02 XGBooost Backtesting.zip

1. Backtesting Machine Learning Indicators In Python.mp4

11:14

2. Quick Recap And Final Thoughts.mp4

04:34

Description

Blending Algorithmic Trading with Machine Learning For Forex and Stock Market Indicators

What You'll Learn?

Understand the basics of Machine Learning and its applications in Algorithmic Trading.
Learn how to implement Machine Learning algorithms for predicting stock prices and making trading decisions.
Gain hands-on experience with real-world trading data and learn how to preprocess and analyze this data for Machine Learning.
Learn how to evaluate the performance of Machine Learning models in the context of Algorithmic Trading.

Who is this for?

Beginners in Python curious about Data Science

Financial Analysts and Traders looking to enhance their trading strategies using Machine Learning techniques.

Data Scientists and Data Analysts

Students and Professionals in Computer Science and Mathematics

Individuals with a background in Finance and a keen interest in Machine Learning

Anyone interested in learning about Algorithmic Trading and Machine Learning

What You Need to Know?

Python Basics

Trading Basics

More details

Description
A comprehensive course on "Machine Learning in Algorithmic Trading". This course is designed to empower you with the knowledge and skills to apply Machine Learning techniques in Algorithmic Trading.
In the world of finance, Machine Learning has revolutionized trading strategies. It offers automation, pattern recognition, and the ability to handle large and complex datasets. However, it also comes with challenges such as model complexity, the risk of overfitting, and the need to adapt to dynamic market conditions. This course aims to guide you through these challenges and rewards, providing you with a solid foundation in Machine Learning and its applications in Algorithmic Trading.
The course begins with a deep dive into the basics of Machine Learning, covering key concepts and algorithms that are crucial for Algorithmic Trading. You will learn how to use Python, a versatile and beginner-friendly language, to implement Machine Learning algorithms for trading. With Python's robust libraries like Pandas and NumPy, you will be able to handle and process large and complex financial datasets efficiently.
As you progress through the course, you will learn how to use Machine Learning for predictive modeling. This involves studying historical market data to train a Machine Learning model that can make predictions about future market movements. These predictions can then be used to make better-informed trading decisions.
You will also learn how to use Machine Learning for pattern recognition in market data. Machine Learning algorithms excel at identifying complex patterns and relationships in large datasets, enabling the discovery of trading signals and patterns that may not be apparent to human traders.
By the end of this course, you will have a comprehensive understanding of how Machine Learning can be used in Algorithmic Trading. From acquiring and preprocessing data to creating hyperparameters, splitting data for evaluation, optimizing model parameters, making predictions, and assessing performance, you will gain insights into the entire process. This course is designed to be accessible to beginners with a basic understanding of Python and Machine Learning concepts, making it a great choice for anyone interested in learning about Algorithmic Trading and Machine Learning.
Who this course is for:
Beginners in Python curious about Data Science
Financial Analysts and Traders looking to enhance their trading strategies using Machine Learning techniques.
Data Scientists and Data Analysts
Students and Professionals in Computer Science and Mathematics
Individuals with a background in Finance and a keen interest in Machine Learning
Anyone interested in learning about Algorithmic Trading and Machine Learning

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PhD, Qualified and experienced educator and researcher in the field of data science. I am here to share my background and expertise in numerical disciplines. I have a proven track record of publishing academic research and shaped my teaching skills through previous instructional roles. You will notice my courses come in a condensed format avoiding waste of energy and time, I strongly believe that most of the principles can be acquired in few minutes if taught the proper way and there is no need for lengthy explanations which sometimes lead to boredom and students dropping the course! I hope you will benefit from my teaching style and that you will also enjoy your learning adventure. Good luck!

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