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Hands-on algorithmic trading with Python.
Artificial intelligence in general and specifically machine learning are becoming increasingly important tools for many industries and enterprises. But one business sector in particular has long since adopted and benefitted from these powerful computing paradigms: investment services. In fact, over...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly Media, Inc.,
[2019]
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| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a Artificial intelligence in general and specifically machine learning are becoming increasingly important tools for many industries and enterprises. But one business sector in particular has long since adopted and benefitted from these powerful computing paradigms: investment services. In fact, over the past decade, few other industries and sectors have experienced the frenetic pace of automation as that of the investment management industry, the direct result of algorithmic trading and machine learning technologies. Industry experts estimate that today as much as 70% of the daily trading volume in the United States equity markets is executed algorithmically--by computer programs following a set of predefined rules that span the entire trading process, from idea generation to execution and portfolio management. But although all algorithmic trading is executed by computers, the rules for generating trades are either designed by humans or discovered by machine learning algorithms from training data. Not surprisingly, the ability to create these algorithms, particularly using Python, is in high demand. In this video course, designed for those with a basic level of experience and expertise in trading, investing, and writing code in Python, you learn about the process and technological tools for developing algorithmic trading strategies. You'll examine the pros and cons of algorithmic trading as well as the first steps you'll need to take to "level the playing field" for retail equity investors. You'll explore some of the models that you can apply to formulate trading and investment strategies. You'll also learn about the Pandas library to import, analyze, and visualize data from market, fundamental, and alternative, no-cost sources that are available online. You'll even see how to prepare for competitions that can fund your algorithmic trading strategies. (Note that live trading is beyond the scope of the course.) What you'll learn--and how you can apply it By the end of this video course you'll understand: The advantages and disadvantages of algorithmic trading The different types of models used to generate trading and investment strategies The process and tools used for researching, designing, and developing them Pitfalls of backtesting algorithmic strategies Risk-adjusted metrics for evaluating their performance The paramount importance of risk management and position sizing And you'll be able to: Use the Pandas library to import, analyze, and visualize data from market, fundamental, and alternative sources available for free on the web Design and automate your own specific investment and trading strategies in Python Backtest and evaluate the performance of your strategies using the Zipline library Prepare for competitions by crowd-sourced hedge funds such as Quantopian to fund your algorithmic trading strategies This video course is for you because... You're a retail equity investor, financial analyst, or trader who wants to develop algorithmic trading strategies and mitigate the disadvantages of emotional, manual trading You have Python development experience and want to learn how to apply that to open up opportunities in the financial services and investment industry Prerequisites: You should have basic experience trading and investing in equities You should have basic knowledge of Python and Pandas DataFrames You should be able to create a Google Colab document: https://colab.research.google.com/ Materials or downloads needed in advance: "Algorithmic trading in less than 100 lines of Python code" (article) "Getting Started with pandas Using Wakari.io" and " Algorithmic Trading" (Chapters 1 and 7 in Mastering pandas for Finance) "The Trinity of Errors in Financial Models" (article) Further resources: Quantitative Trading (book) Python for Finance, 2nd Edition (book) Hands-On Machine Learning for Algorithmic Trading (book). | ||
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