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Systematic Options Trading: Evaluating, Analyzing, and Profiting from Mispriced Option Opportunities
Sophisticated options traders need systematic, reliable approaches for identifying the best option combinations, underlying assets, and strategies. This book makes these approaches available for the first time. Leading-edge traders and researchers Sergey Izraylevich and Vadim Tsudikman treat the option market as a whole: an unlimited set of trading variants composed of all option combinations that can be constructed at any specific time moment (using all possible strategies and underlying assets). They introduce a system that permits thorough analysis and comparison of many option combinations in terms of both expected profitability and potential risk. For the first time, they formalize and classify more than a dozen criteria intended to select preferable trading alternatives from a vast quantity of potential opportunities, and show how to apply multiple valuation criteria concurrently to select the best possible trades. By applying these principles consistently, traders can systematically identify subtle price distortions using proven statistical parameters. They can gain a clear and consistent advantage over competing traders, transforming option trading into a continuous process of profit generation with tightly controllable parameters of risk and profitability.
289 pages, ebook
First published July 28, 2010
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August 12, 2023
I'm giving 4-stars just because the book managed to convince me that the ideas presented in it would work.
The writing is very dry. If I wasn't already doing my own research in the field and trying to build out my ideas, I wouldn't have made it through the book. The first part of the book is the most interesting - it discusses the various criteria you can use to rank all available option combinations. The rest of the book is aimed at presenting empirical evidence that the ideas work. I found the empirical evidence inadequate, but the author nevertheless managed to convince me that the ideas could work. The authors try to push convolving a few functions to get a single function as a novel idea but I found it utterly unhelpful. I don't believe that mixing all the different criteria to produce a single number is of any help. The book also contains a few mistakes. For example, the figures representing the probability densities of lognormal functions in chapter 2 have the wrong axis - different lognormal distributions with different variances end up having the same mode. I plotted them (scipy.stats.lognorm) to confirm that the figures are wrong. Some of the formulas do not have enough information to implement them. For example, the formula for historical volatility presented in chapter 2 seems very suspicious - if you follow the book you will end up with very low volatility and the criteria called "profit probability according to log normal" will almost always end up being > 95%. All this is to say that the ideas are useful but you will have to play around with them quite a bit to get actual results.
That being said, I couldn't find any other book out there that discuss the ideas presented in this book. It might be an imperfect work, but it is the only one we have, and I'm grateful to the authors for publishing it. At the end of the day, this dry book managed to do something that very few books manage to do - it inspired me.
The writing is very dry. If I wasn't already doing my own research in the field and trying to build out my ideas, I wouldn't have made it through the book. The first part of the book is the most interesting - it discusses the various criteria you can use to rank all available option combinations. The rest of the book is aimed at presenting empirical evidence that the ideas work. I found the empirical evidence inadequate, but the author nevertheless managed to convince me that the ideas could work. The authors try to push convolving a few functions to get a single function as a novel idea but I found it utterly unhelpful. I don't believe that mixing all the different criteria to produce a single number is of any help. The book also contains a few mistakes. For example, the figures representing the probability densities of lognormal functions in chapter 2 have the wrong axis - different lognormal distributions with different variances end up having the same mode. I plotted them (scipy.stats.lognorm) to confirm that the figures are wrong. Some of the formulas do not have enough information to implement them. For example, the formula for historical volatility presented in chapter 2 seems very suspicious - if you follow the book you will end up with very low volatility and the criteria called "profit probability according to log normal" will almost always end up being > 95%. All this is to say that the ideas are useful but you will have to play around with them quite a bit to get actual results.
That being said, I couldn't find any other book out there that discuss the ideas presented in this book. It might be an imperfect work, but it is the only one we have, and I'm grateful to the authors for publishing it. At the end of the day, this dry book managed to do something that very few books manage to do - it inspired me.
Displaying 1 of 1 review