{"title":"Stefan Jansen","description":"\u003cp\u003eDive into the world of Stefan Jansen, where data science meets practical application. Explore innovative approaches to machine learning and AI, ideal for both enthusiasts and seasoned professionals. Start your data journey here.\u003c\/p\u003e","products":[{"product_id":"machine-learning-for-algorithmic-trading-book-stefan-jansen-9781839217715","title":"Machine Learning for Algorithmic Trading","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.\n\nPurchase of the print or Kindle book includes a free eBook in the PDF format.\n\nKey Features\n\nDesign, train, and evaluate machine learning algorithms that underpin automated trading strategies\nCreate a research and strategy development process to apply predictive modeling to trading decisions\nLeverage NLP and deep learning to extract tradeable signals from market and alternative data\n\nBook DescriptionThe 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.\n\nThis 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.\n\nThis 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.\n\nBy 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\n\nLeverage market, fundamental, and alternative text and image data\nResearch and evaluate alpha factors using statistics, Alphalens, and SHAP values\nImplement machine learning techniques to solve investment and trading problems\nBacktest and evaluate trading strategies based on machine learning using Zipline and Backtrader\nOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio\nCreate a pairs trading strategy based on cointegration for US equities and ETFs\nTrain a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and quotes data\n\nWho this book is forIf 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.\nSome understanding of Python and machine learning techniques is required.","brand":"WoB","offers":[{"title":"GB \/ VERY_GOOD \/ INTERNAL","offer_id":49545326952721,"sku":"GOR011115713","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ GOOD \/ SBYB","offer_id":49949134160145,"sku":"CIN1839217715G","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ NEW \/ INGRAM","offer_id":51057352179985,"sku":"NIN9781839217715","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ WELL_READ \/ SBYB","offer_id":51826638160145,"sku":"CIN1839217715A","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ WELL_READ \/ INTERNAL","offer_id":52365346504977,"sku":"GOR014508588","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52673095663889,"sku":"NLS9781839217715","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ VERY_GOOD \/ SBYB","offer_id":53262891974929,"sku":"CIN1839217715VG","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ LIKE_NEW \/ INTERNAL","offer_id":54110968119569,"sku":"GOR010998067","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1839217715.jpg?v=1786614061"},{"product_id":"hands-on-machine-learning-for-algorithmic-trading-book-stefan-jansen-9781789346411","title":"Hands-On Machine Learning for Algorithmic Trading","description":"Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras\n\nKey Features\n\nImplement machine learning algorithms to build, train, and validate algorithmic models\nCreate your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions\nDevelop neural networks for algorithmic trading to perform time series forecasting and smart analytics\n\nBook DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies.\n\nThis book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies.\n\nFinally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.What you will learn\n\nImplement machine learning techniques to solve investment and trading problems\nLeverage market, fundamental, and alternative data to research alpha factors\nDesign and fine-tune supervised, unsupervised, and reinforcement learning models\nOptimize portfolio risk and performance using pandas, NumPy, and scikit-learn\nIntegrate machine learning models into a live trading strategy on Quantopian\nEvaluate strategies using reliable backtesting methodologies for time series\nDesign and evaluate deep neural networks using Keras, PyTorch, and TensorFlow\nWork with reinforcement learning for trading strategies in the OpenAI Gym\n\nWho this book is forHands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.","brand":"WoB","offers":[{"title":"US \/ NEW \/ INGRAM","offer_id":51052198396177,"sku":"NIN9781789346411","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ GOOD \/ SBYB","offer_id":51079156072721,"sku":"CIN178934641XG","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ VERY_GOOD \/ SBYB","offer_id":51797168128273,"sku":"CIN178934641XVG","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ VERY_GOOD \/ INTERNAL","offer_id":52074132144401,"sku":"GOR012761572","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52617799696657,"sku":"NLS9781789346411","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/178934641X.jpg?v=1751153944"},{"product_id":"machine-learning-for-algorithmic-trading-second-edition-book-stefan-jansen-9781837027095","title":"Machine Learning for Algorithmic Trading - Second Edition","description":"\u003cp\u003e\u003cstrong\u003eLeverage 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.\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eDesign, train, and evaluate machine learning algorithms that underpin automated trading strategies\u003c\/li\u003e\n\u003cli\u003eCreate a research and strategy development process to apply predictive modeling to trading decisions\u003c\/li\u003e\n\u003cli\u003eLeverage NLP and deep learning to extract tradeable signals from market and alternative data\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eBy 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.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eLeverage market, fundamental, and alternative text and image data\u003c\/li\u003e\n\u003cli\u003eResearch and evaluate alpha factors using statistics, Alphalens, and SHAP values\u003c\/li\u003e\n\u003cli\u003eImplement machine learning techniques to solve investment and trading problems\u003c\/li\u003e\n\u003cli\u003eBacktest and evaluate trading strategies based on machine learning using Zipline and Backtrader\u003c\/li\u003e\n\u003cli\u003eOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio\u003c\/li\u003e\n\u003cli\u003eCreate a pairs trading strategy based on cointegration for US equities and ETFs\u003c\/li\u003e\n\u003cli\u003eTrain a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and quotes data\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf 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.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eMachine Learning for Trading - From Idea to Execution\u003c\/li\u003e\n\u003cli\u003eMarket and Fundamental Data - Sources and Techniques\u003c\/li\u003e\n\u003cli\u003eAlternative Data for Finance - Categories and Use Cases\u003c\/li\u003e\n\u003cli\u003eFinancial Feature Engineering - How to Research Alpha Factors\u003c\/li\u003e\n\u003cli\u003ePortfolio Optimization and Performance Evaluation\u003c\/li\u003e\n\u003cli\u003eThe Machine Learning Process\u003c\/li\u003e\n\u003cli\u003eLinear Models - From Risk Factors to Return Forecasts\u003c\/li\u003e\n\u003cli\u003eThe ML4T Workflow - From Model to Strategy Backtesting\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e(N.B. Please use the Look Inside option to see further chapters)\u003c\/p\u003e","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51054721696017,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51054724317457,"sku":"NIN9781837027095","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52677631639825,"sku":"NLS9781837027095","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1837027099.jpg?v=1750724261"},{"product_id":"machine-learning-for-trading-book-stefan-jansen-9781803246970","title":"Machine Learning for Trading","description":"Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAP\n\nKey Features\n\nBuild point-in-time pipelines, integrate alternative data, and ensure data integrity\nBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signals\nDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generators\n\nBook DescriptionThe rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning. \n\nIt introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems. \n\nYou’ll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. \n\nPractical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets. \n\nBy the end of this book, you’ll be proficient to build your own industrial-grade “alpha factory\".What you will learn\n\nTransform raw data into predictive alpha factors, validated with leak-proof cross-validation\nMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agents\nHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standards\nBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safely\n\nWho this book is forIf 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. \n\nSome understanding of Python and machine learning techniques is required.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":53795180478737,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":53795180806417,"sku":"NLS9781803246970","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9781803246970.jpg?v=1784595740"}],"url":"https:\/\/www.worldofbooks.com\/en-gb\/collections\/author-books-by-stefan-jansen.oembed","provider":"World of Books ","version":"1.0","type":"link"}