Backtesting stocks is simple. Almost trivial because there are only a few parameters. Even when you introduce some sophisticated technical or trending analysis, it is still rather simple because stocks are fairly one dimensional – they go up, down or nowhere.
Backtesting options on the other hand has been a much more complicated task. Just a single option has so many dimensions affecting its price. Long/Short, Call/Put, Strike, ask/bid spread, Time to expiration, implied volatility, historical volatility, etc. Then you combine single options into a higher-level option strategy such as a Vertical Spread, Iron Condor, Calendars, etc and you want to start also dealing with net premiums, spread widths, wing width, short deltas, theta decay, gamma exposure, OTM%, etc. This is before we even start to think about other indicators that may help us enter, manage, or exit the position. To add fuel to the fire, there are a bunch of different option pricing models (i.e. Black-Scholes) that produce slightly different Greeks depending on the simplicity/complexity we want, american/european styled options, dividends, etc.
I was looking to test some claims made by many different authors on option strategies. What were good entry techniques, how and when do you manage bad trades, are Close-To-The-Money (CTM) trades better than their Far-out-of-the-Money (FOTM) brethren, are back-month trades really safer, do they perform well enough, are front-month trades really that risky to hold to expiration, and ultimately what are some good trading guidelines that will help me optimize my trades but are also within my comfort level. I am looking for data to provide me some answers to these questions, and to help me choose how to manage my trades.
There are some stock backtesting sites and programs available (most with major limitations). Aside from OptionVue (which had great data but major limitations for me including the cost) I could not find any site or platform that would backtest options (I’ve certainly not seen any that are customizable enough to allow you to program complex scenarios such as rolling spreads or entering positions when the VIX is within a certain range).
The first reason is probably the complexity – it is manageable but certainly much harder that stocks. But the second is simply the cost of the option data itself. While there is free end of day (EOD) historical stock data available on Yahoo, Google and plenty other sites; there is no free EOD historical option data available that I know of. This is primarily due to the shear volume of the data itself. Nine years worth of EOD historical data on a single index, the RUT, is roughly 200MB. I currently have EOD historical data dating back to 1990 for 2200 stocks and its 270MB. Roughly speaking, option data is 4,000 times larger on a per year basis.
So I felt compelled to build my own backtesting tool from scratch. And I purchased the historical data on the RUT to analyze. I would love to run a few tests against all the stocks listed on the S&P500, but the data is a little pricey.
My goal with this site is initially to share my observations and test results surrounding these backtests. I am not a statistician (if you are and you want to help… we should talk), but I will do my best. As I attempt to analyze different entry and exit parameters, I hope that others will contribute by recommending different scenarios I can test, recommending better methods for analysis, or just general observations. If the suggestions are easy enough or they are really compelling, I will implement them and publish results. I will usually try to make the result data (excel files) public as well.
I am initially looking at Iron Condor trades on the RUT. I personally use Iron Condors frequently, and they seem to be one of the most popular trades to start with. Can we optimize our trades by either improving profits or reducing risk?
Option I/O is named in reference to the engineering term Input/Output. I am hoping with the right inputs, I can garner the right outputs with my trades.