Mô tả

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.

Bạn sẽ học được gì

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.

Yêu cầu

  • Python Basics
  • Trading Basics

Nội dung khoá học

11 sections

Introduction

2 lectures
Introduction
03:06
Course Content
02:27

Machine Learning Introduction

5 lectures
What Is Machine Learning
02:41
Understanding The Basics
03:33
Types Of Machine Learning Models
05:31
Building Blocks And The Machine Learning Process
07:33
About Applications Using Machine Learning
06:54

Supervised Learning

5 lectures
Supervised Learning
01:08
Supervised Learning Key Concepts
01:57
Supervised Learning: Regression By Example
06:34
Supervised Learning: Classification
05:09
Supervised Learning: Training Process
05:38

Unsupervised Learning

5 lectures
Unsupervised Learning
02:44
Unsupervised Learning: Key Concepts
04:44
Unsupervised Learning: Process Pipeline
04:20
Unsupervised Learning: Challenges And Best Practices Discussion
05:18
Unsupervised Learning: Importance And Closing Notes
01:33

Data Splitting Methods | Overfitting And Underfitting

4 lectures
Data Splitting For Machine Learning
04:23
Data Splitting Techniques
14:51
Overfitting Underfitting And Generalization
11:01
Splitting Python Examples
12:57

Classification Algorithms

18 lectures
Classifiers Introduction
02:43
K Nearest Neighbors
07:56
K Nearest Neighbors Python Example
09:15
K means
06:39
K means Python Example
07:05
Decision Trees
08:18
Decision Trees Python Example
05:41
Random Forests
12:16
Random Forests Python Example
04:16
Logistic Regression
10:27
Logistic Regression Explained Example
02:30
Logistic Regression Python Example
02:29
Naive Bayes Classifier
08:44
Naive Bayes Gaussian Classifier
05:48
Naive Bayes Python Example
03:16
Support Vector Machines
06:10
Support Vector Machines: Kernels
08:47
Support Vector Machines: Python Example
05:34

Evaluating Classifiers

5 lectures
Accuracy, Confusion Matrix And Classification Report
12:33
ROC-AUC And PR-AUC
11:17
Confusion Matrix Python Example
03:30
ROC-AUC Python Example
05:41
PR-AUC Python Example
01:41

Data Analysis And Labelling Financial Data

16 lectures
About Data Sources And Labeling
04:10
Downloading Historical Data
09:59
Visualizing And Inspecting Indicators
15:33
Visualization Examples
12:22
Data Labeling
02:41
Fixed Time Horizon Method
05:50
Fixed Time Horizon: Python Example
05:15
Improved Time Horizon Method
04:07
Improved Time Horizon Python Example
07:08
Triple Barrier Method
02:10
Triple Barrier Python Example
12:04
Strategy Specific Dynamic Labeling
02:59
Strategy Specific Dynamic Labeling Python Example
11:03
Strategy Thresholds Optimization
01:43
Strategy Example For Optimization
02:45
Thresholds Optimization Python Example
12:05

Feature Engineering

6 lectures
Processing Technical Indicators
09:41
Processing Technical Indicators Python Examples
13:57
Features Enhancement And Dimensionality Reduction
09:55
Features Enhancement And Dimensionality Reduction Python Examples
12:51
Standardization Normalization And One Hot Encoding
15:14
Standardization Normalization And One Hot Encoding Python Examples
05:52

Training Machine Learning Models

5 lectures
Fitting Classifiers
23:50
Fitting Classifiers Without Data Leakage
21:50
XGBoost
04:02
Neural Networks Classifier
05:26
XGBoost And Neural Networks Python Example
12:30

Financial Backtesting Of Machine Learning Strategies In Python

2 lectures
Backtesting Machine Learning Indicators In Python
11:14
Quick Recap And Final Thoughts
04:34

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