Machine Learning for Algorithmic Trading Bots with Python

Introducing the study of machine learning and algorithmic trading for financial practitioners

3.25 (55 reviews)
Udemy
platform
English
language
Data Science
category
Machine Learning for Algorithmic Trading Bots with Python
632
students
5 hours
content
Mar 2019
last update
$34.99
regular price

What you will learn

You will learn about financial terminology and methodology and how to apply them

Get hands-on financial data structures and financial machine learning

Understand complex financial terminology and methodology in simple ways

Ensemble models and cross-validation for financial applications

Backtesting for models and strategies evaluation and validation

Apply your skills to real world cryptocurrency trading such as BitCoin and Ethereum

Putting machine learning into real world problems and derive solutions

Why take this course?

Have you ever wondered how the Stock Market, Forex, Cryptocurrency and Online Trading works? Have you ever wanted to become a rich trader having your computers work and make money for you while you’re away for a trip in the Maldives? Ever wanted to land a decent job in a brokerage, bank, or any other prestigious financial institution?We have compiled this course for you in order to seize your moment and land your dream job in financial sector. This course covers the advances in the techniques developed for algorithmic trading and financial analysis based on the recent breakthroughs in machine learning. We leverage the classic techniques widely used and applied by financial data scientists to equip you with the necessary concepts and modern tools to reach a common ground with financial professionals and conquer your next interview.By the end of the course, you will gain a solid understanding of financial terminology and methodology and a hands-on experience in designing and building financial machine learning models. You will be able to evaluate and validate different algorithmic trading strategies. We have a dedicated section to backtesting which is the holy grail of algorithmic trading and is an essential key to successful deployment of reliable algorithms.

About the Author

Mustafa Qamar-ud-Din is a machine learning engineer with over 10 years of experience in the software development industry. He is a specialist in image processing, machine learning and deep learning. He worked with many startups and understands the dynamics of agile methodologies and the challenges they face on a day to day basis. He is also quite aware of the professional skills which the recruiters are looking for when making hiring decisions.

Content

Building Your First Trading Bot

The Course Overview
Introduction to Financial Machine Learning and Algorithmic Trading
Setting up the Environment
Project Skeleton Overview
Fetching and Understanding the Dataset
Build the Conventional Buy and Hold Strategy
Evaluate the Strategy’s Performance

Design a Machine Learning Model

Intuition behind Random Forests Algorithm
Build and Implement Random Forests Algorithm
Plug-in Random Forests Implementation into Your Bot
Evaluate Random Forest’s Performance

Build a Trading Algorithm

Introducing Online Algorithms
Getting Statistical Correlation
Implement Exploit Correlation Strategy
Evaluate the Strategy

Design Advanced Machine Learning Model

Ensemble Learning Theory
Implementing GBoosting Using Python
Evaluating the Model Performance

Build Advanced Trading Algorithm

Introduction to Scalpers Trading Strategy
Implement Scalpers Trading Strategy
Evaluate Scalpers Trading Strategy

Model and Strategy Evaluation

Introducing Value at Risk Backtest
Implement Value at Risk Backtest
Value at Risk with Machine Learning
Implement VaR Using SVR
Conclusion and Next steps

Screenshots

Machine Learning for Algorithmic Trading Bots with Python - Screenshot_01Machine Learning for Algorithmic Trading Bots with Python - Screenshot_02Machine Learning for Algorithmic Trading Bots with Python - Screenshot_03Machine Learning for Algorithmic Trading Bots with Python - Screenshot_04

Reviews

Anca
December 17, 2022
I purchased this course primarily because of the promise of practical content. However this content is outdated: the Python dependencies (zipline, mpl-finance) are not maintained any longer since 2020 or have been deprecated. The team behind `zipline` doesn't exist anymore since Quantopian was shut down in 2020. I've tried nevertheless installing zipline with Python 3.6 and failed. Don't waste your money on this course unless it gets updated with fresh usage of new dependencies.
Chang
May 22, 2020
The course is very inspiring. But is also highly outdated without a lot of research it will be impossible to keep it up. The study's progress could be slow due to outdated information. I hope the instructor can update the course.
Edwin
August 25, 2019
confusing, teacher seems to be reading from paper, very little machine learning, examples are not always working

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Enrollment distribution

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2256952
udemy ID
3/6/2019
course created date
7/23/2020
course indexed date
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