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Stefan Jansen 
Hands-On Machine Learning for Algorithmic Trading 
Design and implement investment strategies based on smart algorithms that learn from data using Python

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The 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, Py MC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spa Cy, 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 Py Torch 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 Open AI Gym.

€47.99
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Language English ● Format EPUB ● Pages 684 ● ISBN 9781789342710 ● File size 36.4 MB ● Publisher Packt Publishing ● City Brookland ● Country US ● Published 2018 ● Downloadable 24 months ● Currency EUR ● ID 6813058 ● Copy protection without

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