Stefan Jansen – författare

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and KerasKey FeaturesImplement machine learning algorithms to build, train, and validate algorithmic modelsCreate your own algorithmic design process to apply probabilistic machine learning approaches to trading decisionsDevelop neural networks for algorithmic trading to perform time series forecasting and smart analyticsBook 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.This 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.Finally, 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 learnImplement machine learning techniques to solve investment and trading problemsLeverage market, fundamental, and alternative data to research alpha factorsDesign and fine-tune supervised, unsupervised, and reinforcement learning modelsOptimize portfolio risk and performance using pandas, NumPy, and scikit-learnIntegrate machine learning models into a live trading strategy on QuantopianEvaluate strategies using reliable backtesting methodologies for time seriesDesign and evaluate deep neural networks using Keras, PyTorch, and TensorFlowWork with reinforcement learning for trading strategies in the OpenAI GymWho 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.

Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAPKey FeaturesBuild point-in-time pipelines, integrate alternative data, and ensure data integrityBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signalsDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generatorsBook 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. It 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. You ll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. Practical 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. By the end of this book, you ll be proficient to build your own industrial-grade alpha factory"e;.What you will learnTransform raw data into predictive alpha factors, validated with leak-proof cross-validationMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agentsHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standardsBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safelyWho 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. Some understanding of Python and machine learning techniques is required.

Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAPKey FeaturesBuild point-in-time pipelines, integrate alternative data, and ensure data integrityBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signalsDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generatorsBook 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. It 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. You’ll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. Practical 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. By the end of this book, you’ll be proficient to build your own industrial-grade “alpha factory".What you will learnTransform raw data into predictive alpha factors, validated with leak-proof cross-validationMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agentsHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standardsBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safelyWho 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. Some understanding of Python and machine learning techniques is required.

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 strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage 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 dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain 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.

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 FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook 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.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 learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and quotes dataWho 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.Some understanding of Python and machine learning techniques is required.

Sobald Sie das Wort Schaschlik hören, denken Sie wahrscheinlich an einen gemütlichen Abend im Sommer mit Ihren Freunden, richtig? Wussten Sie allerdings, dass Sie Schaschlik auch ideal auf dem Elektrogrill oder im Backofen machen können? Dabei muss Schaschlik auch nicht immer Fleisch enthalten, denn es gibt zahlreiche vegetarische und vegane Alternativen. Einige der Alternativen, die Sie in diesem Buch kennenlernen werden, sind Tofu, Tempeh und Halloumi. Egal ob süß, herzhaft, mit Fleisch, mit Fisch, vegetarisch oder vegan – Schaschlik ist ein leckeres Gericht für jedermann und für jede Jahreszeit. Dabei ist es ganz egal, ob Sie ihn im Sommer mit Ihren Freunden über dem Holzkohlegrill genießen, mit Ihrer Familie im Wohnzimmer über dem Elektrogrill sitzen oder ob Sie es ganz einfach im Backofen zubereiten. In diesem Kochbuch finden Sie über 100 verschiedene Rezepte aus den Kategorien vegan, vegetarisch, mit Fleisch und mit Fisch. Zusätzlich finden Sie am Ende des Buches Rezepte für Marinaden, Soßen und ein Rezept für hausgemachtes Fladenbrot, welches Sie zum Schaschlik servieren können. Viel Spaß beim Ausprobieren und guten Appetit! Das erwartet Sie: ✓100 abwechslungsreiche Schaschlik Rezepte ✓Leckere Marinaden Rezepte ✓Ein Fladenbrot Rezept zum Selbstmachen

Die Anfängerhausarbeit im Staatsrecht ist ein Baustein für die Leistungsnachweise der Zwischenprüfung an nahezu allen juristischen Fakultäten. Häufig kommt diese in Gestalt einer Ferienhausarbeit am Ende des 2./3. Semesters auf die Anfänger im Staatsrecht zu. Die wissenschaftliche Arbeitsweise, ein stringenter Lösungsweg mit Stellungnahme zu allen im Sachverhalt aufgeworfenen Problemen und schließlich die formale Abfassung der Arbeit bedeutet für Studienanfängerinnen und -anfänger absolutes Neuland.Dieses Anleitungsbuch liefert einen auf die Probleme zu Studienbeginn zugeschnittenen Leitfaden zur Herangehensweise und formalen Bearbeitung einer Hausarbeit und zehn nachvollziehbar und mustergültig gelöste Fälle. Das Werk bietet also kompaktes Prüfungstraining aus einem Guss. Es ist inhaltlich eng verzahnt mit den Schwerpunkte-Lehrbüchern von Kingreen/Poscher, Grundrechte. Staatsrecht II, und Degenhart, Staatsrecht I. Staatsorganisationsrecht.Für die Neuauflage wurden sämtliche Kapitel und Hausarbeiten überarbeitet und aktualisiert. Hinzugekommen ist eine Fallbearbeitung zu grundrechtlichen Fragen einer Impfpflicht. Das Kapitel "Gestaltungsrichtlinien" behandelt vertiefter als zuvor die an Bedeutung gewinnenden Online-Quellen (einschließlich Blogs). Den Leserinnen und Lesern ist ein gewinnbringender Fundus an Prüfungs- und Lehrerfahrung weiterhin sicher!