151 Trading Strategies
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151 Trading Strategies

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Description

The book provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation, global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around 2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students.
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Product details

  • Hardback | 480 pages
  • 155 x 235 x 30.48mm | 865g
  • Cham, Switzerland
  • English
  • 1st ed. 2018
  • 1 Illustrations, black and white; XX, 480 p. 1 illus.
  • 3030027910
  • 9783030027919
  • 1,834,906

Back cover copy

The book provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation, global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around 2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students.
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Table of contents

1. Introduction.- 2. Options.- 3. Stocks.- 4. Exchange-traded funds (ETFs).- 5. Fixed Income.- 6. Indexes.- 7. Volatility.- 8. Foreign Exchange (FX).- 9. Commodities.- 10. Futures.- 11. Structured Assets.- 12. Convertibles.- 13. Tax Arbitrage.- 14. Miscellaneous Assets.- 15. Distressed Assets.- 16. Real Estate.- 17. Cash.- 18. Cryptocurrencies.- 19. Global Macro.- 20. Infrastructure.
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About Zura Kakushadze

Zura Kakushadze received his Ph.D. in theoretical physics from Cornell University, USA at 23, was a Postdoctoral Fellow at Harvard University, USA and an Assistant Professor at C.N. Yang Institute for Theoretical Physics at Stony Brook University, USA. He received an Alfred P. Sloan Foundation Fellowship in 2001. After expanding into quantitative finance, he was a Director at RBC Capital Markets, Managing Director at WorldQuant, Executive Vice President and substantial shareholder at Revere Data (now part of FactSet), and Adjunct Professor at the University of Connecticut, USA. Currently he is the President and CEO of Quantigic (R) Solutions and a Full Professor at Free University of Tbilisi, Georgia. He has over 17 years of hands-on experience in quantitative trading and finance, 130+ publications in physics, finance, cancer research and other fields, 3,400+ citations and h-index 30+, 130,000+ downloads on SSRN, and over a quarter million followers on LinkedIn.



Juan Andres Serur holds a Master's Degree in Finance from the University of CEMA, Argentina. With more than 6 years of experience in trading in the stock market, he currently works as a quantitative analyst and strategist in an Argentine quantitative asset management firm and as a financial consultant for large corporations. In addition, he serves as the Academic Secretary of the Master of Finance Program at the University of CEMA, where he teaches undergraduate and postgraduate computational finance courses as an Assistant Professor. In 2016 he won the First Prize in an Argentine Capital Markets Simulation Challenge for Universities and Professional Institutions.
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