| Description |
1 online resource (1 volume) : illustrations |
| Contents |
Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning for Trading; How to read this book; What to expect; Who should read this book; How the book is organized; Part I -- the framework -- from data to strategy design; Part 2 -- ML fundamentals; Part 3 -- natural language processing; Part 4 -- deep and reinforcement learning; What you need to succeed; Data sources; GitHub repository; Python libraries; The rise of ML in the investment industry; From electronic to high-frequency trading; Factor investing and smart beta funds |
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Algorithmic pioneers outperform humans at scaleML driven funds attract 1 trillion AUM; The emergence of quantamental funds; Investments in strategic capabilities; ML and alternative data; Crowdsourcing of trading algorithms; Design and execution of a trading strategy; Sourcing and managing data; Alpha factor research and evaluation; Portfolio optimization and risk management; Strategy backtesting; ML and algorithmic trading strategies; Use Cases of ML for Trading ; Data mining for feature extraction; Supervised learning for alpha factor creation and aggregation; Asset allocation |
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Testing trade ideasReinforcement learning; Summary; Chapter 2: Market and Fundamental Data; How to work with market data; Market microstructure; Marketplaces; Types of orders; Working with order book data; The FIX protocol; Nasdaq TotalView-ITCH Order Book data; Parsing binary ITCH messages; Reconstructing trades and the order book; Regularizing tick data; Tick bars; Time bars; Volume bars; Dollar bars; API access to market data; Remote data access using pandas; Reading html tables; pandas-datareader for market data; The Investor Exchange ; Quantopian; Zipline; Quandl |
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Other market-data providersHow to work with fundamental data; Financial statement data; Automated processing -- XBRL; Building a fundamental data time series; Extracting the financial statements and notes dataset; Retrieving all quarterly Apple filings; Building a price/earnings time series; Other fundamental data sources; pandas_datareader -- macro and industry data; Efficient data storage with pandas; Summary; Chapter 3: Alternative Data for Finance; The alternative data revolution; Sources of alternative data; Individuals; Business processes; Sensors; Satellites; Geolocation data |
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Evaluating alternative datasetsEvaluation criteria; Quality of the signal content; Asset classes; Investment style; Risk premiums; Alpha content and quality; Quality of the data; Legal and reputational risks; Exclusivity; Time horizon; Frequency; Reliability; Technical aspects; Latency; Format; The market for alternative data; Data providers and use cases; Social sentiment data; Dataminr; StockTwits; RavenPack; Satellite data; Geolocation data; Email receipt data; Working with alternative data; Scraping OpenTable data; Extracting data from HTML using requests and BeautifulSoup |
| Summary |
With the help of this book, you'll build smart algorithmic models using machine learning algorithms covering tasks such as time series forecasting, backtesting, trade predictions, and more using easy-to-follow examples. By the end, you'll be able to adopt algorithmic trading in your own business and implement intelligent investigative strategies |
| Notes |
Online resource; title from title page (Safari, viewed February 25, 2019) |
| Subject |
Machine learning.
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Python (Computer program language)
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Finance -- Data processing.
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Finance -- Statistical methods
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COMPUTERS -- General.
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Finance -- Data processing
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Finance -- Statistical methods
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Machine learning
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Python (Computer program language)
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| Form |
Electronic book
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| ISBN |
1789342716 |
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9781789342710 |
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