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Unresolved Quandaries (Part 1)

On May 8, 2016, I met up with my friend CJ and got to talking about other trading-related activities I would like to be doing. I took notes and due to its continued relevance, I am now formalizing this as a complete blog post.

I told CJ that I had considered giving educational presentations in addition to managing money for others. I discussed what I might offer compared to other investment advisers (IA).

CJ felt that most IAs are glorified salespeople. “All they do is sell other people’s funds,” she said right up front. Her husband used to work for a hedge fund and she seemed to be more informed than most on the topic.

CJ next supported the idea that I might bring something different to the table. I come from a trading—not sales—background. This might give me an edge over most advisers who have little to no option trading experience [much less know how options actually work]. I blogged about this previously and concluded it may not actually be the case but she agreed emphatically even before I could offer any caveat. At the very least, her support affirms it to be a decent marketing claim.

I next discussed how I support self-directed investing because much advertised in the industry is not as it appears to be (discussed in my last post). Again, CJ quickly agreed. “Advisers are salespeople and they know nothing. My husband knows more than our adviser and has had to tell him from time to time to make certain changes in the account. They just care about selling their company’s funds.”

If only I could always be addressing an audience with buy-in before even saying a word!

“It’s more than just selling funds. I also think it’s a failure to offer options as part of the trading strategy.” Because long stock is speculative, I view most stocks and funds as having added risk.

CJ now fell off the bandwagon as she came face-to-face with a perceived complexity about derivatives themselves. “Teaching people about options would be very difficult. That could take a long time to understand and is probably something the average person wouldn’t have any interest in learning.”

I lost her further when I tried to explain how the average person might trade options on a casual basis. CJ described how tired she is when she comes home from her part-time job (full-time would be even worse); checking up on her investments is the last thing she would want to do.

“You could probably check the market just once per day,” I said.

“How many people are able to do anything every day? On some days I’m lucky if I can even remember to brush my teeth!”

I will continue next time.

Prerequisites for Trading as a Business

Trading as a business means generating consistent profits to cover all living expenses. Usually this is a full-time, independent pursuit. While I don’t think advanced degrees are necessary, I can think of three prerequisites for a trading start-up.

First, some degree of math proficiency is necessary for this level of money management mastery. Arithmetic is most applicable in order to calculate profit targets, stop-loss levels, performance, buy/sell points, position sizing, etc. A more advanced math background can make some derivative concepts more intuitive because option pricing models are based on differential equations. This isn’t mandatory, though; one can trade without having any knowledge of option greeks at all.

The second prerequisite for trading as a business is a strong capacity for critical thinking. No industry may be more littered with fraudulent pitches, false marketing/advertising claims, and overall chicanery than finance. I have written about this subject extensively (e.g. here, here, here, and here). In designing a business plan and sticking to it one must avoid being derailed into the quicksand. A background in statistics is a good defensive arrow to have in the quiver. Statistics provides a solid foundation from which to put questionable performance claims into reasonable perspective.

Critical thinking defends against the death knell that is being defrauded and suffering losses. Even greater than the financial losses may be the psychological damage in terms of trust, confidence, and safety: three key components to a fledgling business effort. I have spoken with a handful of people who have been violated by shady advisers. Once (if) they got out they never wanted (or couldn’t afford) to invest again (also reminiscent of catastrophic loss). I strongly suggest avoiding any black box system or advertisements of unrealistic returns. Steer clear of advisers who charge too much or smooth talkers who seem like they may have cut their teeth selling used cars. These last two sentences deserve separate blog posts of their own.

Finally, trading as a business requires money! Start-up capital is a safety cushion because like many other businesses, beginning traders often lose money (early success may actually be a curse leading to false confidence and a subsequent failure to adequately assess risk). Without this cushion, the stress of being forced to profit to stay afloat with bill payments can make long-term survival difficult. As the learning curve is scaled, magnitude of living expenses will dictate how large the trading account must be. A six-figure trading account might be necessary to cover the house, credit card, and insurance payments without taking on risk so great that Ruin is likely.

Managing Winners (Part 2)

Today I want to conclude my discussion on managing winners.

I can imagine something like the following if I were to start managing winners. Placing trades daily, I would see some days where number of open positions would decline precipitously. Over the next couple weeks I would build back toward a maximum number unless other positions had been managed in the interim. The big losers would be unaffected. Smaller losers would have a better chance at being flipped because they may at some point hit the profit target (think probability of touching). Management of winners (e.g. at 50% max potential profit) means the average win would be smaller.

Taken together, persistent big losers, higher win rate, significantly smaller average win, and equal number of trades all strikes me as a recipe for disaster.

One thing I observe in daily trading without managing winners is a mix of “in-play” and “out-of-play” positions. Recently placed trades are in-play with responsive position greeks. Older trades often carry unrealized gains approximating max potential profit. Even big market moves are unlikely to affect these older, “out-of-play” positions due to negligible, unresponsive deltas, gammas, and thetas. With the exception of MR, the total portfolio acts as if these out-of-play positions were already closed.

Reflecting on these observations makes me thankful the market hasn’t made any large, adverse moves as of late. Such moves tend to increase the number of positions in play up to 100% (managing losers—perhaps with a SL—can decrease total number). I am grateful for the trading experience that allows me to make and analyze these observations because at some point, volatility will return in a big way marking the return of stressful nights [hopefully not for very long]. My goal is to factor all this into the trading system development.

Reflecting on these observations also makes me think that carrying some out-of-play trades is part of the whole design (i.e. perpetual scaling and time diversification). I could close out-of-play positions to cut risk, though. Indeed, I strongly suggest other option traders “do the right thing” and adhere to such a guideline. In effect, closing out-of-play trades is managing winners albeit much less aggressively (e.g. 80%-90% max potential profit as opposed to 50%). The impact on performance metrics is therefore going to be different than that profiled in many of the TT studies.

I will wrap this up with an empirical question for the future. As a more conservative way to manage winners, exiting out-of-play positions may also be conceptualized as buying back-end insurance. How does this compare, I wonder, to buying the insurance up-front in the form of being net long [DOTM] contracts or buying a small number of NTM hedges?

Managing Winners (Part 1)

One direction I may or may not choose to explore at some point is that of managing winners. I have trouble wrapping my brain around this concept and today I will explain why.

Managing winners is a frequently discussed concept by the Tasty Trade (TT) folks but it certainly is not a new idea. In the past this has been described as a “yield grab” or “profit target.” Just like I might manage losers with a stop-loss order, I can manage winners based on a variety of criteria.

The TT philosophy is to manage winners, adjust (or not) losing trades if tested, and trade small. If a trade goes to max loss then I don’t get hurt because my position size is small. Some trades get down significant percentages but then recover to win, which is why they don’t advocate managing losses.

One of the problems I have with this approach is doubt over whether I can “trade small” and still make enough to pay the monthly bills. I could certainly do this with a multi-million dollar trading account. Few retail traders have this luxury, though. I see this as a big problem for anyone looking to trade full-time as a business but it probably doesn’t affect TT viewers very much if my lack of success in finding other full-time traders is any indication. “Trading small” suits part-time/hobby traders who don’t rely on trading profits to cover living expenses.

According to TT “research” (I use that term loosely for reasons discussed here), managing winners increases win rate, number of trades, and profit per day while decreasing standard deviation of returns. They generally conduct studies by comparing trades held to expiration with trades closed at a profit target. In both cases, a new trade is placed after the old trade is closed.

I have tailored my backtesting methods to avoid the pitfall of insufficient sample size by entering new trades every trading day. At some point I realized that as a perpetual scaling technique, this might be a viable approach to live trading. As long as I keep my daily position size small, the total position size may remain reasonable. This has been at the core of my NP research.

I believe it is through this looking glass that I get confused when contemplating management of winners. Managing winners allows for more frequent trade placement but I am already placing trades every day as part of my scaling effort. My number of positions is, in other words, already maxed out. Opening multiple new trades on a favorable market day that sees a number of winners managed (e.g. a big up day for NPs) would compromise my time diversification, which is not something I will pursue.

I will continue this discussion next time.

Backtesting Frustration (Part 8)

Recall that my impetus for resurrecting this “Backtesting Frustration” blog series was the realization that I cannot use quick spreadsheet manipulations and calculations to reprocess 188 backtrades with lower transaction fees (TF). Today I want to go through a sampling of chart action showing different cases of false and real bottoms.

The highlighted candle below is a false bottom from 9/18/2001:

RUT Chart 9-18-01 False Bottom (8-7-17)

The SL would be triggered in subsequent days even if it was not triggered here due to lower TF.

The highlighted candle below is a false bottom from 7/11/2002:

RUT Chart 7-11-02 False Bottom (8-7-17)

The highlighted candle below is a real bottom from 3/24/2004:

RUT Chart 3-24-04 Real Bottom (8-7-17)

Because SL was not triggered two days earlier, this was the last downside move capable of taking the trade out at a loss. Smaller TF (slippage) would allow the position to evade SL and proceed to full profit.

Here is another real bottom from 7/21/2006:

RUT Chart 7-21-06 Real Bottom (8-7-17)

This is a false bottom from 3/1/2007:

RUT Chart 3-1-07 False Bottom (8-7-17)

Because I backtest [once] daily, long wicks (as shown here) represent price extremes that may or may not force trade exit depending on what time intraday (see 5/6/2010 candle, below) they occur.

Here is a real bottom from 11/21/2008:

RUT Chart 11-21-08 Real Bottom (8-7-17)

These are real bottoms from 2/4/2010 and 2/8/2010:

RUT Chart 2-4-10 2-8-10 Real Bottoms (8-7-17)

While the market went a few points lower on 2/8/2010, being close to February expiration allowed accelerated time decay to offset the move. Were this a March position, 2/4/2010 probably would have been a false bottom.

Here is a false bottom from 5/7/2010:

RUT Chart 5-7-10 False Bottom (8-7-17)

This would have been a real bottom for a May position but with the additional month to expiration, the market had time to recover and then fall once again.

Here is a false bottom from 6/10/2011:

RUT Chart 6-10-11 False Bottom (8-7-17)

Here is a false bottom from 11/14/2012:

RUT Chart 11-14-12 False Bottom (8-7-17)

Real bottom from 8/30/2013:

RUT Chart 8-30-13 Real Bottom (8-7-17)

False bottom from 10/9/2014:

RUT Chart 10-9-14 False Bottom (8-7-17)

False bottom from 8/21/2015:

RUT Chart 8-21-15 False Bottom (8-7-17)

Here is a false bottom from 1/20/2016:

RUT Chart 1-20-16 False Bottom (8-7-17)

This is another big wick but a lot can happen with several weeks to expiration.

Finally, here is a real bottom from 6/27/2016:

RUT Chart 6-27-16 Real Bottom (8-7-17)

While spreadsheets are great at managing large volumes of data and allowing us to do computational operations quite efficiently, we also have to be cognizant of what information they do not reveal. Besides outright fraud, I believe oversights like these are a major contributor to falsely optimistic backtesting results. This is a good reason why even advanced traders are best advised to undertake system development with others capable of proofreading the work.

Backtesting Frustration (Part 7)

Today I resume my series on backtesting frustrations by talking about the frustration of “flips.”

I mentioned this in Part 1 with regard to recalculating results with different TF values. A trade that is down 25.5% with $0.26/contract may only be down 24.5% at TF $0.16/contract thereby evading the trigger of SL and ending up profitable. Simply recalculating the results, I thought, would overlook these flips (from loser to winner) altogether.

In taking a closer look at the put credit spread results I see that 188 out of 1,093 trades originally hitting the -25% SL show a loss smaller than -25% with $40/trade added.

I can now proceed in a few different ways: 1. Redo the 188 trades with TF $0.16 ($0.06)/contract; 2. Assume the SL was not hit and use either 7 DTE or Exp PnL values instead; 3. Assume these 188 trades closed for zero gain/loss.

Without doubt, the first option would be most accurate and also the most time-consuming.

I therefore started working with option #2 until I realized a major problem with the assumption. A trade that evades SL on one day may still hit SL on a subsequent day. Being so close to SL, another down day would probably trigger the unprofitable exit. With the market showing recent bearishness this seems quite feasible.

Furthermore, if the subsequent down day is big then the loss might end up being much greater than initially recorded.

Option #3 was intended to be a more conservative form of #2. In [falsely] thinking most of these trades avoiding SL would flip, it occurred to me that the market may not recover enough for full profit to be realized. To be conservative I could just call those zeros. Even a zero is much better than -25% for overall performance.

Hopefully I have made it clear that I can’t assume enough to go with option #2 or #3.

I then considered a fourth option: look at the chart. If the market is bottoming on the day the SL is hit then I can proceed per option #2 or #3 depending on where the market is at 7 DTE or Exp.

Still though, if the “Furthermore” (look up four paragraphs) happens then I may be looking at a much larger loss; just leaving it as before plus $40 would be inaccurate. This would be an argument for redoing all 188 trades. While it may not seem like lower TFs could translate to larger losses, there is a lack of granularity when testing on an EOD basis.

In stepping back and considering the wider perspective, it seems like a chance occurrence whether a flip or larger loss will occur. Unfortunately, I feel I must retest in order to have any possibility of knowing for sure.

Put Credit Spread Study 1 (Part 3)

Last time I presented initial results for the put credit spread (PCS) backtest. Rarely does a trade actually average TF of $0.26/contract, though, so today I will look at smaller values.

Calculated on net MR, here are the results for TF of $0.16/contract:

RUT PCS 30delta, 40pts width, TF 0.16, net MR raw data (7-27-17)

Following are the results for $0.06/contract TF based on net MR:

RUT PCS 30delta, 40pts width, TF 0.06, net MR raw data (7-27-17)

I generally find these numbers more encouraging than the bullish iron butterfly because the latter is not profitable with TF greater than $0.06/contract. The PCS is marginally profitable even with TF $0.26/contract. Reducing TF to $0.06 increases the average PCS trade to 3-5% profit, which is 24-40% annualized.

Unlike a butterfly, the PCS has risk in one direction only. This dramatically increases the probability of profit.

Like a butterfly, magnitude of losses are a problem with the average PCS loss being 2-4x the average win. I thought the 7-DTE exit would cut out the worst losses but it reduces profit as well. The best performing trade seems to be holding to expiration with a 50% SL although I would also seriously consider the 25% SL for risk-adjusted reasons.

Put Credit Spread Study 1 (Part 2)

Today I will start presenting results for my first put credit spread study.

The global disclaimer is to say no winner really exists in the “best performing trade” competition. What is most meaningful to me may be less meaningful to you. This is why alignment between a trade strategy and individual personality is so important. All I can do is explain my interpretation of these numbers. You will have to do the same.

Here are the results for TF = $0.26 using net MR (see last post for explanation of table contents):

RUT PCS 30delta, 40pts width, TF 0.26, net MR raw data (7-27-17)

The first thing I look at is PF followed by risk-adjusted return. Exp barely edges out Exp w/50% SL for PF and vice versa for Avg Trade/SD. I would therefore trade Exp w/50% SL because this gives me a better chance of avoiding the biggest losses. Looking at the SD data gives me pause because I really like seeing drawdowns minimized. Perhaps a better comparison to put this in proper context would be to compare against $4,000 of long shares daily (as done in the last link).

Given the choice between exiting trades at 7 DTE or Exp, the latter seems to outperform. Avg Trade, PF, and Avg Trade/SD all reflect this in the comparisons between rows 2 vs. 3, 4 vs. 5, and 6 vs. 7.

Two things are missing from this rationale, though, with the first being max loss. 7 DTE has smaller max losses than Exp each time. That makes sense with the rapid option decay of the final week. Max loss can significantly limit position size. In thinking about strategies with max loss -100% vs. -50%, I would trade the former much smaller. Do remember, though, that in trading like I backtest my position size would be relatively small simply in virtue of putting a new trade on every single day. This not only gives me a large sample size but it also dilutes drawdowns.

Especially in thinking about this “perpetual scaling” approach, the risk-adjusted return is more important to me than max loss. As mentioned, expiration outperforms 7 DTE every time.

The second missing piece from the performance comparison is trade duration. The expiration trade is seven days longer. Annualized ROI would be one way of factoring this in because the same average trade would have a lower ROI per year if held to expiration than 7 DTE. PnL per day would be even more direct.

Next time I will study the impact of TFs.

Put Credit Spread Study 1 (Part 1)

After less than two months (personal record!) I now have initial data to present for put credit spreads.

The arbitrary parameters are as follows:

     –Sell first strike < 0.30 delta (less fudge factor for OV inconsistency)
     –40-point spread width
     –Exit at 7 DTE or 1 DTE
     –Stop-loss (SL) levels -25% or -50% based on net margin
     –Transaction fee (TF): $0.26/contract

This is a daily backtest using 3:30 PM ET data from 1/2/2001 – 6/21/2017 (4,136 trades). When the OV database was incomplete I went to 3:00 PM or 4:00 PM and/or filled in with theoretical values.

Margin requirements (MR) for credit spreads may be presented as gross or net. Net MR subtracts initial credit received from spread width multiplied by 100. This makes for larger winners and losers on a percentage (ROI) basis compared to gross MR and therefore increases standard deviation. I evaluated trades based on net MR.

Remembering my previous discussion about TFs, I recalculated results for $0.16/contract and $0.06/contract. One further consideration is that some losers near SL cutoffs might become winners (e.g. decreasing TF by $0.10 equates to an improvement of 1% in ROI on gross margin). I did not include the flips in the performance calculations.

Results of the backtest will be presented in forthcoming tables with bold type reflecting the best values (most positive for winners and least negative for losers) for each performance metric (row). Average Trade is mean ROI across all trades. SD is standard deviation. PF is profit factor. SD in the penultimate row of each table is calculated across all trades. The last row (Avg Trade/SD) is a risk-adjusted return.

The performance metrics were calculated for six (columns) exit combinations. 7-DTE ROI reflects trades closed with seven days to expiration. Exp ROI includes trades closed on expiration Thursday (1 DTE). 7-DTE ROI w/25% SL tabulates the first value of MAE to exceed 25% or ROI at 7 DTE if the threshold is never breached. Exp ROI w/25% SL uses Exp ROI if that 25% threshold is never breached. 7-DTE ROI w/50% SL tabulates the first value of MAE to exceed 50% or ROI at 7 DTE if the threshold is never breached. Exp ROI w/50% SL uses Exp ROI if that 50% threshold is never breached.

I will present the tables next time.

Questioning Butterflies

I feel like I could go on with BIBF discussion for quite some time but I think it may be time to change course altogether.

In the final paragraph of my last post, I laid out a solid plan for future research directions. I now have five degrees of freedom, which are multiplicative in trading system development. This could easily take years of manual backtesting.

I find it hard to accept this significant time commitment given the disappointing first impression for butterflies when compared to naked puts (NP). Consider NPs versus the BIBF:

BIBF (Part 2) vs. Naked Put Study 2 (Part 1) results comparison (6-9-17)

These numbers somewhat confirm my NP worries about the potential for large downside loss. Max Loss / Avg Loss is 2.1x greater for the BIBF. Average win/loss metric is 2.52x greater for the BIBF. In both cases, advantage: butterflies.

But to get the BIBF looking this good, I had to significantly reduce transaction fees. I question whether I can reliably get these trades executed for so little slippage on each side. If not then up to 319 of the 4092 backtested trades [with MAE = 0] are at risk of going unfilled, which means the backtested performance is artificially high.

The BIBF performance is hardly compelling. A profit factor of 1.14 is just slightly into the profitable range. 1.58, for the NPs, is much more to my liking especially being saddled with a healthy amount of transaction fees at $26/contract. Whereas 1.14 may be optimistic, 1.58 may be pessimistic.

One other thing to notice is the much larger commission cost for butterflies over NPs. Trading NPs is dirt cheap: one or two commissions per position. Trading butterflies involves at least six commissions per position and possibly 7-8. All that and I get less profit? This is a soft poke in the eye.

If the real challenge is to limit potential for catastrophic downside risk then perhaps the better way to proceed is with put spreads or put diagonals.

Another idea is to consider a bearish butterfly as a hedge for trading NPs since the latter will be hurt by a down market whereas the former could benefit. I’d be interested to see how a bearish butterfly performs compared to this bullish one but I would be inclined to implement fixed width, which would mean two additional lengthy backtests.