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Published by Packt Publishing, Limited, 2018
ISBN 10: 178934641X ISBN 13: 9781789346411
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Language: English
Published by Packt Publishing, Limited, 2020
ISBN 10: 1839217715 ISBN 13: 9781839217715
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Condition: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
Language: English
Published by Packt Publishing, Limited, 2020
ISBN 10: 1839217715 ISBN 13: 9781839217715
Seller: Better World Books, Mishawaka, IN, U.S.A.
Condition: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
Language: English
Published by Packt Publishing (edition 2nd ed.), 2020
ISBN 10: 1839217715 ISBN 13: 9781839217715
Seller: BooksRun, Philadelphia, PA, U.S.A.
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Published by Packt Publishing 7/31/2020, 2020
ISBN 10: 1839217715 ISBN 13: 9781839217715
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Paperback or Softback. Condition: New. Machine Learning for Algorithmic Trading - Second Edition. Book.
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Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
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Language: English
Published by Packt Publishing Limited, GB, 2020
ISBN 10: 1839217715 ISBN 13: 9781839217715
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.
Paperback. Condition: New. This thoroughly revised and expanded second edition demonstrates on over 800 pages how machine learning can add value to algorithmic trading in a practical yet comprehensive way. It has four parts that cover how to work with a diverse set of market, fundamental, and alternative data sources, design ML solutions for real-world trading challenges, and manage the strategy development process from idea to backtesting and evaluation.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Published by Packt Publishing 2020-07, 2020
ISBN 10: 1839217715 ISBN 13: 9781839217715
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Published by Packt Publishing Limited, GB, 2020
ISBN 10: 1839217715 ISBN 13: 9781839217715
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Add to basketPaperback. Condition: New. This thoroughly revised and expanded second edition demonstrates on over 800 pages how machine learning can add value to algorithmic trading in a practical yet comprehensive way. It has four parts that cover how to work with a diverse set of market, fundamental, and alternative data sources, design ML solutions for real-world trading challenges, and manage the strategy development process from idea to backtesting and evaluation.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Language: English
Published by Packt Publishing, Limited, 2020
ISBN 10: 1839217715 ISBN 13: 9781839217715
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. pp. 820 2nd Edition.
Language: English
Published by Packt Publishing Limited, GB, 2018
ISBN 10: 178934641X ISBN 13: 9781789346411
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
US$ 95.40
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Add to basketPaperback. Condition: New. 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.