SPX Weekly Verticals

On a forum I read, someone suggested a strategy similar to one that I have researched in the past. Their rules were simple:

  • Sell a 20pt Bull Put Spread on Thursday in the opening hours, expiring the next week
  • The Short strike needs to be at least 100 points from the current spot price
  • Sell the vertical when it reaches 0.05
  • Allocate 50% of your portfolio to this trade.

This strategy is predicated on the belief that the SPX does not move more than 100 points in an 8 day window and that weekly vol is expensive. Looking at the last 15 years, I find this is true 99.31% of the time. There are 26 instances out of 3752 samples where the market moved down more than 100 points. Several of these were during the crisis in 2008, 2001, but the most recent period was in Oct 2014. Fortunately for this strategy, it did not occur on a Thursday so it got a lucky pass.

Here are the rules for this strategy:

TVert100pt-Rules

I choose 10AM on thursday to place the trade. Note also that slippage and commissions are also taken into account.

I tested this strategy from Jan 2008 through to Dec 2014. The results are promising.

Weekly Verticals 100Pts from Spot

Overall, this trade performs very well post 2011.

Prior to 2011, it is relatively flat. Largely this is because, while there were weekly expirations, there were not enough strikes available in those expirations to find conforming trades. So much of the time it just sat out of the market, and traded the monthly expirations.

During this period the 2008 crash occurred and while the strategy had a large drawdown during that event, in the big picture it did quite well. But its hard to draw conclusions given it sat out the the market 75% of the time during this period.

From 2011 onward, the trade really picks up steam. It is entering a trade every week by this point. It is also during a bull run, so the trade as a wind at its back. But you can see the drawdowns (lower chart) are relatively small.

But why 100 points? I think it was chosen for the reason that the market almost never moves that much in that time period (99.31% remember). But 100 points today (5% move) is very different than a 100 points in 2009 (8-13% move). So I need to also look at percentage moves. I tested 4.5%, 5%, 6% and 7%.

4.5%

20pt Vertical Spread 4.5% from Spot

20pt Vertical Spread 4.5% from Spot

5%

20pt Vertical Spread 5% from Spot

20pt Vertical Spread 5% from Spot

6%

20pt Vertical Spread 6% from Spot

20pt Vertical Spread 6% from Spot

7%

20pt Vertical Spread 7% from Spot

20pt Vertical Spread 7% from Spot

All Tests

Vert-All

The 4.5%, 5%, and 6% tests where pretty severely impacted by the 2008 crash. Afterwards they all take on similar performance. Its pretty clear that back in 2008 staying a greater percentage distance made sense. The 100pt strategy is essentially a 8-10% strategy when volatility is high and a 5-8% strategy when low.

The summary statistics show that the 100pt strategy has a very good sharpe, and reasonable drawdown considering you are placing 50% of your portfolio at risk every week.

Stats

 

 

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Karen The SuperTrader

There have been quite a few articles/blog posts written about Karen Bruton or “Karen The SuperTrader”. Notably, TastyTrade has posted many video interviews with Karen, bringing her to fame within the option trading community. But other than speculation as to whether she is real (she is), or her performance is real (it probably is), or discussions about her rules – I’ve not seen anyone backtest similar strategies.

Karen runs a hedge fund, Hope Investments, and primarily uses short puts and short call options in order to generate superior returns. Her fund is open to accredited investors, and is not required to disclose their performance, but we believe she makes 30+% annual returns.

Her general rules follow:

  • Treat selling puts and selling calls as independent trades
  • Sell puts at 95% probability of success (about 2 standard deviations)
  • Sell calls at 90% probability of success
  • Sell puts between 40-56 days out
  • Sell Calls around 14 days out
  • Use 50% margin (up to 80% depending on the market)
  • Sell puts when the market swings down. Sell calls when the market swings up.
  • Adjust positions when they become 70% probability of success.
  • Use ~50% margin. Up to 80% depending on market conditions.

Her rules are fluid and other than what she has disclosed in the videos, proprietary. She has a team working full time monitoring and adjusting to the market. Replicating her exact strategy is impossible.

Selling strangles are not new. I’ve heard that a large percentage of CTA’s employ short strangle positions as they have a very high win ratio. Of course, one major market correction and your account could be liquidated. If you can avoid the market meltdowns, this strategy is lucrative.

Portfolio Margin

Portfolio Margin is used in this case when selling puts and calls. The calculation for PM is proprietary by the exchanges and brokerages, but in general they stress test your entire portfolio with a -12%/+10% move in the market in order to determine your required margin. Other variables such as volatility, and brokerage specific factors, and personalized risk factors also take effect. That is to say for the same positions, I may have slightly different requirements from one brokerage to another, or even from one account to another within the same brokerage. Even specific instruments will have different risk parameters. All of which are somewhat subjective and controlled by the brokerage and exchanges.

PM offers tremendous advantage and leverage over other options.

For example, lets look at the current SPX put trading 2SD and 53 days out:

SPX 2086.54
Exp 29-May-15
Strike 1875
Bid 6
Cash Margin 208654
Reg-T Margin 21244
Portfolio Margin 3411

PM requires 16% the margin than RegT does and 1.6% the margin a cash margin account would. But PM margin is much more fluid than the others. As the market drops, the margin requirement is increased. The details of PM margining are much more complex – but for the sake of simplicity and due to the fact I cannot calculate the PM margin, I will assume it is a constant percentage of RegT margin.

Short Puts

I’m going to focus on the short puts since the short calls are a smaller proportion to the overall profits. Calls are very cheap compared to the puts, and Karen reportedly sells half the number of calls to puts. I’m sure it adds to her bottom line, but for simplicity, I will leave them out for now.

The following shows the risk profile for the SPX 1875 short put as of the beginning of April..TOS-ShortPut

This trade stands to gain $600 if the SPX closes above 1875 in 52 days. But looking at the P/L in the price slices you can see just how rapidly this trade looses value as the market drops. If the market drops -12%, the trade would be under water by 7.4k. Actually, its worse than that as it does not take into account any changes to vega due to the drop.

With PM margining, the value at a -12% drop has to be greater than your account’s net liquidity. So if you have a $1M portfolio, you could sell 135 contracts. But as the market falls or volatility increases, your -12% calculation will increase and you will be placed into a close-only status.

Testing Rules

I’m not going to attempt to perfectly replicate Karen’s trades since that is impossible – but instead focus on the viability of selling short puts. Specifically:

  1. Sell Puts utilizing 50% of margin
  2. Sell Puts at 2SD
  3. Sell Puts between 40-56 days to expiration
  4. Open 1 trade a week. Ideally on a big down day. Each trade uses 25% of trade allocation (or 12.5% of margin)
  5. Only hold 4 open trades at a time
  6. Close puts early when they reach 80% of profit
  7. Returns are compounded.
  8. I approximate PM margin as 30% of Reg-T (2X what I found above).

Under the ideal situation, trades should be closed at 80% profit in about 4 weeks.

Short Puts – No Adjustments

I first want to see just how bad short puts can be.

KST-PutsOnly-NoAdj

If you were asleep at the wheel and did nothing to protect your position, your account is clearly liquidated, and rapidly. Note this is at 50% margin, but I have seen even using 25% margin when setting up the trades, would likely result in a margin call.

Short Puts – Sell at 30% PITM

KST-PutsOnly-pitm30%

70% probability of being OTM is important to Karen’s strategy (or 30% ITM). It makes sense. At about that point, Gamma starts accelerating. This test will simply close the trade at the ask when ever the delta of the put is > 0.30 (a rought approximation to 30% ITM).

Clearly, this alone dramatically helps protect the position. There are several large drops (-30-50%) but overall, the strategy is fairly profitable due to that long quite period in 2013-2014.

Short Puts – Roll Puts down at 30% PITM

But Karen does not simply closes her puts, she rolls them down, sometimes increases those positions (as I understand it) and sells more calls to help make up the loss.

KST-PutsOnly-pitm30-roll

This test shows the results of rolling down the puts and placing another 2SD trade at the same expiration. It is interesting to note that this makes matters worse. Like picking up pennies in front of a steam roller. The initial 2008 crash looses 75% of the account value. The subsequent drops are similar in magnitude to those without the rolls.

Conclusion

The 2008/2009 data has to be taken with a grain of salt as there were no weeklies to trade. If the crash of 2008 happened today, I think the results would be slightly better due to the fact that you could spread your volatility risk over more expirations.

KST-PutsOnly-all

I was also surprised to see that rolling made matters worse. True, this is not Karen’s strategy. She also sells calls, but I don’t know how those calls could make up those gaps. Reportedly Karen turned a profit in 2008, but I am not sure she disclosed how. I’m not sure she was following the same rules or not. She likely was following the trend and trading less puts/more calls on the way down. And probably at smaller scale.

To be continued.

Test 8: IV

This week we are going to look at the implications that implied volatility has on iron condor trades. Can the implied volatility be a good indicator on when or where to enter a trade?

To start with, lets look at the implied volatility of the ATM options for the RUT for the first three month expirations over the last 9 years:

IV-comparison

The front month is definitely more volatile as we would expect, but the 2nd and 3rd months look to track one another closely. We can see that the minimum is around 15 and the max is in the neighborhood of 70. Note that I am calculating the implied volatility of the option chain as a weighted-average of the ATM option’s IVs.

These tests will using the following IV thresholds:

0.10, 0.125, 0.15, 0.175, 0.20, 0.225, 0.25, 0.30, 0.40, 0.5, 0.6, 0.7

Note as the graph shows, we should not expect any trades with an IV under 0.10. Using this value serves as a sanity check on the results.

IVs Greater than Threshold

The first test run uses the IV as a minimum threshold to entering a trade. For examples, trades with a minimum IV of 25 would not likely enter a trade when this article was written (IV is around 16).

IV-hist

The Top row are the IVs.

The first thing to note is that trades requiring an IV above 30 are very hard to come by.  Trades over 30 occurred less than 14 times in the last 10 years. The data show above for these high vol trades are interesting but cannot be counted on due to the very low population size. But it is interesting to note that there may be an advantage for low delta trades has extremely high IVs over high delta trades.

In general I would say that there is marginal effect when using IV as the sole entry criteria until the IV starts getting north of 22.5. Between an IV of 22.5 and 30 there is a significant sample size and the IV threshold, especially at lower deltas, seems to benefit the trade.

iv-lo-expret

Expected Returns over Minimum IV

Looking at a graph of the expected returns based on using a minimum IV threshold we can see a couple patterns. First, for the extremely high IV trades (which are very rare), the low delta Front Month trades perform very well, as do all the deltas in the 3rd month. But again, this is based on a very small sample size.

Second, many traders often note that condor trades when the IV is under ‘X’ are not worthwhile. As I write this, the VIX and RVX are at extreme lows, and this comment is a frequently shared sentiment. But according to this data, it doesn’t really stand up. The low IV trades do well. But we have to wait for a few more tests to be sure their effect is due to the low IV trades and not being propped up by other high-IV trades.

IVs Less Than Threshold

Now lets look at the reverse situation  What if we place an upper threshold on the entry IV of a trade.

iv-upper-heatmap

Improvement in Profit Factor by maximum IV levels (IV in columns)

Now we can see that the very low IV trades were much harder to come by except for the Front Month. Setting a maximum IV threshold may be a detriment to high delta Front Month trades, but largely the effect is marginal. The Front Month, low IV trades (i.e under 18) appear to perform better than the baseline which provides evidence that there may be good IC trades in low volatility environments.

Most of the trades in the second month have marginal effect. But the Third month appears to have non-marginal improvements in trades with IV less than 20.

IV Between Thresholds

Now we can look at combine a lower and upper threshold and see if any patterns emerge.

Improvement in Profit Factor between Bounded IV ranges

Improvement in Profit Factor between Bounded IV ranges

If you were to only trade within a specific range, lower IV environments would actually give you a boost. There are also some good scenarios in the Front Month with IVs between 25 and 50. If you only want to trade in the very high IV ranges, you will be waiting a long time to initiate a trade, but you are also best advised to use the Back Month trades. From this table we can see the largest improvements are:

  • IV between 13 and 18: Lower Delta in any Month.
  • IV between 18 and 20: Very Low delta front month trades, or lower delta trades in third month.
  • IV between 20 and 30: There is no clear winner. Most trades look marginally impacted.
  • IV between 30 and 50: Low Delta Front Month
  • IV over 50:  Third Month trades

So… can we use this to help pick better positions.

Expected Return with bounded IV ranges.

Expected Return with bounded IV ranges.

Above is a heatmap of the expected returns of all the trades with bounded implied volatilities. Those trades with very low number of trades are circled in red. Each column is colored according the the best and worst performers for that IV range.

Note the trend in the back months: high delta trades perform worse and worse as the IV range is increased, barring extreme levels.

Today, the IVs of the RUT are 15.3, 16.6 and 17.9 as of this writing. They all fall squarely in the second column. According to this data, it may be most advantageous to enter a Front-Month trade with Deltas between 15 and 22, or a back month trade with deltas below 22.

To me, this sort of dispels the belief that there are not good returns in low volatility environments. In fact according to this data, the front month trades in the 13 to 20 delta range out performed those in 20 to 25. Quite the opposite of what I understand many to believe. BUT, these trades are all held to expiration, and that is generally not practiced…

Conclusion

Low IV environments may not be as bad as many seem to claim. In fact the Mid-IV range has historically shown to perform slightly worse. But in either case, IV can be a very useful tool in selecting which month and which strikes to enter.

Believe it or not, this test took me quite a while. I thought it was going to be trivial to implement, but I kept finding interesting nuggets to investigate (as well as a lot of refactoring to my codebase). But the effort is worth it and I can’t wait to test these concepts across a broader base of instruments.

Test Files:

Minimum IV Data Results

Maximum IV Data Results

Bounded IV Data Results

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.

Results

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:

iv-heatmap-diffpf

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?

iv-style-heatmap

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.

Conclusion

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.

Results

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:

EE-heatmap-diffPf

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.

HeapMap-EE-3-28

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.

Conclusion

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)

Files

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.

TSP-Heatmap-PF

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%

TSP-Heatmap-ExpRet

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.

Conclusion

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.

mp-heatmap-diffPf

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.

ap-avgprem

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%)

mpa-heatmap-diffPF

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.

ap-sdprem

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.

mpasd-heatmap-diffpf

Conclusion

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