Download or read book Option Income Strategy Trade Filters written by Brian Johnson and published by . This book was released on 2016-11 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brian Johnson, a professional investment manager with many years of trading and teaching experience, is the author of two pioneering books on options: 1) Option Strategy Risk / Return Ratios: A Revolutionary New Approach to Optimizing, Adjusting, and Trading Any Option Income Strategy, and 2) Exploiting Earnings Volatility: An Innovative New Approach to Evaluating, Optimizing, and Trading Option Strategies to Profit from Earnings Announcements. His new in-depth (100+ page) article, Option Income Strategy Trade Filters, represents the culmination of years of research into developing a systematic framework for optimizing the timing of Option Income Strategy (OIS) trades. His research was based on the analysis of 15,434 OIS trades, each with a comprehensive set of objective, tradable entry and exit rules. The results for each of the 15,000 plus trades were scaled to a constant dollar amount at risk, to ensure all trades were equally-weighted when calculating the performance metrics. The back-test results were all based on actual option prices and are summarized in this article for a selection of back-testing filters, making this one of the most comprehensive studies of option income strategy results ever published. The results of over 100 different back-tests are provided. The OIS strategy back-test results for ten different types of filters are evaluated in this article, including unique filter combinations that delivered exceptional results. A custom market-edge hypothesis was created in advance for each filter type, which was then used to evaluate the filter-specific results. This critical step helped identify robust, exploitable relationships, rather than spurious correlations. Several of the resulting filters generated over 95% winning trades, with average returns of over six percent per trade (including losing trades). The ratios of cumulative gains to cumulative losses were over 20 to 1 for a few of the best performing filters. Option Income Strategy Trade Filters is written in a clear, understandable fashion and provides detailed examples of how to create and test market-edge hypotheses using the recent advances in back-testing software. Very few formulas were included. As a result, the material in the article should be accessible to all option traders. Useful for traders with a wide range of option trading experience, this practical guide begins with a detailed review of option income strategies, including basic examples that provide the requisite foundation for subsequent chapters. Portions of this crucial background material also appeared in Brian Johnson's first book: Option Strategy Risk / Return Ratios. Chapter 2 includes a comprehensive description of the option income strategy, position model, and trade plan used to generate the back-test data. Every entry and exit rule is explained in detail, including actual graphical examples. The performance metrics for the 15,434 unfiltered OIS trades are summarized at the end of this chapter, which provide a performance benchmark for evaluating the effectiveness of the trade filters introduced in the next three chapters. The trade filters are grouped by classification, with a chapter devoted to each class or type. The market-edge hypotheses and corresponding results for trend filters are analyzed in Chapter 3. Unlike trend filters, discriminating filters exclude an increasing percentage of trades as the filter condition or threshold becomes more extreme or restrictive. The discriminating filter market-edge hypotheses and results are analyzed in Chapter 4. Chapter 5 is devoted entirely to a very unique and powerful example of a discriminating filter: the OIS Universal Filter (OISUF). The final chapter examines practical considerations and prospective applications of trade filters and other resources in managing option income strategies in actual market conditions.