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Mastering Python for Finance

You're reading from   Mastering Python for Finance Understand, design, and implement state-of-the-art mathematical and statistical applications used in finance with Python

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Product type Paperback
Published in Apr 2015
Publisher Packt
ISBN-13 9781784394516
Length 340 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Python for Financial Applications FREE CHAPTER 2. The Importance of Linearity in Finance 3. Nonlinearity in Finance 4. Numerical Procedures 5. Interest Rates and Derivatives 6. Interactive Financial Analytics with Python and VSTOXX 7. Big Data with Python 8. Algorithmic Trading 9. Backtesting 10. Excel with Python Index

Introduction to algorithmic trading


In the 1990s, exchanges had already begun to use electronic trading systems. By 1997, 44 exchanges worldwide used automated systems for trading futures and options with more exchanges in the process of developing automated technology. Exchanges such as the Chicago Board of Trade (CBOT) and the London International Financial Futures and Options Exchange (LIFFE) used their electronic trading systems as an after-hours complement to traditional open outcry trading in pits, giving traders 24-hour access to the exchange's risk management tools. With improvements in technology, technology-based trading became less expensive, fueling the growth of trading platforms that are faster and powerful. Higher reliability of order execution and lower rates of message transmission error deepened the reliance of technology by financial institutions. The majority of asset managers, proprietary traders, and market makers have since moved from the trading pits to electronic...

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