A way to determine the probability of success of a strategy or strategies in which the inputs are stochastic or have complex relationships is known as a “Monte Carlo” Simulation. It is powerful, yet computationally expensive. Closed form solutions are less flexible, yet more efficient. We used this analysis tool to provide more proof of the soundness of the tastylive philosophy. Dr. Data is here and will use his tools of Data Science to make it understandable.
An example of simulating with a Monte Carlo Simulation would be to look at the trading account of “Carlos Monte”. Carlos begins with a $25,000 account. Carlos shorts option premium by selling 1 Standard Deviation (SD) Strangles each month. The Monte Carlo Simulation would tell us how much his account would be worth after a year has passed.
There are four steps to our simulation. The first is to start with a distribution which are the results of selling 1 SD Strangles and holding to expiration. We then apply this to a simulated trader. We add the P/L to the starting account size.. We then repeat this for 1000 different simulated traders. Finally, we analyze the results. A table displayed the data after 6 months, one year and two years for the percentage of profitable accounts, the average dollar amount per account the top 5% and the bottom 5%. The results were impressive and did not use tastylive methods such as managing winners at 50% of max profit and rolling losing trades to take advantage of duration. Our studies indicate doing so would further improve the results. Taking the simulation out 4 years and 96% of all the “traders” are profitable and that is also without managing winners, etc.
For more on some of the strategies and concepts discussed here see:
Market Measures on August 20th, 2015: "Mechanically Managing Strangles"
Market Measures on February 18th, 2016:"Rolling Strangles"
Watch this segment of “Skinny on Options Data Science” with Tom Sosnoff, Tony Battista and the head of our research team Dr. Data (Michael Rechenthin, Ph.D.) for the valuable takeaways and more information on why “trade small, trade often” improves your chances of success.
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