STT Backtesting Notes (Part 2)

I continue with miscellaneous observations from recent STT backtesting done in OptionNet Explorer (ONE):

• I did not use PT or max losses, which would require separate tranche monitoring. I realized afterward that I had not separated tranches by trade ID. This is discouraging because as it was, I had difficulty switching between combined/separate isolation modes and [de]selecting position and/or model for adequate visualization to set up trades.
• I used a global bearish STT for adjustment when technicals turned bearish. Per previous bullet point, monitoring separate tranches is important to roll back or exit tranches by the time short/upper strikes are breached.
• My bias is to save money on debit spreads and to buy only when market conditions turn ugly even though I’ll have to pay more at that time. Although I believe the STT may be a profitable strategy, always-on insurance costs extra. Starting without the debit spreads is essentially trading an income STT. Either way, before hanging tight to this bias I really should give the standard STT a chance to see how it fares without loading the additional credit at inception.
• With or without always-on insurance, I still haven’t quantified exactly how profitable the strategy might be.
• I sometimes lost count of STT and tail-hedge tranches. The idea is to sell an STT every time a tail hedge matures. Upon realizing I had gotten behind with tail hedges, I doubled the number of NPs with the intent to sell one fewer STT upon maturation to catch up.
• The last bullet point suggests additional ONE limitations: not showing total count of long/short contracts, not being able to click on a particular expiration and see statistics for just those contracts, and not being able to click a button to jump straight to farthest-dated contract (as I am able to do with to the shortest-dated). I felt like I needed a separate spreadsheet to monitor current number of contracts and total tranches (and trade IDs) for STT and tail hedge.
• I discovered reverse calendar/diagonals taking shape from extra LPs with NPs in far-dated months after closing NTM/losing STTs. Closing NPs for max loss would avoid SOD risk. Alternatively, I could choose to pay some higher price to force tail-hedge maturity and sell a new STT at the same time.
• Tail hedges are always OTM from STTs themselves and I question their lift strength in this ratio (also see Part 1 comments on convexity). As noted in the third paragraph of Part 1, next time I will buy one month closer (perhaps planning to at least close the embedded STT PCS if necessary when associated tail hedges expire).
• What would happen if debit spreads were purchased one month closer?
• I kept an eye on PMR (down 12-14% on the underlying). I should also monitor LEL at trade inception.
• At one point, I clicked ahead to the next day and was surprised to see a blank risk graph. All positions had expired and the previous months had not given opportunity to sell new STT tranches as tail hedges did not mature. This argues for a supplemental volatility-based or episodic STT entry should market conditions turn bearish (although when the market gets crazy, being totally out isn’t a bad thing; see first bullet point in Part 1).
• The strategy is discretionary with many moving parts and I really don’t know what to track. For this reason, I didn’t keep a spreadsheet and don’t have concrete results to present. This doesn’t sit so well with me.
• Although not implemented in this backtest, I did try to model a couple standard 242s and they seemed nowhere close to delta neutral. I used 25-point strikes and I figure this must have been the result of low delta density. I can always tweak strikes as necessary to change NPD as desired.

I will be doing more backtesting, will hopefully sharpen some of the ill-defined points, and will ideally generate some results to share. Things that might help with this include: PT and max losses, guidelines for UEL and delta bias, clear technical criteria, attention paid to unique Trade IDs, and continuous tracking of inventory.

Easier said than done!

STT Backtesting Notes (Part 1)

Today I present miscellaneous notes based on observations seen doing manual STT backtesting in OptionNet Explorer (ONE).

I would like to give a disclaimer about level of sophistication. Someone recently looked at my blog and said, “I was completely lost after two sentences.” I have made particular effort in the past to explain things for an audience unfamiliar with trading concepts. I have cut back on this recently. Please realize my primary motivation for blogging is to organize my thoughts and to keep myself on track with my projects. I don’t need basic definitions because I’m immersed in this stuff every day. Should you have particular questions, always feel free to leave comments below or even contact me via website e-mail.

The backtest begins in Nov 2019 in order to be fully loaded for the March 2020 crash. This is primarily an income STT (BWB) backtested as 10 contracts (five tranches). I hedged starting with nakeds that become 40 LPs upon maturation. I sold the STT in the same month; next time, I will look to sell the STT one month farther out.

I tried to use some rudimentary technical analysis to guide whether I should lean positive or negative NPD. I looked at slope of 50-MA along with IV term structure.

If I’m going to trade this live, then I need to be very clear with technical criteria. Even if the criteria alone do not constitute a profitable strategy, they should at least filter out large contrary moves. I then need to make sure to lean directionally rather than letting NPD grow too large (especially in defensive periods where HV is high and big moves would not surprise).

I charged $20/contract to cover transaction fees. This should be sufficient as discussed here. All expiring contracts were BTC at 3:55 PM. This added expense may be considered to offset crash conditions where larger slippage would be expected.

Here are my general impressions:

• When things get crazy, look to peel back or exit because major adjustments moving around lots of contracts in market crash conditions may not be feasible. I totally forgot about this.
• I was usually short convexity although sometimes T+0 bottomed before reversing higher. I thought the idea with the tail hedge was to generally be long convexity, though.
• While I admit I did not add volatility stress to check convexity, never in 2020 did I see a T+0 smile. Neither was this realized when the underlying tanked despite tail hedges profiting handsomely.
• I did not harvest in this study, which could potentially remove risk for some added cost.

I will continue next time.

Naked Call Backtest (Part 5)

As mentioned previously, increasing position size makes this profitable: not anything about the naked call strategy itself.

Do any research on Martingale betting systems and you will see they are not recommended. I wrote about this here. The smaller I start as a percentage of the total bankroll, the lower probability I will run into a string of consecutive trades long enough to go bust. Make no mistake, though: it most definitely can happen [and since Mr. Market “can remain irrational longer than you can remain solvent,” it probably will].

What are some ways a strategy like this may be viably implemented?

One way is to position size as a fraction of the entire account. I did calculations in Part 4 based on $240K risk. If this is 10% (for example) of my total account, then I can rest easy because at absolute, never-before-seen worst, I lose 10%.

Another way to trade this responsibly might be to overlay on top of a long strategy that offsets naked call (or vertical spread) losses when the market rallies. Keep in mind that calls are NTM compared to equal-delta puts due to vertical skew, which means portfolio margin requirements can grow faster. Trading fewer call than put contracts as net short premium is one way this can make sense; just remember the number of call contracts may increase at least 16-fold.

Implementation of stop-losses is another avenue for the naked call (or vertical spread) strategy. By improving the avg win:avg loss ratio, I can mitigate position size increases. Stops increase number of losses, though, because a trade cannot recover once it has been closed. Backtesting is needed to better understand whether one factor is clearly more likely to prevail.

Finally, intraday backtesting (discussed in this blog mini-series) remains undiscovered country. Mine is a once-daily backtest,* which allows up to 24 hours for things to go from bad to worse. The worst backtesting loss is on 4/6/20 when a call sold for $1.35 is closed for $45.30. As mentioned in the second-to-last paragraph here, I think ITM short puts are best rolled before they go OTM. The mirror image dictates rolling short calls before they go ITM. Win percentage would decrease, but magnitude of loss would be lower. Again, backtesting is needed to better understand whether one factor is clearly more likely to prevail.

As discussed in the last paragraph here, by rolling rather than taking on some unknown legging risk and leaving short options to expire worthless, the current backtest errs on the side of conservatism. Rolling involves buying out remaining premium and realizing excessive slippage upon exit, which would both be expected to dampen performance slightly.

* — I used market prices at 3:50 PM until Dec 2020 and at 3:45 PM daily thereafter.
       This was due only to inadvertent oversight. The catch-22 is to use data as
       close to expiration (4:15 PM ET) while not suffering widened, distorted bid/ask
       spreads often seen after normal market close (discussed in fifth paragraph here).

Naked Call Backtest (Part 4)

Do you take it or leave it? I say the latter. With a couple changes, though, this trading strategy becomes a bit more enticing.

All of this exempts previously-addressed caveats having to do with dynamic strategy guidelines:

1. Use OptionNet Explorer (ONE) between 3/23/2020 – 7/1/2021.
2. Sell 10 naked calls in nearest weekly expiration (1 – 4 DTE) for just over $1.00/contract.
3. Assess transaction fee of $16/contract for slippage and commission.
4. Monitor market and/or trade once daily at 3:50 PM ET (3:45 PM ET starting Dec 2020).
5. If ITM, roll to following weekly expiration as far OTM as possible for credit; increase size with discretion.
6. Otherwise, rinse and repeat on expiration day with 10 new contracts.

With regard to (2), the target premium is $1.00 but I aim for ~$1.30 – $1.40 to offset transaction fees. If I have to pay material premium to close (e.g. more than $0.05) then I add to target premium in order to net target premium. For example, if I BTC for $0.75, then I will look to sell the next call for ~$2.00.

With regard to (5), I aim to increase size and leave sufficient cushion in case the market continues upward. What constitutes “sufficient” is what makes this discretionary.

Here are the results:

Shocking improvement from Part 1! This equity curve climbs consistently with eight large, brief pullbacks. The average loss is about seven times the average win. While not so great, the strategy wins 93% of the time.

The maximum drawdown (MDD) occurs here:

Albeit confusing, SPX price increases 10% from April 3 – 14, 2020. I could have rolled the initial 10 to 40 instead of 20, but I would still have lost a second time and had to roll to 80 contracts. A market pullback allows for a profitable exit.

Is this a viable strategy for live-trading? Follow the logic:

• Contract size increases a maximum of eight-fold (significant improvement from 64-fold seen in Part 1).
• Err conservatively by position sizing for a 16-fold increase (one additional consecutive loss).
• Err conservatively by utilizing a 150-point call credit spread that costs an additional dime.
• Account for spread expense by subtracting ($0.10 + $0.16*) x 192 trades from $22,504 backtesting net profit.
• Max margin $15K/contract * 16 contracts = $240K.
• Net profit ~$17,500 in 16 months (rounding up) is ~5.6% annualized return (rounding down).

Given this may supplement a long portfolio, I am somewhat encouraged especially in comparison to the monthly approach.

Unfortunately, increasing position size is what makes this strategy work—not the strategy itself. When I apply constant position size throughout and add the additional cost to spread off risk, I get a profit factor of 1.03: dismal at best.

But, but… I’ll take any bonus return since I’m trading this sufficiently small to support a 16x increase in position size!

Is it conceivable that we could have more than four consecutive big up periods maybe lasting two weeks or more?

Could I go bust as a result?

Yes. Absolutely.

I will continue next time.

* — This is a very conservative estimate; with the option being $0.10, slippage would actually be half that.