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Hands-on machine learning for algorithmic trading : design and implement investment strategies based on smart algorithms that learn from data using Python /
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 bu...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Jansen, Stefan, |e author. | |
| 245 | 1 | 0 | |a Hands-on machine learning for algorithmic trading : |b design and implement investment strategies based on smart algorithms that learn from data using Python / |c Stefan Jansen. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2018. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from title page (Safari, viewed February 25, 2019). | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 520 | |a 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Finance |x Data processing. | |
| 650 | 0 | |a Finance |x Statistical methods. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Finances |x Informatique. | |
| 650 | 6 | |a Finances |x Méthodes statistiques. | |
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| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Jansen, Stefan. |t Hands-On Machine Learning for Algorithmic Trading : Design and Implement Investment Strategies Based on Smart Algorithms That Learn from Data Using Python. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781789346411 |
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