Test 7: IV/HV

I thought it would be fun today to test whether it makes sense to use implied volatility as an entry criteria to our tests. Specifically, I am looking at whether the ratio of IV to HV (historic vol) will be beneficial.

I’ve heard that generally speaking it is a bad practice to sell premium when the HV is larger than the IV. We like to sell in high IV environments with less underlying movement (actually that is ideal). When IV is lower than HV, can we get enough premium to justify the potential large swings in the underlying? We are actually under that situation right now with the VIX at 13.49.

For this test, I construct rules that only allow trades to be entered when the IV/HV ratio is greater than (1.0, 1.1, 1.25, 1.5, 1.75, and 2.0). No other entry or exit parameters are used. Note that in this test I am only using the implied volatility of the front-month option chain calculated based on a weighted average of the ATM call and put IVs.


1 Month 2 Months 3 Months All
Improved PF 9(17%) 38(70%) 38(70%) 85(52%)
Improved MaxDD 54(100%) 54(100%) 54(100%) 162(100%)
Improved ExpRet 9(17%) 38(70%) 38(70%) 85(52%)

The initial pass is interesting; the back months show a much higher benefit that the front month. The heatmap shows this in even more detail:


The obvious conclusion is that if you find yourself with a 2x IV over HV, make the trade, but the data shows these are extremely rare (i.e. 6 trades in 9 years). Discounting these trades, we can see that generally speaking in the other cases using IV/HV as a marginal impact.

There are several trades that really stand out to me, all in the 2nd month. The 10-Delta, 15-Delta, and 18-Delta trades with IV/HV of 1.25 and 1.5 all saw at least 25 trades during this period and had substantial improvements. The 10-Delta 1.5 IV saw only winners. What is so special about this cluster of trades? Digging into the specific trades they show that they only have a few losses, but their neighbors have about the same number of trades and just 1 or 2 more losses. Those extra losses were enough to dramatically skew the results considering this is an expiration test and the losses were all max losses.

Now, what happens if we also include the early exit trigger from the previous test?


In this case we start to see pretty consistent and significant improvements in the back month trades. These are also fairly significant improvements over the early exit tests themselves. And note the cluster in the second month trades is no longer as significant.


On its own, the IV/HV ratio is not a great indicator to help enter trades. While there are scenarios where the ratio seems to help a great deal, these come at the cost of significantly less trades. However, it seems that paired with other triggers it may be more beneficial than not.

The higher the IV/HV ratio, the less number of trades that are going to occur. Is it worth it to wait until the IV is greater than the HV, in my opinion… yes if you are looking at back months. I am actually quite surprised that there was not a more dramatic effect for the front month since this test was using the front-month IV in its calculation. In the last 9 years there are only a few cases where the IV/HV ratio was less than one (i.e. HV greater than IV), and only one period where the HV was higher for sustained period of time.

Overall, I’m not convinced this tool is critical unless one is already in a low vol environment. If the HV is high, but the IV is also high, we would expect to be paid a decent premium and be able to get wider strikes, possibly nullifying the effect.

It is also interesting to note that the HV is a lagging indicator and the IV is a current sentiment indicator based on the market’s perception. Realized volatility in the past may not carry though to the time when HV is greater than IV. This would result in potentially missing trades good trades, and possibly allowing some bad ones.

Test Stats

Permutations 162
Profitable Scenarios 121 75%
Total Trades 7283
Profitable Trades 5212 72%
Expired 2442 34%
MaxDays 4841 66%
Test Duration 1.38(min)

Test Files

Excel Data

Test 6: Early Exits

Today I am going to test early exits. To date, my tests have held trades through to expiration unless a target or stop loss was reached. But many traders prefer to exit trades well before the expiration to minimize gamma risk. Of course, one would miss out on the largest theta decay, but theta and gamma often are opposing greeks.

The general sense I get is that those entering front month trades attempt to hold though to expiration, possibly exiting with 1 week left. Those entering the back months tend to be out of the trade with at least 3 weeks left to go. This run will test exiting 1 through 4 weeks prior to expiration, with no other entry or exit parameters. With this test, I am simply hoping to find a best practice depending on the expiration month.


1 Month 2 Months 3 Months All
Improved PF 18(50%) 35(97%) 28(78%) 81(75%)
Improved MaxDD 36(100%) 36(100%) 36(100%) 108(100%)
Improved ExpRet 12(33%) 24(67%) 26(72%) 62(57%)

The table shows that where were substantial improvements in the Profit Factor for the back month. In all cases the risk was reduced, but in the majority of cases in the back months the expected returns were also improved.

Looking at a heatmap for the improvement in profit factor over the baselines:


We can wee that front month actually saw an improvement when the trades were exited 7 days prior to expiration. And very high delta trades may also see an improvement at all exit periods. However most other trades saw marginal or negative improvements as we might expect from these trades.

The back month trades are another matter. Clearly exiting early has a benefit on nearly all these trades. Exiting 3 weeks prior seems to be a general sweet spot for both back month trades, however, the 3-month 10-Delta trade saw the largest improvement when held to the last week.

So there is something to exiting early. Lets see if we can nail down some better rules. I reran the test, but this time included all days between 3 and 28 prior to expiration.


With this heatmap I removed all the weekend days and then color coded each row individually. Green reflects the most improvement and red the worst by delta. Red does not necessarily mean bad as any value over zero is an improvement over the baseline. The aim is to show the best exit point for each expiration month and delta. For example, if you were to enter a trade with the front month and a 30 delta, an exit with 21 days remaining may actually be in your interest.

What is interesting about this map is that several patterns emerge.

  • Exiting 22 days for the 3 month trade is generally optimal except for the delta-10 trades where holding to 4 days before seems to be beneficial. This is very anomalous.
  • Exiting between 17 and 22 days for the 2 month trades has the highest benefit.
  • Exiting early in the front month generally has marginal to negative improvement. Low delta trades may benefit from exiting just prior to expiration. Otherwise they are harmed. Higher delta trades show a marginal benefit to exiting early at most points with the largest gain being 17-22 days prior (note these would be very short term trades).
  • Exiting 2 weeks prior on the Friday seems to perform much worse than if you were to exit a few days before or after that point. Why two weeks before? This pattern is not seen at 3 or 4 weeks on fridays.

Now, why is there an abnormality around the 3-Month, 10-Delta, Day-4 exit trade? It performs significantly better compared to its neighboring trades. The neighboring trades on the same delta have identical entry points and the same number of trades. However, when exited one day earlier (day 5) the scenario wound up with 4 more losses. Similarly, the neighbor in the 15-delta row had a few more total trades, but also experienced 4 more losses. Given that many of the trades were identical and that just exiting one day vs another made such a difference in 5% of the trades, I suspect this is just a statistical anomaly that would be smoothed out over time with more trades/data.


Clearly there is a benefit to exit back-month trades earlier. It substantially decreases the risk of the trade, and in many cases leads to higher expected returns probably by missing out on larger losses. Exiting between 18 and 22 days appears to lead to the largest benefit.

For most of my tests going forward unless noted otherwise, I exit trades for back month trades based on this data.

Test Stats

Permutations 702
Profitable Scenarios 584 83%
Total Trades 141185
Profitable Trades 91969 65%
MaxDays 141185 100%
Test Duration 1.56(min)


Excel Data

Test 5: Targets, Stop Losses and Min Premium

Now its going to get more interesting. We combine Profit targets, Stop Losses and Minimum Premiums together to see if combined they are greater than individually.

For this test, I am going to use the same Profit Target and Stop Loss parameters in the past tests. I am also going to use the historical average premiums as an entry point into the trades (i.e. the minimum premium is set to the average historical premium).

High Level Breakdown

The breakdown suggests that once again the front-month trades are harmed by these trade parameters, but the 2nd and 3rd month trades find improvements.

1 Month 2 Months 3 Months All
Improved PF 211(23%) 558(62%) 657(73%) 1426(53%)
Improved MaxDD 900(100%) 900(100%) 900(100%) 2700(100%)
Improved ExpRet 218(24%) 359(40%) 553(61%) 1130(42%)

Profit Factor Improvement

A heat map of the profit factor improvement (over the baseline) shows much more detail. Generally speaking, all parameters decrease performance for front month trades. The high delta trade for the front month does show marginal improvement consistently, but not enough for me to be confident.


The second and third month trades are a different story. It is hard to find an exact pattern, but we can claim that generally adding these parameters improves performance of these trades, with substantial gains for lower delta trades (i.e. Delta 10). The Delta 10 trades in the second month and some of those in the third month have massive improvements in performance compared to the baseline. Ideally I would like to see the neighboring trades (i.e. Delta 15) also experience similar but diminished gains in order to point to a possible pattern, but that is not the case (it could be that my resolution is not fine enough and that Delta 12 trades show that diminished improvement).

Looking deeper at the specific trades of one neighboring trades (all things being the same except the delta), its trades experienced 6 losses instead of one. This may add to the credibility that the low delta trades improvement was not just due to luck.

Expected Returns

A heatmap of the expect returns of each scenario shows that generally speaking

  1. High Delta trades still perform poorly regardless of month
  2. Very Low target trades perform poorly,
  3. Low stop loss trades perform worse
  4. Front Month trades perform best when the target is > 70%, Deltas <= 25, and stop losses > 30%
  5. Second and Third Month trades perform best when Target >= 30%, Stop Losses >= 20%, and Deltas <= 25%


We can also look at the top three trades for each expiration period so gain similar insights.

Month Delta Target StopLoss ExpRet MaxDD PF WinRatio Trades AvgDays
1 1st 0.10 90% -90% 6% -127% 2.2 93% 89 20.8
2 1st 0.10 90% -100% 6% -127% 2.2 93% 89 20.8
3 1st 0.10 90% -60% 6% -127% 2.2 92% 90 20.4
1 2nd 0.10 40% -60% 6% -71% 10.4 99% 115 19.1
2 2nd 0.10 40% -70% 6% -71% 10.4 99% 115 19.1
3 2nd 0.10 20% -70% 3% -71% 8.7 99% 180 10.1
1 3rd 0.10 20% -70% 3% -78% 7.1 99% 151 15.2
2 3rd 0.10 20% -80% 3% -100% 5.5 99% 151 15.2
3 3rd 0.10 20% -90% 3% -100% 5.5 99% 151 15.2

Those are some impressive win ratios. Those with a 99% win ratio only experience a single loss, and it was during the Oct 2008 market crash.

It is also interesting to note that as the expiration period increases, the expected return decreases by so does the average duration of the trade. Trades with shorter duration may be sitting on the sidelines frequently.


The combination of targets, stop losses and minimum premiums seems to substantially improve low delta trades in the second and third month, and to a lesser degree most of the other trades in those expiration period. But again, while the risk in the front month trades was reduced, the overall profit factor of almost all trades was also reduced.

Test Stats

Permutations 2700
Profitable Scenarios 1745 65%
Total Trades 526411
Profitable Trades 229165 44%
Expired 59983 11%
StopLoss 48866 9%
TargetMet 417562 79%
Test Duration 2.60(min)

Test Files

Excel Test Results

Test 4: Minimum Premiums

This test will use minimum premiums on trades to determine whether this simple addition to our entry criteria can generate alpha.

Adding minimum premiums is actually a little complicated as the question is really relative to what. The first part of this test will blindly accept trades if the trade is over a given premium. The variety of premiums I use are:

1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4, 4.25, 4.5, 4.75, 5.00

This creates 459 different scenarios for the three different expiration period. Now some of these premiums don’t make sense and/or result in duplicate scenarios. For example a $4 premium on a front-month trade with a delta of 10 is pretty hard to come by (we found none). But in the analysis, I will remove scenarios that don’t meet a minimum of 25 total trades.

Summary Breakdown

1 Month 2 Months 3 Months All
Improved PF 17(17%) 14(13%) 18(16%) 49(15%)
Improved MaxDD 100(100%) 107(100%) 112(100%) 319(100%)
Improved ExpRet 22(22%) 15(14%) 19(17%) 56(18%)

The summary shows that again we find the adjustment reduces the risk in all cases. Surprisingly the addition of a minimum premium does not generally help the expected return, but again, we are looking at many unrealistic trades. So we need to dig deeper.

Many of the trades actually matched the baseline. That is the minimum premium was low enough that all trades found in the baseline test, matched the scenario. If I strip out those trades and we look at a heatmap of the improvement in the Profit Factor we can see that the PF for most trades is marginally affected.


Looking deeper still, the average number of trades for this test is about 20% smaller than the baseline. Since the baseline tests actually performed well on average, this minimum premium threshold is preventing us from participating in more successful trades (and potentially allowing negative ones when the volatility of the market is high).

Average Premiums

So using random minimum premiums was a wash. I wonder whether using minimum premiums based on the average premium for each delta and period would be of benefit. I wrote a program to collect the average premiums for all iron condors found for all deltas between 4 and 40 on every trading day in the first 3 months. For example, the program would find the average price for a RUT IC entered with a delta of 12 with 56 trading days left till expiration.


With the exception of the very last day of trading where premiums appear to perk up, these patterns resemble the theta decay we would expect. I double checked the premium on a delta 40 trade today (the last day of trading in Dec expiration) and it matches the chart above. The tests don’t initiate trades anywhere near the last day, so this anomaly is ignored.

Running the test against the average premium results in only 27 permutations  but doesn’t yield any great advantage either:

1 Month 2 Months 3 Months All
Improved PF 2(22%) 1(11%) 3(33%) 6(22%)
Improved MaxDD 9(100%) 9(100%) 9(100%) 27(100%)
Improved ExpRet 3(33%) 1(11%) 3(33%) 7(26%)


Again, the change in PF is overall negative, but the changes are generally marginal.

I didn’t really expect using the average price to help alot since it is after all – average.

Average + 1 SD

Lets try for something much better than average. I take the average price plus 1 standard deviation. This means that the minimum price should be better than 84% of the historical prices.


The standard deviation as a fair amount of noise to the prices, so I am actually going to smooth it out using a very small moving average. This still results in 27 scenarios.

1 Month 2 Months 3 Months All
Improved PF 6(67%) 2(22%) 3(33%) 11(41%)
Improved MaxDD 9(100%) 9(100%) 9(100%) 27(100%)
Improved ExpRet 7(78%) 2(22%) 4(44%) 13(48%)

Now we are starting to finally see some improvements, but they are mostly in the front month trades. But again, the changes are all marginal.



I would have thought that adding minimum premiums to our trade would improve the performance but it seems to keep us in most of the loosing trades and away from many of the other winning trades. The average number of trades is reduced in most scenarios, and is substantially reduced in some to the point that we consider them too small to rely on.

I tried several variations of setting minimum premiums and all seemed to lead to marginally better or worse performance. Every trade helped reduce the risk, but the offset in lost expected return reduced the performance further.

Summary Stats

Permutations 459
Profitable Scenarios 190 41%
Total Trades 30368
Profitable Trades 19709 65%
Expired 30368 100%
Test Duration 1.38(min)

Test Files

Minimum Premium Test Data

Average Premium Data

Min. Premium using Historical Avg Test Data

Min. Premium using 1 SD Test Data

Test 3: Targets and Stop Losses

Profit targets were found to be helpful, and Stop Losses less so, but can the combination of the two be greater than the sum of the parts?

Test Setup

For this test I create scenarios that include all profit targets and all stop losses from the previous two tests. Targets and Stop losses from 10% to 100% will be used. Ultimately this creates 2700 unique scenarios across the first three months.

Now that we have four input parameters (Delta, Days to Expiration, Profit Targets, and Stop Losses) for every scenario, analyzing the general trends is much harder. Ideally I need a holographic projection to show the data in three dimensions – but the budget here at Option I/O is low, so we will have to make due.

Highlevel BreakDown

We start by looking at the high-level break down of the tests.

1 Month 2 Months 3 Months All
Improved PF 200(22%) 645(72%) 713(79%) 1558(58%)
Improved MaxDD 900(100%) 900(100%) 900(100%) 2700(100%)
Improved ExpRet 202(22%) 441(49%) 573(64%) 1216(45%)

This are pretty surprising results to me. There was no question that adding stop losses and targets limits should improve the risk for every trade since we saw that in the last two tests. But I thought the combination of the two would help with the over all expected return as well. But what we see is the Front Month trades are resoundingly impacted by adding stops and limits, however the 2nd and 3rd month trades have a definite improvement.

Looking at a heatmap of this data shows more detail.

This heatmap is a little complicated to read. The major columns are the expiration months of the trades, the minor columns are the deltas. The Major rows are the profit targets, and the minor rows the stop losses.

This heatmap is a little complicated to read. The major columns are the expiration months of the trades, the minor columns are the deltas. The Major rows are the profit targets, and the minor rows the stop losses.

This data does not mean that any of these trades are necessarily bad trades. This is simply a comparison to the baseline test. The red indicates the trade is worse than holding to expiration based on the Profit Factor. Green is an improvement.

Here are some of the takeaways from this data:

  • Front-month trades are heavily impacted until the profit target is ~100%. Even then adding stop losses seem to help some, but not all the trades. And most gains or losses are marginal.
  • Almost all CTM trades in the 2-3 month time frame were improved.
  • Very low stop losses are stopped out a lot and are generally detrimental to the trade.

Looking at the heatmap in another way (not posted here but in the data file), I see a rough pattern where in the 2-3 month trades, the largest benefit is received by high delta/low target trades though to low delta/high target trades. There seems to be a sweet spot depending on the delta of the trade. You can see in the data that as the delta is decreased the sweet spot moves.

One final point in this section, the largest benefit goes to the CTM trades. This can easily be seen in the following chart. The DiffPF is the improvement in the Profit Factor over the baseline.


Expected Returns

With 2700 points, this is also a little hard to read, but this graph shows the general trends based on the delta and month of the trade. Most Delta 10 through 20 trades appear to have a positive expected average. While most with 30 and above do not.


Top 3 Trades for each Month

The following table shows the top three trades (by Profit Factor) for each of the different expiration months.

Month Delta Target StopLoss ExpRet MaxDD PF WinRatio Trades AvgDays
1 1st 0.10 90% -90% 6% -100% 2.5 95% 112 21.7
2 1st 0.10 90% -100% 6% -100% 2.5 95% 112 21.7
3 1st 0.10 90% -70% 5% -100% 2.2 94% 112 21.6
1 2nd 0.10 90% -40% 7% -100% 2.7 92% 102 41.7
2 2nd 0.10 90% -80% 7% -100% 2.7 95% 100 43.2
3 2nd 0.10 90% -90% 7% -100% 2.6 95% 100 43.2
1 3rd 0.10 30% -90% 3% -100% 3.3 99% 136 24.9
2 3rd 0.10 30% -100% 3% -100% 3.3 99% 136 24.9
3 3rd 0.10 60% -90% 6% -100% 3.2 97% 90 44.6

What is interesting to note is that many of these trades are very close to the baseline test. It’s also interesting to note that all the trade are with a delta of 10. Personally I would have thought that all the top trades for any month would have been slight variations of each other, but two trades stand out as different. The 2nd month 90/40 trade and the 3rd month 30/90 trade.

Top 3 Trades by Trading Style

I thought it would be interesting to also start listing the best trades by style.
(See my previous article about trader styles here.)

Month Delta Target StopLoss ExpRet MaxDD PF WinRatio Trades AvgDays
FrontMo-FOTM 1 1 0.10 90% -90% 6% -100% 2.5 95% 112 21.7
FrontMo-FOTM 2 1 0.10 90% -100% 6% -100% 2.5 95% 112 21.7
FrontMo-FOTM 3 1 0.10 90% -70% 5% -100% 2.2 94% 112 21.6
FrontMo-CTM 1 1 0.15 100% -30% 8% -112% 2.0 82% 113 23.3
FrontMo-CTM 2 1 0.18 100% -40% 9% -120% 1.9 79% 114 23.1
FrontMo-CTM 3 1 0.15 90% -30% 6% -124% 1.8 82% 114 21.9
BackMo-FOTM 1 3 0.10 30% -90% 3% -100% 3.3 99% 136 24.9
BackMo-FOTM 2 3 0.10 30% -100% 3% -100% 3.3 99% 136 24.9
BackMo-FOTM 3 3 0.10 60% -90% 6% -100% 3.2 97% 90 44.6
BackMo-CTM 1 2 0.15 90% -70% 11% -200% 2.4 92% 103 47.4
BackMo-CTM 2 2 0.20 30% -40% 6% -100% 2.4 91% 141 22.1
BackMo-CTM 3 2 0.18 50% -40% 8% -141% 2.4 88% 116 29.2

Again we see that for Front-month trades, the best scenarios are the same or close to the baseline. The back-month CTM trade however shows there may be an advantage with a 50% target and a 40% stop.


Surprising, Profit Targets and Stop losses do not have a major impact on the front month trades, but they do on the 2nd and 3rd month trades. They reduce the risk in every scenario. There is a sweetspot for the stop loss, but it is dependent on the delta of the trade.

Test Stats

Permutations 2700
Profitable Scenarios 1845 68%
Total Trades 679984
Profitable Trades 298187 44%
Expired 65216 10%
StopLoss 66814 10%
TargetMet 547954 81%
Test Duration 2.57(min)

Test Files

Excel Data

Test 2: Stop Losses

What level of stop losses are effective for managing iron condors? Our baseline test effectively had a 100% stop loss on all scenarios. Will adding stop losses based on the at risk margin (See the test on Profit Limits to learn why I use this metric) improve the performance of Iron Condor trades?

My guess before looking at the data is that stop losses will be shown to drastically reduce the maximum risk, and should be able to improve the effective expected return. Because some tests will be stopped out quiet frequently (i.e. those with a 10% stop loss threshold), I expect many tests to have a higher number of trades compared to the baseline.

Test Results

The test scenario use stop losses on the margin at risk for the following values:

10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%

The following summarizes the performance of the test run:

1 Month 2 Months 3 Months All
Improved PF 32(36%) 28(31%) 28(31%) 88(33%)
Improved MaxDD 90(100%) 90(100%) 90(100%) 270(100%)
Improved ExpRet 34(38%) 26(29%) 25(28%) 85(31%)

As expected the risk was substantially reduced in all cases. The vast majority of scenarios actually experienced lower expected returns and lower and lower profit factors. Meaning that the gains in risk were still outweighed by the loss in expected returns.

Profit Factor Improvement VS Delta

Looking at a histogram of the difference in profit factor compared to the baseline, we can see some patterns, but its not as clear cut. Very small stop losses perform poorly on average. And High Delta trades appear to perform poorly on average, although there is a cluster in the 3 month range and Delta 35 that does well. The clusters between the 30 to 60% stop losses and deltas under 22 seem to consistently outperform as well. But again, no very clearcut patterns emerge.




Expected Returns

Looking at the expected returns, we can see that there appears to be a sweet spot between -30% and -60%, and Deltas less than 22. Similar to what was displayed in the profit factor tables.


We can also see that regardless of the stop loss, high delta trades perform poorly. This is not surprising given the construction of the test.



Traders may often hold the Front Month trades to expiration but also use stop losses. This test shows that many of those scenarios can be improved marginally, but using a 30% to 50% stop loss on 15 to 20 delta trades seem to gain the most benefit.

The second month, low delta trades with a 30% to 50% stop loss may also be marginally improved.

The tests with Stop Losses between 10% to 20% are significantly impacted across all deltas and all months. These trades are just stopped out to frequently.

Most other tests are only marginally impacted either positive or negatively.

Test Stats

Permutations 270
Profitable Scenarios 174 64%
Total Trades 41402
Profitable Trades 17911 43%
Expired 19394 47%
StopLoss 22008 53%
Test Duration 1.52(min)

Test Files

StopLoss Excel Data

Test 1: Do Targets Help

Do adding simple profit targets help our trades or hurt them when compared to holding to expiration?

It seems simplistic, but I want to start out simple with these tests to see what particular adjustments add the most value. I want to test the very basic management techniques of setting profit targets and stop losses in isolation and in combination to see whether they actually provide value. Today I start with profit targets. My assumption is that profit targets will help reduce the risk of a trade due to exiting sooner, but the expected return will be lower. I am not sure what overall effect on the profit factor this will have. Lets dig in.

What Method Do We Choose

(I present a side discussion on why I choose my target and stop loss calculations. Feel free to move on to the next section to see results)

When testing targets or stop losses, there are three simple ways to calculate them:

  1. Return on Risk – The return on investment (ROI) or return on risk(ROR) is the gain divided by the actual cash at risk in the trade. For example, a trade that took in a $2 in premium on a 10 point wide condor, would be risking $8; if we were targting a 10% return, we would exit when we reached $0.80 in premium.
  2. Return on Margin – This is a percentage return on the full margin, that is all the cash that is indisposed during the trade. For example, a trade that took in a $2 in premium on a 10-point wide condor would have a margin requirement of $1000 per contract (even though we collected $200); if we had a target of 10%, we would be aiming for a return of $1.00 in net premium.
  3. On Premium – We can also set targets as a percentage of the premium collected on the trade. For example, again a trade that took in a $2 in premium on a 10-point wide condor, a 50% target would aim for $1 in net premium.

There is no incorrect method for a trader. But for my tests, I have to choose one method. For running the tests, On Premium actually provides the best granularity, but ROR is generally what I use for my own trades. Look at the following table as a comparison between the different methods. When we have a trade that takes $1 premium:

Profit Target   Stop Loss
On Risk On Margin On Prem   On Risk On Margin On Prem
          0.45           0.50           0.05 5% -0.45 -0.50 -0.05
          0.90           1.00           0.10 10% -0.90 -1.00 -0.10
          1.00           1.00           0.15 15% -1.35 -1.50 -0.15
          1.00           1.00           0.20 20% -1.80 -2.00 -0.20
          1.00           1.00           0.30 30% -2.70 -3.00 -0.30
          1.00           1.00           0.40 40% -3.60 -4.00 -0.40
          1.00           1.00           0.50 50% -4.50 -5.00 -0.50
          1.00           1.00           0.75 75% -6.75 -7.50 -0.75
          1.00           1.00           1.00 100% -9.00 -9.00 -1.00

Notice how quickly the increasing the target shows fruitless with both On Risk and On Margin. Regardless of how high the target is, we have reached are maximum profit. Alternatively, on the Stop Loss side, On Risk and On Margin do not reach their full loss until at or near 100%. But On Prem has the same issue the other two had with Profit targets; you would actually need to increase the stop loss to 900% to reach the full loss.

But if we change the premium collected to $3.00:

Profit Target   Stop Loss
On Risk On Margin On Prem   On Risk On Margin On Prem
          0.35           0.50           0.15 5% -0.35 -0.50 -0.15
          0.70           1.00           0.30 10% -0.70 -1.00 -0.30
          1.05           1.50           0.45 15% -1.05 -1.50 -0.45
          1.40           2.00           0.60 20% -1.40 -2.00 -0.60
          2.10           3.00           0.90 30% -2.10 -3.00 -0.90
          2.80           3.00           1.20 40% -2.80 -4.00 -1.20
          3.00           3.00           1.50 50% -3.50 -5.00 -1.50
          3.00           3.00           2.25 75% -5.25 -7.00 -2.25
          3.00           3.00           3.00 100% -7.00 -7.00 -3.00

On Risk and On Margin have far larger ranges. This presents another challenge, depending on the premium collected for each trade, the effective target range for both the On Risk and On Margin calculations fluctuates. This actually means that many test scenarios will end up duplicating results which will result in misleading statistics.

So On Risk and On Margin have fluctuating ranges when used for Profit Targets, but their ranges are more appropriate when used as Stop Losses. On Premium has fluctuating ranges when used for Stop Losses, but On Risk doesn’t.

Using On Premium for Profit Targets and On Risk for Stop Losses allows me to use a full range values (10% – 100%) for every scenario, eliminating needless duplications in tests. While this may or may not make intuitive sense to a trader, programmatically it is the correct course of action. It is quiet easy to convert from one form to another.

On a side note, I personally use many of these for different situations. I pay attention to both stop losses as a percentage On Risk and On Premium. I am comfortable setting a 2x  stop loss based on the premium I receive.

On to the Test

The test constructs different scenarios for a variety of deltas and profit targets for the first month, second month and third month expirations.

The test created 270 different permutations. The results were automatically compared to the baseline test (you can view the difference against the baseline with columns named “Diff”) and summerized below:

1 Month 2 Months 3 Months All
Improved PF 26(29%) 79(88%) 88(98%) 193(71%)
Improved MaxDD 90(100%) 90(100%) 90(100%) 270(100%)
Improved ExpRet 26(29%) 59(66%) 68(76%) 153(57%)

Adding just profit targets lead to a large improvement for the longer term tests but less so for the front month scenarios. In every scenario it reduced our maximum drawdown, and surprisingly lead to higher expected returns for the majority of the tests.

If we look at a histogram of the improvement in the Profit Factor, we can see a noticeable pattern. The cells in the table show the difference between the current test run when compared to the baseline. A positive number would represent an improvement in the profit factor. The front-month tests perform worse than simply holding to expiration for the majority of the cases. But all other scenarios generally perform better.


Another way to view this data is by graphing the Profit Factor improvement against the delta:


Next we look at the Expect Returns of each scenario. The heatmap highlights the best performing scenarios. There is no surprise: holding trades to full profit results in larger expected returns. But notice that the high delta trades all perform poorly.


Finally, I look at one more interesting chart. The following chart shows the number of trades and their profit factor for each test. Notice how the front month trades were able to rack up many trades. These are very low profit target trades. They were able to achieve their profit targets very quickly, and put another trade on during the same month. But interestingly these scenarios that had lots of little profit trades also had huge losses and wound up with negative expected returns.ta-pf-vs-trades-split


Adding profit targets alone may improve the performance of longer duration trades so long as the Delta is below 30. However, for front month trades, the number of smaller profitable wins does not outweigh the fewer massive losses experienced when holding to expiration. In these cases while the downside risk is improved, the upside return is reduced far more leading to a negative scenario.

Test Stats

Permutations 270
Profitable Scenarios 198 73%
Total Trades 65071
Profitable Trades 30801 47%
Expired 6377 10%
Target Met 58694 90%
Test Duration 1.61(min)

Test Files

Target-Only Excel Data

Daily Bias

Is there one day that is statistically better to trade verses another?

I’m hoping the answer is no, otherwise something will be a skew with my perception of the option market, but I have to run a test to be sure.

I created a test that allowed each scenario to open a trade a specific number of days from expiration. The days ranged from 1 to 90 days testing all the days in the front three months. The test created all permutations of those days and the deltas

0.05, 0.07, 0.1, 0.15, 0.18, 0.2, 0.22, 0.25, 0.27, 0.3, 0.35, 0.40

For instance, the first scenario could open up trades 1 day from expiration but only with a 0.05 delta on the short strikes. This resulted in 1068 different scenarios. All trades were held to expiration.

After the test results were generated, I created a heatmap of the number of trades for each delta and day range (ignoring any that had less than 25 trades).


A very interesting pattern emerges. The green shows high number of trades, and the red lower numbers. There is a definite pattern where we find lots of trades for short expiration and small delta increasing linearly as days from expiration and delta are both increased. Conversely, trades are harder to find for low delta/long duration and high delta/short duration.

Another peculiar pattern emerges. Notice every 5 days or so the entire chain for that day it is harder to find acceptable trades compared to a day before or after. These bands all occur on Mondays. But after (too much) analysis, it turns out that these abnormalities are due to holidays. Many of the days when the market are closed are generally Mondays or Fridays leading to lower trades on those days. This confirms that my tests are finding the correct number of trades.

Next I create a heatmap of the average profit factors for every Delta and Day. I then eliminated all trades with excessively low number of trades (under 25). The results were a little surprising. In general, I would not except any particular days to stand out as being particularly good or bad to trade on. The map shows that generally, the higher delta trades perform worse; since these trades are held to expiration this is inline with the results we found in the baseline test. However, several anomalies also appear in the data.


First, look at the Day 2 row. This row actually reflects trading on the Thursday before expiration (expiration actually being Saturday, and no trading on Friday).  A value of 100.0 for the PF reflects that there were only winning trades. But the entire row has a very high PF. These are very low expected return trades, but consistently winners. These would be relatively binary trades that largely depend on the opening price the next day. A gap up/down on the expiration day could incur a big loss, but historically, has not.

It is also apparent from the first table that these trades are harder to come by. Usually the day before expiration, the frequency of deltas changes dramatically and less variety can be found.

Now I am not sure how easy it would be to get fills with low deltas just 1 day before expiration. Strictly speaking, these are trades that are assuming that there is negligible gap up or down the following Friday morning.

There are a few hotspots around the Delta-5 trades during days 10, 16, and 47. If I take a deeper look at the biggest hotspot (day 16) it turns out that it was just a statistical fluke. The days before and after (days 14 and 15) both incurred one more loss than did day 16. Day 16 had just 1 loss, and during the downturn in Nov 2008 it managed to eek out a profit when its neighbors took a loss. There were large swings in prices those days. And because of that, I can conclude that there was nothing special about trades on this day. The other days fall into very similar patterns. Just one less loss was able to catapult the profit factor for these scenarios.

I look at one final heat map on the data. This time, I want to look at which trades are profitable or not. I use the last heat map, but color all cells green that have a PF >1, otherwise red:


This is very similar to the last heat map, but the patterns are much more stark. Clearly, low delta trades do better when held to expiration than do high delta trades. There are a few outliers, but they have relatively close distance to their neighbors that they do not stand out as anomalous.


There appears to be no daily bias from the historical RUT data. Certainly, trades on the day prior to expiration day may statistically have an advantage, but for our test cases they have no room for management or adjustment.

Data Files

Daily Bias Test Excel Data


Permutations 1056
Profitable Scenarios 522 49%
Total Trades 67366
Profitable Trades 49815 74%
Expired 67366 100%
Test Duration 2.06(min)

Baseline Test

Defining a baseline test is necessary for all test runs so that we have something to measure the test’s parameters against.

The baseline test needs to be comprised of something common amongst all future test runs. Since future tests will primarily manipulate the exit triggers, the only truly common element are length of trade and the entry delta. Every test will test the Front, Second and Third Month. And every test will test roughly the same variety of entry deltas. The permutation of these variables creates 36 trades.

For the baseline we choose to hold these trades to expiration. This makes sense as a first attempt as any adjustment to the trade should probably be better (in either risk or reward) than holding to expiration if it is to be worthwhile. I would assume that most test runs with substantially improve the ‘risk’ component when compared to the base line, while possibly improving the reward. When holding to expiration, the risk is that we loose 100% of the margin associated with the trade.

First we look at the the Expected Return vs the Delta of each trade


Note that there may be a parabolic relationship with the data, and a local maximum appears to be around a delta 20 to 25 trade. The Delta 30 trades and above have negative returns.

Graphing the sum of the winning trades against the sum of the losing trades (in other words the Profit Factor) we can see a what might be another parabolic trend. There appears to be diminishing returns with trades that aim for higher profits.


Another way to look at the data is to look at the Sum of Profits Vs the Sum of Losses over Delta splitting out the trades by their duration. This view shows that there may be some tailing off for profits in any linear relationship to the delta when the delta’s get higher than 30; but the losses keep declining in almost a linear fashion. This again shows the diminishing returns.


I also like to look at the Number of trades VS the Profit Factor to ensure that trades with a high profit factor are not due to a very low sample size.


We can see that the Front Month trades found the highest number of trades. This is probably due to two factors. First, my tests aggressively look for more trades if targets are met early. So if a test exits within a few days, it can find another trade for the same test cycle. This would not be the case for this baseline test run as all trades are held to expiration. The second explination may have to do with the dataset itself, or the available trades. Further back in time, there appears to be fewer strikes available in the RUT. Few strikes, especially for back months would make it harder to find condors with a specific delta and width. The good news is that all scenarios  found I high number of trades. This graph will become more important in the future when applying different limitations to the test to ensure a sufficient number of trades occur.

It is interesting at this point to highlight that trade with the excessive PF. A test I ran to determine whether there is a Daily bias found an annomoly with low Delta (5-7) and very low duration (8-15 days from expiration) trades. I added a very specific case to test this scenario, and it seems to perform well with an Expected Return of 5.45%, and a Win Ratio of 94%.

Finally, I want to look at the Win Ratio. Some traders may not find it useful, but I do when it is used in conjunction with other metrics. This chart shows us what we should expect: there is a nearly linear relationship between the delta’s we choose and the chance of the trade being profitable.


What is very interesting to note is that the rule of thumb for delta’s does not seem to be applicable. The rule of thumb uses the delta as an estimate to the chance that the trade will be profitable if held to expiration. If our Iron Condor has short strikes with a delta of 25, each short strike has a 25% chance of not being profitable at expiration. Combined, the strikes have 50% chance of being profitable. However, the test show that historically with the RUT, these trades would have a 70-80% chance of being profitable.

Test Files

Baseline Excel Data

Test Statistics

Permutations 38
Profitable Scenarios 30 79%
Total Trades 3812
Profitable Trades 2905 76%
Expired 3812 100%
Test Duration 1.05(min)