Wins at option trading can be maximized by using Distribution Rank as option purchase filter.
When using Distribution Rank as a option purchase filter the best rate of winning for SPY Call options occurs when the 10 day distribution rank is 70% to 85%. The winning rate is at this level can be 3 times higher than when the distribution rank is between 15% to 45%. This shows the buying at the lowest possible price will not produce the best ratio of option trading wins.
The Bayes Analytic stock recommendations engine found that SPY Call options with the lowest 5% of prices yielded a 84% success while those from options purchased with a price 15% to 45% where low performers. Options with a 10 day distribution rank between 70% and 85% yielded the highest success rates. This is an example where the actual statistical output disagreed with my intuitive expectations.
The Bayes analytic option trading engine combines thousands of statistical measures to determine which offers are most likely to meet a specific goal. The statistics are combined to make a accurate and sensitive option purchase filter. The stock recommendation engine produces training database which contains valuable data nuggets I would never have discovered without the engines ability to identify them as important correlating facets. One such nugget is the strongly correlated value of distribution rank as a selection filter for successful option transactions.
The most interesting output is when you combine the many statistical facets like the full stock recommendations engine but some of the individual facets such as Distribution rank are valuable in isolation even if you do not have the full stock trading engine.
Distribution rank is one of the popular tools in statistics but for some reason it has never gained much popularity as an indicator for stocks and is not available as standard feature in many stock programs. I have analyzed many stocks and found that distribution rank can be a valuable option purchase filter for a majority symbols. I believe more attention should be given to Distribution rank as an option purchase filter.
The easiest way to think about Distribution rank is to take the set of all prices across a given time period and rank order them top to bottom. Then take any given price and figure out how many prices are higher than the current price which gives you a position. From this you can easily calculate that a given price is above 82.3% of all the other prices.
10 day Dist Rank Statistical Excerpt SPY Call Option
Data from 6/4/2013 SPY CALL option for goal 30% gain in 7 days
The distribution rank of the option purchase price compared against all option purchases in the last 10 days. This computation looks at each transaction available in the historical data set which in this instance is 6 months and then computes it’s distribution rank for the current interval which in this instance is 10 days. The results are shown here grouped into 5% buckets.
0% rank = 85% success of 71,786 transactions
5% rank = 78% success of 44,850 transactions
10% rank = 67% success of 31,905 transactions
15% rank = 59% success of 26,569 transactions
20% rank = 54% success of 25,114 transactions
25% rank = 47% success of 21,992 transactions
30% rank = 44% success of 18,922 transactions
35% rank = 39% success of 14,872 transactions
40% rank = 43% success of 14,436 transactions
45% rank = 49% success of 14,305 transactions
50% rank = 56% success of 15,295 transactions
60% rank = 74% success of 19,656 transactions
65% rank = 81% success of 24,482 transactions
70% rank = 86% success of 27,789 transactions
75% rank = 85% success of 28,449 transactions
80% rank = 85% success of 35,701 transactions
85% rank = 86% success of 50,115 transactions
90% rank = 80% success of 67,895 transactions
95% rank = 79% success of 134,993 transactions
100% rank= 78% success of 123,666 transactions
There is an interesting cluster of trading in the 95% to 100% buckets where a large portion of all trading actually occurs. There is also a lot of buying when the option price is at the highest price during the last 10 days. I think this represents traders trying to catch a wave that is rising and it may work for them but they could increase their chances of winning by simply waiting until the price drops into the 70% to 85% range.
Based on this analysis you should consider buying this option when the 10 day distribution rank is from 0 to 5% and then re-consider when it is between 70% to 85% rank. Skip buying those in the top 90% to 100% rank and never buy anything that is between 15% and 65% in price rank.
I do not suggest using any statistical filter in isolation but if you already have another option selection system then this filter may be able to increase your success rate.
Perhaps a more interesting metric would be combining the pricing distribution rank with the day of week and week of month to see if the correlation for success holds across all three when combined. If so it could be a very powerful option purchase filter. If I receive enough requests then I may publish such a comparison.
These metrics change for different goals. When using a 7 day goal the system will naturally optimize for short term movement which means that it reacts pretty quickly as compared to optimizing for longer periods of time. This can be a benefit but it increases the risk that the engine will buy-into a movement that is only partly completed.
In the full option trading engine I actually look at several different periods of distribution rank and it is the changes between these that represent some of the more valuable inputs as statistical features. It is also very difficult for a mortal human to compute these without custom software so I will not show them here.
I had about 6 months of option trading data loaded at the time of this calculation. Our data feed does not allow us to capture every trade but we are able to capture the last trade for every stock with generally less than a 2 minute resolution. We found that this costs us some detail but the engine can handle the extra data if we choose to pay for a real-time feed.
These numbers change across time so you really need a stock trading engine capable of recalculating easily to make it easy to include the output in your decisions. You should consider an engine which can look at how these success rates are changing across time to identify patterns needed so you know when to stop using measures that have been successful in the past or to boost their priority.
Thanks Joe Ellsworth
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