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Hands-On Financial Trading with Python

You're reading from   Hands-On Financial Trading with Python A practical guide to using Zipline and other Python libraries for backtesting trading strategies

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781838982881
Length 360 pages
Edition 1st Edition
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Authors (2):
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Sourav Ghosh Sourav Ghosh
Author Profile Icon Sourav Ghosh
Sourav Ghosh
Jiri Pik Jiri Pik
Author Profile Icon Jiri Pik
Jiri Pik
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to Algorithmic Trading
2. Chapter 1: Introduction to Algorithmic Trading FREE CHAPTER 3. Section 2: In-Depth Look at Python Libraries for the Analysis of Financial Datasets
4. Chapter 2: Exploratory Data Analysis in Python 5. Chapter 3: High-Speed Scientific Computing Using NumPy 6. Chapter 4: Data Manipulation and Analysis with pandas 7. Chapter 5: Data Visualization Using Matplotlib 8. Chapter 6: Statistical Estimation, Inference, and Prediction 9. Section 3: Algorithmic Trading in Python
10. Chapter 7: Financial Market Data Access in Python 11. Chapter 8: Introduction to Zipline and PyFolio 12. Chapter 9: Fundamental Algorithmic Trading Strategies 13. Other Books You May Enjoy Appendix A: How to Setup a Python Environment

Understanding financial asset classes

Algorithmic trading deals with the trading of financial assets. A financial asset is a non-physical asset whose value arises from contractual agreements.

The major financial asset classes are as follows:

  • Equities (stocks): These allow market participants to invest directly in the company and become owners of the company.
  • Fixed income (bonds): These represent a loan made by the investor to a borrower (for instance, a government or a firm). Each bond has its end date when the principal of the loan is due to be paid back and, usually, either fixed or variable interest payments made by the borrower over the lifetime of the bond.
  • Real Estate Investment Trusts (REITs): These are publicly traded companies that own or operate or finance income-producing real estate. These can be used as a proxy to directly invest in the housing market, say, by purchasing a property.
  • Commodities: Examples include metals (silver, gold, copper, and more) and agricultural produce (wheat, corn, milk, and more). They are financial assets tracking the price of the underlying commodities.
  • Exchange-Traded Funds (ETFs): An EFT is an exchange-listed security that tracks a collection of other securities. ETFs, such as SPY, DIA, and QQQ, hold equity stocks to track the larger well-known S&P 500, Dow Jones Industrial Average, and Nasdaq stock indices. ETFs such as United States Oil Fund (USO) track oil prices by investing in short-term WTI crude oil futures. ETFs are a convenient investment vehicle for investors to invest in a wide range of asset classes at relatively lower costs.
  • Foreign Exchange (FX) between different currency pairs, the major ones being the US Dollar (USD), Euro (EUR), Pound Sterling (GBP), Japanese Yen (JPY), Australian Dollar (AUD), New Zealand Dollar (NZD), Canadian Dollar (CAD), Swiss Franc (CHF), Norwegian Krone (NOK), and Swedish Krona (SEK). These are often referred to as the G10 currencies.
  • The key Financial derivatives are options and futures – these are complex leveraged derivative products that can magnify the risk as well as the reward:

    a) Futures are financial contracts to buy or sell an asset at a predetermined future date and price.

    b) Options are financial contracts giving their owner the right, but not the obligation, to buy or sell an underlying asset at a stated price (strike price) prior to or on a specified date.

In this section, we learned about the financial asset classes and their unique properties. Now, let's discuss the order types and exchange matching algorithms of modern electronic trading exchanges.

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Hands-On Financial Trading with Python
Published in: Apr 2021
Publisher: Packt
ISBN-13: 9781838982881
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