The Backtester

With the arrival of college football season (September), I hope to end my blogging hiatus and announce a new category of content:  the Daily Digest.

When I left off, I was two parts into a roughly four-part (or more) analysis of the collar trade. I did do some extensive collar backtesting of 4-5 stocks and I may return to that for some analysis. The findings were definitely interesting and worthy of some consideration.

About the time I left off, my blogging time was replaced by daytrading study. In an attempt to learn more about the price action of my most oft-traded vehicle, I started watching the Russell 2000 index very closely. Suddenly, 4-5 hours every day that I had been able to devote to blogging, to paper trading, and to more active option trading was occupied by staring at streaming charts and watching the tape. I started to notice some interesting patterns and I eventually came up with some guidelines for daytrading futures.

The Daily Digest is meant to review the day’s activity on the market from a technical point of view. Every day I follow the market live and decide in real-time where my entries and exits will be. Now having done this for 58 trading days, I want to focus more on going back to previous days and looking for patterns that have repeated themselves. The Daily Digest will be a tool for me to better organize my thoughts.  Should you be interested, you are welcome to follow along too.

Here’s hoping the long road leads to Profit!

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Last week in my post “Death by Collar (Part 1),” I presented a recent internet article outlining some basic mechanics of the collar trade.  Today I will present another article.  In addition to much content taught by trading educators, these articles would have you understand the collar trade as a means to safely protect your capital from the downside while also allowing enough upside potential to ultimately make you rich. 

The following article appeared in my e-mail on December 3, 2007, as an advertisement for an options trading service.  The proprietor is a well-known “talking head” who may just as well remain anonymous for our purposes:

“One of my favorite strategies is called a ‘collar,’ and it’s pretty much what it sounds like — using your head by not sticking out your neck…

Because we’re looking to buy protection, we’re buying put options as the first half of the trade. But the second half of the trade… encompasses selling call options against those long puts. Both options should be OTM…

For example, a stock that keeps on moving upward is Apple (AAPL). To ‘collar’ a 1,000-share position in AAPL, which is trading at $181, you could:

Buy 10 AAPL Jan (2009) 180 Puts, and
Sell 10 AAPL Jan (2008) 195 Calls

Notice that I went all the way out into 2009 for the January puts at the $180 strike price. Not only does the long put position protect us from a downward move as AAPL moves up, but it does so for a year.

When we’re simply buying options, particularly calls, we’re usually looking to profit from a quick move in the near term. But when we’re buying protection, it makes sense to buy as much time as we can!

That brings me to the call options that we will sell to open. Those calls at the $195 strike represent the share price we want to ride AAPL up to.

Notice that we’ve gone with calls that have a closer expiration date. The goal with the collar strategy is to collect premium from those short calls every month or so as you’re riding this trade till the later expiration date of the puts.

If you were to sell the AAPL Jan 195 Calls today, you could collect $10.80 a share ($1,080 a contract or $10,800 a 10-lot that is “covered” by your thousand-share position in AAPL stock). But here’s the magic: If AAPL doesn’t trade up to $195 by expiration Friday in January, this is premium that stays in your pocket.

Assuming these short calls expire OTM, you keep that money and, once again, sell the calls at that strike price (or higher, if the stock keeps climbing) each month. You might just be pocketing premium again and again!

But back to those long puts — what you pay to buy those puts should come out of the profits you make on the short calls. Those puts are going for about $34 right now ($3,400 a contract or $34,000 to cover your thousand shares).

That insurance may seem expensive, but look at it this way — you spend $34,000 now to buy those puts, but it’s a one-time expenditure. If you spent $180,000 to buy 1,000 shares of AAPL today, that $34,000 looks like a reasonable ‘life insurance’ contract to keep you protected for the next year.

Once you collect that $10,800 on those AAPL Jan 195 Calls, you’ve started profiting on the collar, as you’ve brought your investment down to $23,200. Remember, as long as the stock stays above the strike price of the long puts, you will be selling calls against them, and that’s money you take in every time you initiate a new trade.

All things being equal in this example, if you took in $10,800 a month during the next 12 months, you could collect $130,000 in that time — a profit of nearly $100,000 (Ed’s note: 46.5% annual return) after you subtract the initial investment in the long puts. That number could be less or even more, depending on the options’ value!

So, to review, here is what the AAPL collar is designed to achieve:

1. The long-term put provides downside protection with slower time decay.

2. You cap off your upside potential by virtue of the short-term call sale.

3. You could generate monthly income with the sale of OTM calls. Or, you could buy back the call (between expirations) and roll it up to a higher strike price if the stock moves farther and faster than you had anticipated.

But what would happen if the stock went down? Simple. Those long AAPL Jan (2009) 180 Puts would expire worthless if the stock kept going up, but if shares pulled back to $180 or lower by January 2009 expiration, you could collect the premium that would be available as the put moved in-the-money.

That way, if you lost money on the stock, you could come out better off than you would have been otherwise if your shares dropped and you didn’t have any protection in place.

Now, I know some of you might have gotten nervous about the short calls because of the risk of assignment. But remember, you have the shares in your account, so if you were assigned to provide 1,000 shares of AAPL at the $195 strike, you already had the shares in your account, and if you bought them at market ($181, in this example), you’d be paid $195 per share to be taken out of your position — a profit of $14 per share.

And since you were planning on riding the stock up to that level anyway, the work of closing a position might just be done for you!”

I would encourage you to take some time rereading this article and then review the article in “Death by Collar (Part 1)” from earlier this week.  Compare and contrast the two:  on what points do they agree and disagree? 

Regardless of what these articles claim, I dissent.  In my next post I will start to explain why.

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Having baked the latest rendition of my Chocolate Mint Bars over the weekend, the title of today’s blog is quite apropos.  The collar trade is where education begins for many stock and option traders alike.  A quick web search will turn up many claims of programs that teach collar trade utilization to become filthy rich.  I humbly dissent.  If you do not understand its numerous possibilities then as I see it, the collar trade can destroy your account along with the best of ‘em.

I will begin my exploration into the collar trade by presenting two blog posts I have read over the past few years.  While collar trades have infinite variance in the details of management, I simply want to make sure we’re all in the same ballpark as we move forward with the consideration.

The first post comes to us courtesy of http://slopeofhope.com:

“Generally I prefer to forgo the ‘ownership experience’ and pretty much stick with an associated option. Often misaligned and frequently misused, options were (this is a recording) actually invented to control risk. But here’s how Grandma might do it:

Let’s say the old lady buys 1000 shares of PFE @ $19.47 in Jan 2010: total cost $19,470. 

After many years in the market Grandma has a few cast-in-stone rules; one is to never let a stock run more than 10% against her. Since she doesn’t like stop loss orders she’ll purchase 10 PFE Jan 2011 $17.50 puts @ $1.40: total cost $1400.  Grandma has defined her risk at 10% (give or take, roll with me on this please). 

The puts for insurance were $1400. PFE pays 3.70% dividend which comes to about $720. So if PFE goes nowhere this year (certainly possible) this trade’s still a $680 loser. To counter, our geriatric derivative wizard sells 10 out of the money (OTM) February 20 calls, about 2.7% OTM, @ .30 for a credit of $300.

Here’s how this year might pan out: 

Grandma collects $300.00 x 12 months for $3600 selling OTM calls. Minus the remaining $680 for those puts= $2950 or about 15% return on her original investment. Not bad…

-or-

Grandma’s PFE gets called away every month. She’s made the $300 call premium plus the $530 difference from her original purchase and the strike price that she “loses” her stock. And she reloads to do it all again. Here’s the math: $300 call premium + $530 stock appreciation= $830 x 12 months= $9960 or 51% on her original investment. Tasty.

-or-

Some combo of the above.

-or-

Trade is complete and utter failure. PFE falls into the abyss (we’re talking the largest health care/drug manufacturer in the US here) Grandma loses the spread between purchase price and put insurance floor, plus the price of that insurance for a total of $3390 less the call premium collected for 12 months $3600=  no loss? Yep.

Yes they’ll [sic] be commissions.

Yes these numbers are ‘today’s’ numbers and we don’t know what future premiums will be. On the other hand (without launching into the Greeks) there’s certainly a good probability premiums will become higher as the year progresses–then tasty becomes downright sweet!” 

Read that over a couple times just to make sure it’s clear as day. In my next post, I will present another delectable dish for your collar consuming pleasure before we move on to the critical analysis.

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Investors typically calculate returns on an annual basis.  This makes sense since they do not have much buy/sell activity:  positions may be held for months to years, in most cases.  Swing traders and day traders act much more frequently.  The same is often true for option traders.  In today’s post, I want to detail how trading more frequently can affect returns on an annual basis. 

Placing trades each and every month can potentially supercharge returns.  Consider a trade that profits 5%.  If the set-up occurs once per year then its annual return is 5%.  If the set-up occurs three times per year then the annual return is 15%.  If the set-up occurs once per month then the annual return is 60% (79.5% on a compounded basis).  The more frequently one is able to make this trade, the greater the returns will be.

The effect of losing trades can be understood in terms of canceling out profitable ones.  Suppose a trade either profits 5% or loses 5%.  If the trade wins every month then the return is as shown above.  If the trade profits 11 times and loses once, however, then the result is a net gain of 10 profitable trades per year, or 50%.  If the trade profits five times and loses seven then the result is a net loss of two trades per year, or -10%. 

Adjustment techniques often decrease the number of losing trades at the cost of diluting the returns of the winners.  Consider “Trade A,” which is defined to profit 5% eight times per year and lose 5% four times per year.  This is a 20% annual return.  Adjustment that doubles capital allocation to the trade, in effect, halves the return of winning trades (see my post “Sizing Risk” from 4/19/10).  What if this increases Trade A to 10 wins and two losses?  The new annual return would be [(10 * 2.5%) - (2 * 5%)] = 15%.   If 11 wins could be achieved then the annual return would be 22.5%–a slight improvement over the original. 

Aside from reducing the number of losses, overall return can be improved by mitigating the size of the losses.  I reviewed this math in my 4/12/10 post “Profit Factor.”  If the magnitude of losses can be cut in half then the annual return of Trade A becomes [(8 * 5%) - (4 * 2.5%)] = 30%–an improvement over the original. 

The savvy trader would hope to get more for her adjustment by not only decreasing the number of losses but also by mitigating the size of those losses.  If doubling capital in Trade A halves both the number of losses and the size of those losses then annual return remains at 20%.  If 11 wins could be achieved then the annual return would be 25%–an improvement over the original.

A final possibility is to decrease the number of losses by rolling the trade out to the next month.  This is more of a “refuse to lose” approach.  The cost would seem to be fewer trades per year.  The supercharged returns described above in the second paragraph would be lower because the average trade would take longer.  This may be worthwhile if losses can be avoided.  What if rolling out to the next month makes two winners out of every four losers in Trade A?  To be realistic, let’s also assume a doubling of capital on the roll.  Winners therefore profit 2.5%.  Losers lose 5%.  Annual return:  11.25%–worse than Trade A.  What if rolling out makes three winners out of every four losers?  Annual return:  16.88%–still worse than Trade A.

Based on this simplistic P/L model, increasing capital to the trade really acts like a dagger on returns.  The latter calculation amounts to 11 wins out of every 12 trades generating a lower annual return than eight wins out of every 12 where capital remains constant.  The equity curve will be smoother in the former case, which may very well speak to psychological stability.  Overall returns will, however, be lower.  If rolling out were able to do even better–like winning on all trades–then we’re still looking at max potential 22.5% return, which is only marginally better than Trade A.

I would encourage new and/or experienced traders (if they have never done so) to play around with return scenarios based on number of wins, average win/loss, and length of trade.  Be absolutely sure that money management confirms your trading plan to be a successful one with any potential adjustments increasing and not slashing those potential profits.

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In my post “Real Reasonable Returns (Part 1)” from 3/24/10, I discussed the inaccuracy of calculating iron condor trading returns.  Today I am going to extend this analysis to the general trading case where capital is added in a rescue effort to generate a profit or minimize a loss.  Sizing risk pertains not only to the potential for larger losses with more capital employed but also to the potential for smaller gains with less capital employed. 

My discovery of Sizing Risk began with a debate about calendar trading.  Consider a position started as a single calendar using 33% of the total capital allocation.  If the market moves outside a breakeven then another calendar is added.  This may be done once more to arrive at 100% capital utilization and a wider profit tent than the original calendar.  How does this scaling technique compare to buying the entire triple calendar at trade inception?

Many traders will favor the scaling approach because it seems to prevent unnecessary deployment of capital.  Waiting for the market to “show its hand” by moving up or down allows subsequent calendars to be placed where they have a greater chance to profit.  The upside (downside) calendar of a triple, for example, will do nothing but lose money if the market goes on to tank (soar). 

Other traders do not favor the scaling approach because it means remaining in the trade longer and closer to expiration.  This is a result of the reduced positive theta generated by fewer spread contracts earlier in the trade.  Theta will grow as more contracts are added.  The triple calendar, with all contracts placed at once, will be collecting more theta from trade inception.

Sizing Risk describes a glaring problem I found with scaling that seemed to receive very little mention.  Consider a trading plan with a 15% profit target and 20% max loss. In months where the market trades sideways, the single calendar will hit its profit target with only one-third total capital utilized. In other months where scaling into the full triple calendar is required, all capital will be deployed.  When the 20% max loss is hit, it will be 20% of the full capital deployment.  When the profit target is hit, it may be on 33%, 67%, or 100% of total capital allocation depending on whether any scaling was necessary.  In effect, then, this trading plan has a max loss of 20% with a profit target of 13.3% (the average of 33% capital allocation * 20%, 67% capital allocation * 20%, and 100% capital allocation * 20%). 

Remembering back to last week’s blog on profit factor, scaling now appears to be a challenged trading strategy because it loses more in bad months than it profits in good months.  If the position allocation is $10,000 per calendar up to a maximum of three calendars, then it will profit $1,500, $3,000, or $4,500 depending on how many calendars were traded. When the trade loses, it will usually be after completely scaling in: 20% of $30,000 is $6,000 lost. If this trade wins eight months out of 12 and averages two tranches (i.e. a double calendar) in a winning month, then in one year it will make 8 months * $3,000/month = $24,000 and lose $6,000 * 4 months: it will break even. If this trade does any worse than winning twice as much as it loses then the annual return will be -30% at best (seven wins and five losses). Should it have a tough year and lose exactly as often as it wins, the annual return will be -60%, which is nothing less than a good recipe for grounding an account into hamburger meat.

Sizing Risk teaches us that excessive losses can be just as counterproductive to successful trading as suboptimal gains. As discussed last week with the naked put selling strategy, the common worry amongst traders is to have a catastrophic loss that wipes out many profitable months. Assuming that every trade will have its fair share of wins and losses then from a mathematical standpoint, making too little in the winning months can be just as harmful to overall returns but much more frequently overlooked.

In the final analysis, I cannot say what approach to calendar trading is right or wrong. While backtesting may be able to weigh in on this matter, one thing is clear: no matter how the scaling calendar trade is managed, it must prevent average losses from overwhelming average gains.

In coming weeks, I will tie together these last two blog posts by addressing one further pitfall that I have yet to hear anybody discuss with regard to scaling/adjustment trades. Be prepared to open a whole other can of worms…

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In viewing several trading educators at http://www.cboe.com as well as programs associated with various brokerages, I have found Dan Sheridan to be one of the best. ”You must maintain sanity between average wins and average losses,” he often says. Sheridan is describing the profit factor: a parameter of trading systems that offers important insight to profitability and long-term success.

Profit factor (PF) is defined as gross profits divided by gross losses. In other words, PF is the total profits of winning trades divided by the total losses of losing trades. In my backtesting, I use net profits and net losses since I always include transaction fees, which is much more realistic. To go one step further:

           average net profit on winning trades      # winning trades
PF =  ——————————————————-  *   ————————–    (1.1)
               average net loss on losing trades             # losing trades

“Average” is typically the arithmetic mean.

Although very similar, I would point out that PF is not exactly the same as a trading system’s expectancy (E). Van K. Tharp is a prolific writer on the subject of trading and money management (see Blogroll). Tharp discusses E and position sizing as two of the most important ingredients to successful trading. E is defined as:

Expectancy (E)  = (Probability of Win * Average Profit) – (Probability of Loss * Average Loss)

Again, “average” is typically the arithmetic mean. E may be interpreted as the average dollars of profit/loss on a trade per dollar risked. PF may be interpreted as the number of dollars made per dollar lost. E should be positive for a successful trading system while PF should be greater than one.

As Dan Sheridan implores, a basic tenet of running a successful income trading business is relative equivalence between average losses and average gains. In other words, the first term of (1.1) should be close to 1. Consider the extreme case of naked put selling as an example. Suppose the strategy is to sell 20 naked puts per month for $2.00 each to collect $4,000. These puts are so far out of the money that only a very rare event will result in a loss. When that rare event happens, though–call it a Black Swan if you like–then those $2.00 puts may explode in price from $2.00 to $200 (e.g. October 2008). This dependable income stream that has been generating $4,000 per month has now lost $396,000. Not only did this one month just wipe out profits from the last 99 (assuming the business had been operating for that long), the psychological devastation to the trader will likely result in a swift career change. The first term of (1.1) in this example is 0.01: only a couple orders of magnitude less than desired.

The PF calculation gives us the minimal requirements by which to profit through income trading. If a strategy’s average losses are three times its average gains then the second term of (1.1) must equal at least 3 for PF to exceed 1.0.  That is, three trades must win per every trade lost: a win rate of 75%. Similarly, if the strategy loses twice as much as it usually gains then it must win 67% of the time to break even. For many trading plans, a common ratio employed is a 15-20% profit target with a max loss of 20-25%.

I will build on this concept of PF in my next post with the introduction of Sizing Risk.

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Where have we been?

From “Real Reasonable Returns (Part 2)” on 3/24/10: 

“This blog has now touched upon a number of important criteria to evaluate the validity of backtested results.  I have gone on at length about the importance of accounting for transaction fees–both commissions and, most importantly, slippage (see the four-part series ‘The Brutal Reality of Transaction Fees,’ which began on 1/13/10).  If the backtest does not account for transaction fees then move on: you can’t believe anything it says, period.  Study the variability of returns across all trades; the larger the potential drawdown, the smaller you must place this trade to limit loss on your overall account. The smaller the trade the more diluted the returns. Make sure all returns are calculated based on the maximal margin used in any trade rather than each trade individually.  Make sure, too, that the backtest is comprehensive enough to encompass a variety of market conditions.  Backtesting just one year may be the ‘well-chosen example’:  one year of success out of multiple years lost.”

Where are we going?

My plan is to do more in the way of posting and discussing backtested returns in an attempt to discover what kind of overall return we can realistically aim for and the most plausible means to achieve it–if such a judgment can be made.  I have already blacklisted one trade, the Skinny Butterfly, for various reasons (see “Oh that Silly, Skinny Butterfly” from 2/27/10)–the most important being that it loses money in multiyear backtesting.

Where are we?

The more time I spend studying trading plans, watching webinars, listening to trading calls, and analyzing return calculations, the more I get the sense that things are not what they appear to be.  This all makes sense from a psychological perspective beginning with brainwashing of the Wall Street esteem.   I will blog on this and still provide a couple more perspectives on already-discussed themes before moving forward. 

The bottom line is that despite my selection of theme (the knife graphic seen in the upper left of your screen), the subject matter of this blog is more like trying to tease apart the layers of an onion than it is trying to slice a banana with a knife.  The latter is black and white, binary, and straightforward.  The former is gray, nebulous, and complex–even for someone with 10 years of college, two degrees including a doctorate, and formal training in critical analysis.  My hope is that by covering these concepts from different perspectives and with different words, a greater, more comprehensive understanding will be discovered that can benefit both you and me.

Filed under: Backtesting Philosophy, Critical Analysis, Options Trading

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In my daily travels to different corners of the trading world, I often hear people working to figure out how they could have made losing trades profitable.  The most important tenet of risk management, however, screams “GET OUT!” to avoid catastrophic collapse of capital.  Ultimately, the best way to trade may come down to whatever best fits your personality.

One of the first laws of trading is to always have a max loss.  This concept has many supporters.  William O’Neil dictates never to let a stock fall more than 7-8% from its buy point.  Dan Fitzpatrick recommends always having a stop in place to define your risk.  Dan Sheridan suggests always heeding max loss to maintain consistency between average losses and average gains thereby ensuring your return to the trading desk next month.  ShadowTrader discusses the psychology of taking losses and the need to feel comfortable accepting this inextricable aspect of the game. 

In reading between the lines of some seemingly veteran traders, I sometimes hear a different modus operandi:  refuse to lose.  These are veteran traders who practice trade adjustment, give lip service to Risk Management 101, and talk max loss in educational forums to beginning traders.  In observing closer, however, rather than cutting trades at reasonable loss points I detect a “fight to the death” attitude. With options you can adjust by rolling up, rolling down, and rolling out:  the trade can conceivably last indefinitely without a max loss.  I would question a “refuse to lose” (RTL) approach that strings a trade out for six months to realize a 5% (for example) profit as opposed to realizing a 20% loss in one month to enable cumulative outperformance after six. What if RTL profits on over 95% of all trades, though?  Perhaps there is something to this. 

I sometimes wonder if RTL could be the product of a sophisticated Wall Street deception.  Despite the academic lessons about risk management, is RTL the hidden secret to success in the markets?  I certainly would not put it past any element of Wall Street to tell retail traders one thing (e.g. always respect max loss) while the “smart money” does just the opposite.  As a partial compromise, perhaps RTL is a cyclic phenomenon–a means to successful trading provided the Black Swans are in hibernation (or whatever Black Swans do).  Nassim Taleb would then consider RTL a synonym for “good luck.”

At my present level of trading experience, trading without max loss would be like walking a tightrope without a safety net.  I live in a world of monthly trades.  My worry, and the main reason I’m skeptical about RTL, is that the potential losses approach the net margin requirement of the trade as expiration approaches.  “Max losses” are typically 10-20% of the net margin requirement; I do not want to lose much more if my average winning trades only make 5-15%.  Fighting the trade means remaining in the trade closer to expiration.  If a large move then occurs the week before or in expiration week itself then catastrophic 50-100% losses could result.  Sure, I could limit the impact of such loss by trading in microscopic size.  This would also significantly dilute overall income potential, though, and make paying the bills with a full-time trading business very difficult.

The entire RTL philosophy may actually boil down to trader ego.  Profiting on most every trade or presenting a backtest that wins most every month is glorious.  Even a hefty drawdown will appear much more impressive if the trade stages a comeback to end up victorious!  Newsletters, educational programs, or professional traders who display such activity will be admired and followed.  Newbie traders will consider them heroes and role models.  Their popularity will spread as people tell their trading partners and associates.  Such approaches will be lauded amongst retail traders as “the greatest thing since sliced bread.”  The RTLer will reap continuous ego boosting from the popularity, the attention, the admiration, and the money if premium services are involved.  Perhaps the RTLer will make a business of it, in fact.  Perhaps the RTLer is just another scammer who has never actually placed a trade.  Do we really know? 

For the sake of survivorship, I would hope all beginning traders make regular (over)use of risk management protocols including max loss.  Ultimately though, the choice to battle onward and upward to the death may be a trading style that better suits one’s personality.  Refuse to Lose–indeed!

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What kind of real reasonable returns can you expect from your trading business? Last week, I analyzed the claims of 8-12% monthly returns with iron condor trading, which is advertised by many websites and advisory services.  A more real reasonable return is closer to 2-4% per month.  Like the iron condor, the Skinny Butterfly (see blogs from 2/27/10 and 3/8/10) is another trade susceptible to deceptive performance claims.

Below are 2009 backtesting results of the Skinny Butterfly.  Slippage and commissions are accounted for by a $6.00/contract charge.  “Margin requirement” and “days in trade” are both abbreviated:

This trade could be advertised with some eye-popping claims.  An outright huckster might start with the maximum margin requirement ever used to initiate the trade: $7,830.  Since the total profit for the year was $24,400, this trade made 311% in 2009, which is over 25% (arithmetic mean) per month.  As discussed last week in Part 1, however, to carry out this trade one must have money in the account available to meet any additional capital requirement.  To be more truthful, then, it could be stated with an average total margin requirement of $19,205, this trade returned 127% in 2009, which is over 10.6% per month. 

Even these statements, however, scream “but you won’t pay the full $400… act NOW and you can pay in four easy installments of only $99.99 each!”  It makes no sense to base the return on the capital used in any one trade because in case the capital requirement were even higher then you absolutely need to have that money in reserve to carry out this trade.  It could therefore be stated with a maximal total requirement seen in any trade to be $29,970, this trade returned 81.4% in 2009, which is over 6.7% per month. 

In addition to annual returns, one must be cautious of misleading statements when interpreting monthly returns. “This trade only lost in two out of 12 months.”  That is true, but does that mean it won in 10?  Not exactly.  This trade won in nine months and broke even in the 12th.  What about, “in five out of 12 months, this trade made over 20%”?  This is true based on the capital used for each trade.  However, since you always must have in reserve as much capital as you will ever need for any trade, this statement is not true:  all returns must be calculated based on $29,970 margin.  In fact, the trade never profited over 20% in any month. 

Finally, be sure to study the total number of trades when evaluating backtested results.  2009 was a much different environment than 2008, which was much different from 2007.  2007 was much different from 2004, and 2006 was much different from 2005.  If you study only the 2009 results then the Skinny Butterfly would appear to be a miraculous trade!  In fact, the trade grossed more than double the profit in 2009 than it did in any of the previous six years.  Only three of six years were winners.  Summed across all six years, the trade was a net loser.  You don’t get this impression if you study only the table above, do you?  In six years, the maximal margin ever required for any trade was $37,630.  Based on this number, the trade made 64.8% in 2009–a good number, to be sure, but over 20% less the “full disclosure” figure of 81.4% given two paragraphs above.

This blog has now touched upon a number of important criteria to evaluate the validity of backtested results.  I have gone on at length about the importance of accounting for transaction fees–both commissions and, most importantly, slippage (see the four-part series “The Brutal Reality of Transaction Fees,” which began on 1/13/10).  If the backtest does not account for transaction fees then move on: you can’t believe anything it says, period.  Study the variability of returns across all trades; the larger the potential drawdown, the smaller you must place this trade to limit loss on your overall account. The smaller the trade the more diluted the returns. Make sure all returns are calculated based on the maximal margin used in any trade rather than each trade individually.  Make sure, too, that the backtest is comprehensive enough to encompass a variety of market conditions.  Backtesting just one year may be the “well-chosen example”:  one year of success out of multiple years lost. 

Critical analysis of these criteria alone will probably eliminate at least 90% of all potential avenues for fraud that money-hungry business people are smart enough to use against you.  The goal here is to formulate a reasonable expectation of returns to prevent you from being disappointed later, at best, or flat-out broke, at worst.

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Www.10percentpermonth.com, www.cashflowavenue.com, www.trademyoptions.com–they’re all the same, and hundreds more like them probably exist. These advisory services promise to deliver iron condor trades that earn 10% per month and will inevitably make you rich. I got news for you, folks: it’s all a bunch of crap based on fuzzy math.

While any of the above might do, let’s pick on www.optiongenius.com as today’s example. The website claims: “Learn How To Make A Safe and Consistent 8-12% Monthly Return On Your Money. Our Results: Up +47% in 2009, Up +102% in 2008, and Up +141% in 2007!” As part of a “free course on options selling,” this service provides the following sample iron condor trade. On October 20, 2008, sell one Nov 670/660/380/370 RUT iron condor for $0.95. Max return, it states, is 10.5%.

Eight days into the trade, the market falls to force an adjustment. On 10/28/08, buy to close the Nov 380/370 spread for $2.10 and sell two Nov 310/290 spreads for $1.35 each. Since calls were nearly worthless at this point, take them off to remove risk by closing the call spread for $0.08.  Cash flow to date is $95 (initial credit) – $210 + (2 * $270) – $8 = $147.

On November 5, 2008, close the trade at a profit after 17 days.  Buy to close the put spreads for $0.10 each. The course material reads, “we were in the trade from October 20 until November 5… We made a profit of $127.  That is more than we originally were going to make…  Our adjustment not only saved us from losing money but made us more than we expected.”

A glance at the “Past Performance” page on the website reveals a November 2008 return of 13.43%, which does not reflect this trade. Profit on this trade was $127.  Capital requirements increased fourfold during the trade as contract size and [put] spread width both doubled.  On the original capital requirements, the trade returned $127 / ($1000 – $127) = 14.5%, which is close to the 13.43% listed.   The true return on this trade, however, is 3.3%, however, because of the capital expansion.*

While this one month seems off, the big picture implications are worse.  To do this trade, you must have at least three times your initial capital requirement sitting aside as cash in the account.  Iron condors often win without needing any adjustments.  These non-adjustment trades will profit on only 25% of your total capital, which represents substantial dilution of returns.  Rather than “8-12% per month,” these are “2.5% per month” returns. 

A return of 8-12% may never be possible in any single month with the strategy illustrated above.  To return over 8% per month would require total cash flow of at least $316.  The initial capital in the above trade, which is basically a plain vanilla iron condor, was $95. This represents a potential return of only 2.4%.  In some months the profitable spreads may be rolled to increase cash flow.  Even doubling the cash flow would only be a 4.8% return.  How do you figure that in 2007, the website claims 10 out of 12 months returned well in excess of 10%, including four months of 15%-20%? No trade in the same genre as that illustrated above could generate returns like this without enormous risk and tremendous luck. 

While the winning months fall woefully short of 8-12% per month, realize that some months lose, too. I can tell you from my own backtesting that rolling spreads is one way to generate a loser. The handful of months this does not generate more profit will include the occasional whipsaw month where the roll turns a would-be winner into a [severe] loser. Other losses will occur in months where the market trends strongly. The Nov 2008 example shown above was a winner, but when such adjustments do not save the trade the average loss will likely be at least twice as much as the average win with decent risk management.

Bottom line: grandiose claims by advisory services and newsletters like those shown above (often in large font, ALL CAPS, and colorful print) are nothing short of faulty math. This is downright dirty and ludicrous absurdity. 

As I have written before in this blog, the world of Snake Oil salesmen had nothing on many components of today’s Wall Street enterprise including many companies that sell newsletters, sell advisory services, and in some cases sell “educational programs” as well.  In my next post, I will take this frame of reference and apply it to the recently discussed Skinny Butterfly trade.

*–Sites often mention adding a “bonus trade” here or there, but the written content indicates the services to be largely dominated by iron condor trading.

Filed under: Critical Analysis

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