--- 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: "NZ$121" currency: NZD in_stock: true reviews_count: 13 url: https://www.desertcart.nz/products/221915022-machine-learning-for-algorithmic-trading-predictive-models-to-extract-signals store_origin: NZ region: New Zealand --- # Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python **Price:** NZ$121 **Availability:** ✅ In Stock ## Quick Answers - **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?** NZ$121 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.nz](https://www.desertcart.nz/products/221915022-machine-learning-for-algorithmic-trading-predictive-models-to-extract-signals) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required. Table of Contents Machine Learning for Trading – From Idea to Execution Market and Fundamental Data – Sources and Techniques Alternative Data for Finance – Categories and Use Cases Financial Feature Engineering – How to Research Alpha Factors Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models – From Risk Factors to Return Forecasts The ML4T Workflow – From Model to Strategy Backtesting (N.B. Please use the Look Inside option to see further chapters) 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: Great book, nice examples. - The book overall is very didactic, my only recommendation would be to use a more simple set up as many of the recommended tools and libraries are outdated, not the author’s fault but renders impossible to follow some of the examples. A more simple set of standard libraries, perhaps could be more stable and allow to follow better the presented examples. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #74,856 in Books ( See Top 100 in Books ) #7 in Financial Engineering (Books) #10 in Machine Theory (Books) #43 in Python Programming | | Customer Reviews | 4.4 out of 5 stars 413 Reviews | ## 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. ### ⭐⭐⭐⭐⭐ Great book, nice examples. *by S***R on July 30, 2024* The book overall is very didactic, my only recommendation would be to use a more simple set up as many of the recommended tools and libraries are outdated, not the author’s fault but renders impossible to follow some of the examples. A more simple set of standard libraries, perhaps could be more stable and allow to follow better the presented examples. ### ⭐⭐⭐⭐ A good book with improvement scopes *by R***) on December 28, 2024* A promising book with plenty of room for improvement. While there are some noticeable typos, the overall reading experience is enjoyable. A more refined and updated version, perhaps a third edition, would enhance its appeal significantly. ## 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) --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.nz/products/221915022-machine-learning-for-algorithmic-trading-predictive-models-to-extract-signals](https://www.desertcart.nz/products/221915022-machine-learning-for-algorithmic-trading-predictive-models-to-extract-signals) --- *Product available on Desertcart New Zealand* *Store origin: NZ* *Last updated: 2026-05-26*