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...
| Autor principal: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing
2018.
|
| Colección: | EBSCO Academic eBook Collection Complete.
|
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b44687916*spi |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.
