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Machine Learning for Algorithmic Trading: Predictive models... by Jansen, Stefan

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Item specifics

Condition
Good: A book that has been read, but is in good condition. Minimal damage to the book cover eg. ...
Title
Machine Learning for Algorithmic Trading: Predictive models to...
Book Title
Machine Learning for Algorithmic Trading: Predictive models to...
ISBN
1839217715
EAN
9781839217715
Binding
Paperback / softback
Date of Publication
2020-07-31
Release Title
Machine Learning for Algorithmic Trading: Predictive models to...
Artist
Stefan
Brand
N/A
Colour
N/A
Publication Year
2020
Type
Textbook
Format
Paperback
Language
English
Publication Name
Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition
Item Height
93 mm
Author
Stefan Jansen
Publisher
Packt Publishing
Subject
Computer Science
Item Width
75 mm
Number of Pages
820 Pages

About this product

Product Information

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. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book 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 learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data Who 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.

Product Identifiers

Publisher
Packt Publishing
ISBN-13
9781839217715
eBay Product ID (ePID)
27046540245

Product Key Features

Publication Year
2020
Subject
Computer Science
Number of Pages
820 Pages
Language
English
Publication Name
Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition
Type
Textbook
Author
Stefan Jansen
Format
Paperback

Dimensions

Item Height
93 mm
Item Width
75 mm

Additional Product Features

Country/Region of Manufacture
United Kingdom
Title_Author
Stefan Jansen

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