---
product_id: 221915022
title: "- Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python"
price: "620 Lei"
currency: RON
in_stock: true
reviews_count: 13
url: https://www.desertcart.ro/products/221915022-machine-learning-for-algorithmic-trading-predictive-models-to-extract-signals
store_origin: RO
region: Romania
---

# - Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

**Price:** 620 Lei
**Availability:** ✅ In Stock

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- **What is this?** - Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python
- **How much does it cost?** 620 Lei with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.ro](https://www.desertcart.ro/products/221915022-machine-learning-for-algorithmic-trading-predictive-models-to-extract-signals)

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## Description

Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python [Stefan Jansen] on desertcart.com. *FREE* shipping on qualifying offers. Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Review: Built My First Successful Trading Bot - As someone who's been interested in both machine learning and financial markets, I was looking for a comprehensive resource that would bridge these two worlds. Stefan Jansen's "Machine Learning for Algorithmic Trading" not only delivered on this promise but exceeded my expectations by enabling me to build my own profitable algorithmic trading bot. What sets this book apart is how it provides a complete end-to-end workflow. The progression from basic concepts to advanced implementation is logical and thorough. Jansen starts with essential market data handling, moves through feature engineering techniques, and culminates in sophisticated model development and backtesting. The Python code examples using popular libraries like pandas, scikit-learn, and PyTorch provided immediate practical value rather than just theoretical concepts. I particularly appreciated the diverse range of ML techniques covered - from traditional algorithms to deep learning approaches. The sections on feature engineering and alpha factor research were especially valuable for my trading bot development. The book doesn't just teach you algorithms; it shows you how to apply them meaningfully to extract signals from market data. The inclusion of backtrader and Zipline for strategy testing was instrumental in helping me validate my ideas before risking real capital. I was able to iterate on my strategies, identify weaknesses, and refine my approach using the framework provided in the book. While the book is certainly dense at 800+ pages, it serves as both a learning resource and a reference manual. I continue to revisit specific chapters as I enhance my trading strategies. Even with some prior knowledge in both programming and finance, I found tremendous value in Jansen's comprehensive approach. One small caveat: some of the code examples require updating as libraries evolve, but the core concepts remain solid and adaptable. In fact, working through these updates enhanced my understanding of the underlying systems. Bottom line - this book delivered exactly what I needed: the knowledge and tools to transform my interest in ML and markets into a functioning algorithmic trading system. For anyone serious about applying machine learning to trading, this book is an essential investment that can potentially pay for itself many times over.
Review: The math theory before Python code is fantastic! - What I love about this book is that it delves into light math theory before diving into the Python code. This book is good for someone with a intermediate Python background and machine learning or finance knowledge. From there, this book will help fill in the gaps. I highly recommend this book!

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #83,166 in Books ( See Top 100 in Books ) #6 in Financial Engineering (Books) #12 in Machine Theory (Books) #52 in Python Programming |
| Customer Reviews | 4.5 4.5 out of 5 stars (404) |
| Dimensions  | 7.5 x 1.86 x 9.25 inches |
| Edition  | 2nd ed. |
| ISBN-10  | 1839217715 |
| ISBN-13  | 978-1839217715 |
| Item Weight  | 3.24 pounds |
| Language  | English |
| Print length  | 820 pages |
| Publication date  | July 31, 2020 |
| Publisher  | Packt Publishing |

## Images

![- Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python - Image 1](https://m.media-amazon.com/images/I/71ycxzrff0L.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Built My First Successful Trading Bot
*by R***L on March 31, 2025*

As someone who's been interested in both machine learning and financial markets, I was looking for a comprehensive resource that would bridge these two worlds. Stefan Jansen's "Machine Learning for Algorithmic Trading" not only delivered on this promise but exceeded my expectations by enabling me to build my own profitable algorithmic trading bot. What sets this book apart is how it provides a complete end-to-end workflow. The progression from basic concepts to advanced implementation is logical and thorough. Jansen starts with essential market data handling, moves through feature engineering techniques, and culminates in sophisticated model development and backtesting. The Python code examples using popular libraries like pandas, scikit-learn, and PyTorch provided immediate practical value rather than just theoretical concepts. I particularly appreciated the diverse range of ML techniques covered - from traditional algorithms to deep learning approaches. The sections on feature engineering and alpha factor research were especially valuable for my trading bot development. The book doesn't just teach you algorithms; it shows you how to apply them meaningfully to extract signals from market data. The inclusion of backtrader and Zipline for strategy testing was instrumental in helping me validate my ideas before risking real capital. I was able to iterate on my strategies, identify weaknesses, and refine my approach using the framework provided in the book. While the book is certainly dense at 800+ pages, it serves as both a learning resource and a reference manual. I continue to revisit specific chapters as I enhance my trading strategies. Even with some prior knowledge in both programming and finance, I found tremendous value in Jansen's comprehensive approach. One small caveat: some of the code examples require updating as libraries evolve, but the core concepts remain solid and adaptable. In fact, working through these updates enhanced my understanding of the underlying systems. Bottom line - this book delivered exactly what I needed: the knowledge and tools to transform my interest in ML and markets into a functioning algorithmic trading system. For anyone serious about applying machine learning to trading, this book is an essential investment that can potentially pay for itself many times over.

### ⭐⭐⭐⭐⭐ The math theory before Python code is fantastic!
*by A***A on April 6, 2025*

What I love about this book is that it delves into light math theory before diving into the Python code. This book is good for someone with a intermediate Python background and machine learning or finance knowledge. From there, this book will help fill in the gaps. I highly recommend this book!

### ⭐⭐⭐⭐ Great topics covered for those interested in the subject
*by M***E on December 29, 2024*

As with all code related books, this one is started to show its age. Some of the links are no longer available and some of the code does not work. If you know what you are doing, you should be able to make it work and keep going, but finding the exact same data sources might be a little more difficult. I'm only up to the second chapter so far, since the first thing I did was to create my docker environment and pull all of the data sources.

## Frequently Bought Together

- Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition
- Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python
- Quantitative Trading: How to Build Your Own Algorithmic Trading Business (Wiley Trading)

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*Product available on Desertcart Romania*
*Store origin: RO*
*Last updated: 2026-03-24*