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Machine Learning for Trading. Integrate GenAI, Causal Inference, and Reinforcement Learning into Real World Trading Systems - Third Edition Stefan Jansen

Język publikacji: angielski
Machine Learning for Trading. Integrate GenAI, Causal Inference, and Reinforcement Learning into Real World Trading Systems - Third Edition Stefan Jansen - okladka książki

Machine Learning for Trading. Integrate GenAI, Causal Inference, and Reinforcement Learning into Real World Trading Systems - Third Edition Stefan Jansen - okladka książki

Autor:
Stefan Jansen
Serie wydawnicze:
Mastering
Ocena:
The 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.

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O autorze książki

Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions for a broad range of business problems.

Before his current venture, he was a partner and managing director at an international investment firm, where he built the predictive analytics and investment research practice. He was also a senior executive at a global fintech company with operations in 15 markets, advised Central Banks in emerging markets, and consulted for the World Bank.

He holds Master's degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin, and a CFA Charter. He has worked in six languages across Europe, Asia, and the Americas and taught data science at Datacamp and General Assembly.

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