The Skinny On Options Data Science

Evaluating Models

| Apr 14, 2016
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    The Skinny On Options Data Science

    Evaluating Models

    Apr 14, 2016

    What would you say if we told you we would explain why tastylivers use the strategies they do by examining how security personnel catch terrorists at airports or physicians find people who have cancer? Dr. Data, the head of our research team, is here to make these predictive models all easily understandable.

    How do you stop a terrorist at an airport? One way would be to assume that everyone there is a terrorist. That might work but it’s more likely no one would ever get anywhere. Mike explains the relative importance of “precision” versus “sensitivity”. The first table shows the results of a screening test for cancer of 1000 patients. The goal is to find the right balance between too many false positives (sensitivity) and too many false negatives (precision). We need a very low number of false negatives because we don’t want someone going undiagnosed who has cancer. We can tolerate more false positives because further testing will reveal the good news, but there is some type of limit. The model for pharmaceutical companies when creating a screening test for cancer favors sensitivity over precision.

    The next table breaks down trading strategies comparing what the model predicts as a profit or loss and what actually ends up a profit or loss. Here we are not so concerned with false negatives. When trading, if the model predicts a loss when it actually would be a profit (equal to a false negative) we want to minimize those occurrences but if we miss some that’s not so bad. We are more concerned with avoiding the true negative strategies that our model and our experience tell us will be losers.

    This also explains why we are interested in trading high implied volatility (IV), making high probability trades and using High Implied Volatility Strategies. That allows us to maximize the situation in which our model correctly predicts a profit (true positive) and minimize using strategies that our model correctly predicts will be a loss (true negative). We can wait for high IV and high implied volatility rank (IVR) opportunities. Waiting for the high IV and high IVR opportunities is precision. Increasing our occurrences during such high IV/high IVR periods is increasing our sensitivity.

    Watch this segment of “The Skinny On Options Data Science” with Tom Sosnoff, Tony Battista and Dr. Data (Michael Rechenthin, Ph.D.) from the research team for the important takeaways and a better understanding of the reasoning behind the tastylive method of trading and the tradeoffs between precision and sensitivity in predictive models.

    This video and its content are provided solely by tastylive, Inc. (“tastylive”) and are for informational and educational purposes only. tastylive was previously known as tastytrade, Inc. (“tastytrade”). This video and its content were created prior to the legal name change of tastylive. As a result, this video may reference tastytrade, its prior legal name.

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