The Revenge Trade

I mentioned revenge trading in the the sixth paragraph of [Epic Fury] Part 9. Today I want to fully discuss the concept.

Google AI defines revenge trading as an impulsive, emotionally driven behavior where one attempts to quickly recover from a significant loss by entering new, often larger, trades without a clear strategy. The behavior is frequently compared to going “on tilt” in poker, where frustration replaces logic and leads to reckless decision-making.

Breaking it down further, revenge trading:

• Is motivated by anger, fear, or a desire to “beat the market” rather than objective analysis.
• Increases risk by doubling or tripling position size to recoup losses faster.
• Involves taking consecutive, “rapid-fire” trades without waiting for valid setups.
• Typically ignores predefined risk management such as stop-losses and trade plans.

Psychologically, revenge trading may occur for a few different reasons:

• Pain of loss may be felt twice as strongly as joy of equivalent gain creating urgent biological need to “fix” the situation.
• Ego leads traders to feel personally slighted by the market with a subsequent desire to prove being “right” rather than admitting a mistake.
• Gambler’s fallacy is the mistaken belief that a big win is “due” after several losses.
• The illusion of control means taking any action, even a poor one, feels better than sitting helpless with a loss.

If revenge trading becomes a continuing problem, then the trading plan should be altered to prevent it. Incorporate mandatory cooldowns by physically walking away from the screen after a loss to reset the mind. Institute daily loss limits where action is halted after reaching a specific dollar loss or number of [consecutive] losing trades.

Journaling can help by tracking emotions felt during revenge trading to identify personal triggers. In thoughts and writing, make an effort to shift focus away from daily PnL to strict trade plan adherence. As with sports, sometimes the other team is just better that day but sticking with a well-thought-out plan puts the probabilities in your favor.

I believe this was my version of revenge trading. I don’t feel slighted or angry, per se. I do feel a need to act quickly and get on with a new plan that will recoup the losses and maintain the gradually upsloping equity curve. I don’t really think so much—it just becomes my [automated] mission in the moment and I may not process all relevant details (e.g. ignoring the red flags discussed in Parts 9 and 10) before moving forward.

The current instance of revenge trading robbed me of the volatility opportunity discussed in Part 7’s fifth paragraph.

Trading Epic Fury (Part 3)

Before moving on, I want to go back to the bottom screenshot in Part 2 and discuss the first arrow (Feb 5) with regard to the second bullet point here. Is that a time to do something?

I need to explain what I mean by “strong close.” I don’t have a quantitative (objective) definition and probably wouldn’t try to come up with one without rigorous backtesting [a whole other discussion]. I’m also not looking for “perfect,” which doesn’t exist. If the market starts acting wonky and this strong close indicator doesn’t trigger, then another of the six probably will.

Having said all that, my qualitative concept of the “strong close” indicator is something like:

• Wide-ranging bar.
• Close higher than the open.
• Close at candle high as a long upside wick suggests rejection of volatility.
• Close is substantially higher than anything in the past few weeks/months.
• Not a doji.

To assess whether Feb 5 is a positive trigger, I need to zoom out on VIX daily. The following chart goes back to Oct 2025:

All four annotated candles are closing highs over the recent past. The first arrow is wide-ranging and the strongest because it closes at session high [impactfulness is directly proportional to lookback period and although I’d have to scroll chart left to determine the period, here I just want to illustrate “wide-ranging”]. The second arrow is a higher close that has an upside wick and does not exceed the open of the large down candle following the first arrow: not a great exemplar for strength. The third arrow is a doji: not impressive despite being a 6-week high. A case could be made one candle earlier because while the tombstone is not wide-ranging, this is substantially higher than the previous month due to gapping up.

The fourth arrow—Feb 5—does not seem to be a “strong close.” It’s the highest close in over two months but only marginally higher than the third arrow. It is [probably] wide-ranging but has an upside wick. The qualitative criteria are really intended to specify a case that is clear-cut, blatant, indisputable, and decisive. Year-to-date volatility has trended up with higher highs but I haven’t seen a sudden, huge volatility explosion that might signify the storm is now upon us and buckle down the hatches!

I will continue next time.

Simple SPX Put Credit Spread Strategy (Part 3)

Today I want to wrap up discussion of the simple (I hope!) put credit spread strategy whose guidelines are provided here.

Continuing the discussion of strategy tweaks, instead of entering on a down day I could just enter every first (or second, third, fourth, or last) trading day of the week. My concern is missing a trade and throwing off the once/week to better define total risk. Historically, down days occur 45-47% of the time so I’m almost guaranteed to have one down day every trading week. And if I don’t then why not just take two the following week? I can’t think of a good reason unless entering on down days does not really provide a volatility advantage (in which case why do it?). It certainly can provide a volatility advantage (farther OTM can mean larger margin of safety unless volatility continues to increase) so I might as well.

A more extreme consideration would be to enter after consecutive down days. This should most certainly provide some volatility advantage at the cost of fewer occurrences. Historically, the chance of a down day is 46% with chance of a consecutive down day a bit less. Conservatively, then, I could estimate 0.44 * 0.34 * 100% * 99 pairs = 14.8 instances every 100 trading days. Also keep in mind that down days tend to cluster during periods of high probability. I could have some intervals of 100 trading days where consecutive down days happen only 7-10 times versus others where they may happen 30 or more times. The risk is having fewer trades on during market environments almost certain to produce winners while maxing out risk during market environments more likely to produce losers.

Since it’s tough to predict these things (especially given past performance is no guarantee of future results), maybe I employ Occam’s razor and enter on trading day #1 (or #2 or #3 or #4 or last) of every week. Another potential benefit to this is spreading out entry points as opposed to having a slew of trades on consecutive days that could lead to more short contracts at the same strikes. The latter is a concern because I’d hate to have lots of contracts at one strike that will all close at stop-loss as opposed to put spreads 25-50 points apart, for example, where only one or two might be stopped out.

Besides “trade every X days and always have Y trades open” to eliminate variability in exposure from “on a down day” or “at swing lows,” etc. (most of which I just discussed above), the strategy may be tweaked in several other ways such as:

• Selling other short deltas (e.g. instead of 10, look for 16, 20, 30, etc.).
• Varying the spread width (e.g. 10, 50, 75, or 100 points).
• Taking profits at different percentage of net credit (e.g. 25%, 80%, 90%).
• Using different stop-loss (e.g. 2x net credit, 4x net credit, etc.).

I could go on deliberating but for the most part, it’s a simple strategy and rather than overthinking just proceed with one contract/week to see what happens. That’s my plan going forward and once I have some closed trades under my belt, I can analyze how it’s going or look closer at any single trade.

Simple SPX Put Credit Spread Strategy (Part 2)

Today I want to give more details about the simple (hopefully) SPX put credit spread strategy.

Much of what I said in Part 1 can be expanded but I will start with some benefits about trading SPX:

• SPX is highly liquid to minimize transaction fees (perhaps the only thing always lost when trading securities).
• SPX options are Section 1256 that means favorable tax treatment (60% long-term capital gains and 40% short-term despite holding period being far less than one year).
• Underlying index has no earnings events to be avoided.
• Cash-settlement avoids the risk of being assigned 100 shares per contract.
• European exercise means no early assignment risk.
• Plethora of expirations available (e.g. AM-settled, PM-settled, Weeklys, Monthlys, Quarterlys, etc.).

Expectancy is the long-term average profit across many trades: (% wins * mean win) – (% losses * mean loss).
Supposing average spread is sold for $1.00 with $3.00 max loss and 80% winners:

Expectancy = [0.8 * ($100 * 0.5)] – (0.2 * $200) = $0 (breakeven).

Although each spread is sold for $1.00 (100 multiplier to get $100/contract), recall profits are taken at 50% net credit = $50 and losses closed for 3x initial credit result in a net loss of $3.00 – $1.00 (credit initially received) = $2.00 ($200/contract).

Supposing the strategy produces 85% winners:

Expectancy = [0.85 * ($100 * 0.5)] – (0.15 * 200) = $12.50/trade.

Supposing the strategy produces 90% winners:

Expectancy = [0.90 * ($100 * 0.5)] – (0.10 * 200) = $25.00/trade.

With the spread being 25 points, the net risk is $2,500/contract minus initial credit. To calculate Return on Risk (ROR), divide expectancy by net risk. ROR for 85% winners is therefore [ $12.50 / ($2,500 – $100) ] * 100% = 0.52%. If average trade is 3-4 weeks then roughly one trade per month is (0.52% * 12) ~ 6.2% per year.

While I am admittedly underwhelmed by that number, the calculation does use conservative inputs. Win percentage will probably be higher and days in trade will probably be lower.

Capital usage is not understated and difficult to quantify. ROR denominator is the maximum amount of capital ever allocated to the strategy. The number of open trades will constantly vary so returns are going to be somewhat diluted by additional cash on the sidelines as dry powder.

Setting a maximum number of open trades is one way to limit capital usage. For example, no more than one trade allowed per week with no more than four trades open at any given time limits total risk to $2,400 * 4 = $9,600. A total return can now be calculated by dividing net profit by $9,600.

Here are some other ideas to consider:

• Add no new trades if SPX is below its 50-SMA as losses tend to cluster around sharp market downturns [rare].
• In addition to down days, consider opening trades when SPX is near swing lows.
• Section 1256 underlyings offering favorable tax treatment but worse liquidity and fewer expirations: XSP, NDX, and RSP.

I will continue next time with more potential strategy tweaks.

Simple SPX Put Credit Spread Strategy (Part 1)

Today I am going to discuss a relatively simple S&P 500 (SPX) put credit spread strategy.

The goal here is to enhance my routine with more discipline. I already check the market around the same time every trading day and usually execute something [may or may not be a good thing]. I track my balances and margin requirements daily. I track bond purchases and the greeks (related to this post but something seems different lately that has rendered some of these triggers less important—potentially a separate discussion altogether): theta, delta, gamma, and vega.

As discussed in the third paragraph here, after repeated episodes of catastrophic loss I want to do something different. A simple strategy will suffice until I do more research to develop that next step.

The strategy is as follows:

• When market is down at 3:50 PM ET, sell an AM-settled SPX put spread 45-60 DTE.
• Sell the farthest OTM 25-point put strike that is 10-delta or higher and buy to cover 25 points below.
• Enter GTC order to close the spread at 50% of the initial credit received after factoring in all fees.
• Stop-loss will be 3x initial credit (for a net loss of 2x initial credit or roughly 4x the average winner).

I think the biggest challenge facing the strategy is closing at stop-loss. I would not use a GTC (or OCO) closing order because quirky option quotes happen and I’d hate to get taken out when the market is proceeding with normalcy and no hint of turbulence. One approach I could use is:

1. Walk the option chain NTM by 25-point strikes to identify first closing spread for at least 2x opening credit.
2. Subtract difference between identified short strike and positional short strike.
3. Subtract (2) from current SPX price.
4. Set an “equal to or below” price alert on SPX for (3).
5. Upon receiving alert, go into the trading platform and monitor the position with live quotes.
6. Close spread with limit order should it reach 3x initial credit.

Whether to close with a market or limit order is debatable and some experimentation may be worthwhile. SPX options are generally liquid enough to use market orders with good execution. I still think a limit order leaving ample room for slippage (e.g. $0.10 – $0.25) is preferable especially because of the occasional fluke quote mentioned above. “Experimentation” means limit order close enough times to make for a valid sample size, which could take months or years. Losses don’t typically occur often with this strategy and they generally cluster around periods of heightened market volatility.

I will continue next time.

Market Measures: An AI Take

I mentioned Market Measures (MM) in the last post. Before going into details about particular episodes, I want to provide an overview of what MM is.

In this day and age, I feel the necessity to get familiar with and lean on AI. To that end, I have asked Perplexity to help us out:

     MM is a segment on tastylive that focuses on advanced trading mechanics and analysis.
     It aims to provide traders with in-depth insights into various aspects of the
     market, regardless of their experience level. The show covers a wide range of
     topics, including:
     
         1. Options probabilities and volatility
     
         2. Trading strategies for options and futures
     
         3. Analysis of market behavior and trends
     
     Recent episodes of MM have explored:
     
         1. Exit times and profit targets for zero-DTE options
     
         2. Optimal profit targets for different delta strangles and 45 DTE trades
     
         3. Intraday market behavior analysis, including volume patterns and index movements
     
     The segment uses data-driven approaches to examine trading mechanics, helping viewers
     build strategies to manage more winning trades. By delving into complex market
     dynamics, MM equips traders with the knowledge to make informed decisions and
     refine their trading techniques.

Google Generative AI gives us:

     MM by Tastytrade, hosted on platforms like Tastytrade, analyzes market volatility,
     reversal probabilities, and trading strategies, offering insights for traders to
     improve their decision-making.
     
     MM delves into various aspects of market analysis, including:
     
          1. Volatility and Reversal Probabilities: Analyzing market swings and predicting
              potential reversals.
     
          2. Trading Strategies: Exploring different approaches to trading, including options,
              futures, and more.
     
          3. Market Dynamics: Understanding the underlying forces and structures that drive
              market behavior.
     
     Key Concepts:
     
     Market Profile Analysis: A method of analyzing market data to identify value areas,
     price rejection points, and the strength of buyers and sellers.
     
     Contrarian Trading: Strategies that capitalize on market reversals after
     significant moves.
     
     Examples:
     
     “How Long Does Market Chaos Last?”: An episode that examines the duration of market
     volatility.
     
     “Since 1994: Market Reversal Data Revealed”: An episode that analyzes historical
     market data to identify patterns of reversal.
     
     Benefits for Traders:
     
     Improved Decision-Making: By understanding market dynamics and potential risks,
     traders can make more informed decisions.
     
     Enhanced Trading Strategies: The show provides insights into various trading
     strategies and techniques.
     
     Risk Management: Learning about volatility and reversal probabilities can help
     traders manage their risk effectively.

Besides never seeing anything on Market Profile Analysis, I think this to be a solid overview.

The segment generally lasts 10-20 minutes and is produced ~4 times per week. Most present a Research Team study that has taken tens of man-hours to complete. Segments are generally hosted by Tom Sosnoff and Tony Battista. Members of the Research Team occasionally make appearances to provide added technical expertise. Tom Preston also makes an occasional appearance. These folks represent a tremendous amount of floor trading, data science, and option modeling experience.

All MM episodes are archived on the tastytrade website along with slides for each segment.

Next time, I will pick up in mid-2019 with a deeper look at particular segments.

Investing in T-bills (Part 19)

I’ve covered a lot of quality detail in this mini-series involving T-bill investing, option trading, municipal bonds, and margin requirements. I will finish today tying up a few other loose ends.

Despite the title “Investing in T-bills,” some may actually consider it trading. As discussed in the fourth paragraph of Part 12, my bond-ladder structure requires a T-bill purchase every week. This may be deemed frequent for those who associate “investing” with long-term buy-and-hold (i.e. few required and/or infrequent transactions). I consider it investing in terms of maintaining an interest-generating vehicle over a longer-term period, but it does require a minimal time commitment.

Prior to the Part 8 revelation that overall option decay of synthetic long stock (SLS) substantially weakens its candidacy as a proxy for long shares, I had two other thoughts about taking advantage.

First, using only long calls instead of [together with short puts to form] SLS would limit the unlimited [to zero] downside risk. Something similar can be accomplished by purchasing roughly half the number of shares and using remaining free cash to invest in T-bills. The capital requirement of half the shares would still be greater than either long calls or SLS, but shares are not subject to the additional payment for time premium.*

Despite the weakened candidacy mentioned above, long calls carry a different risk profile than [SLS or] long stock; backtesting could be used to determine what strategy might be superior.

Second, SPX SLS may be a good proxy for any large-cap ETF or mutual fund. Edge realized by large-cap ETFs or mutual funds that beat the S&P 500 [in some years, at least] would be more than offset by the additional 5.0% return on capital saved by trading SLS instead of long shares.

The Dimensional US Large Company Portfolio (DFUSX) is one such example that does not bear out as advertised. A couple years ago, I spoke with an investment adviser (IA) who uses Dimensional Funds for clients. By retaining most benchmark components while excluding just a few, he said, Dimensional funds outperform. On 4/11/24, I looked at DFUSX holdings (as of 2/29/24) to find 504 stock holdings. The most-recent S&P 500 update I can find (Investopedia from 9/26/23) reports 500 companies have issued 503 total stocks. At 504, DFUSX has added and either not excluded or substituted thereby contradicting the IA. DFUSX also has a somewhat-perplexing 505th position: S&P 500 futures. With stock futures usually in contango, this represents a slight drag over time.

SLS no longer seems like a good proxy for DFUSX given the weakened candidacy mentioned five paragraphs above.

In summary, I would avoid SLS as a proxy for long shares to be used for non-option traders [paying an option-trading IA to manage]. While I think T-bills [or something comparable] are a necessary component to supplement an option portfolio, SLS may be a poor proxy even for option traders given the additional expenses related to taxes and transaction fees.**

* — Suggesting that even before the Part 8 discovery, I knew about the time premium expense—
       just not how much more time premium is lost by the long call than gained by the short put.
** — As mentioned in this second paragraph, I need to expand the sample to verify the ~0.5%
         SLS edge.

Investing in T-bills (Part 18)

As mentioned in the last full paragraph of Part 4, selling puts facilitates T-bill investment by raising cash balance. Short puts are proportional to overall risk, however, and such transactions are subject to specific maintenance margin requirements (MR).

[Initial MR is equal to] Maintenance MR for short puts [and] is calculated differently than that discussed for equities in Part 16. MR is the greatest of:

1. Current market value of option(s) + 20% of underlying stock – amount out of the money (OTM; otherwise zero) [1]
2. 10% of exercise value of underlying stock + premium value [2]
3. Premium value + $50/contract [3]

Example #1: sell six short puts on ABC (stock) at $80 strike price for $2.50 each with ABC at $81.25/share

    [1] ($2.50 x (6 x 100)) + [(20% / 100) x ($81.25 x (6 x 100))] – (($81.25 – $80.00) x 600) =
    $1,500 + $9,750 – $750 = $10,500

    [2] [(10% / 100) x ($80 x (6 x 100))] + ($2.50 x 600) = $4,800 + $1,500 = $6,300

    [3] ($2.50 x 600) + ($50 x 6) = $1,500 + $300 = $1,800

    $10,500 is greatest (the 20% MR [1]).

Example #2: sell six short puts on XYZ (stock) at $70 strike price for $0.75 each with XYZ at $81.25/share

    [1] ($0.75 x (6 x 100)) + [(20% / 100) x ($81.25 x (6 x 100))] – (($81.25 – $70.00) x 600) =
    $450 + $9,750 – $6,750 = $3,450

    [2] [(10% / 100) x ($70 x (6 x 100))] + ($0.75 x 600) = $4,200 + $450 = $4,650

    [3] ($0.75 x 600) + ($50 x 6) = $450 + $300 = $750

    $4,650 is greatest (the 10% MR [2]). This is just Example #1 with a lower strike price (hence lower premium) selected.

MR is proportional to number of contracts because the latter gets multiplied by every term in each calculation. In Example #2, MR is therefore $4,650 / 6 contracts = $775/contract. For 12 puts, MR would be: 12 x $775/contract = $9,300.*

A maintenance call (see fifth bullet point) will be issued when:

MR > market value of securities + free cash – debit balance [4]

• Market value of long (short) securities is positive (negative).
• Market value of OTM short options increases to zero at expiration.
• Debit balance is the loan amount when borrowing funds from the brokerage.*
• Customers typically have two business days to satisfy a maintenance call by falsifying [4] else the brokerage will close positions at its discretion (potential worst-case scenario as catastrophic loss may be locked in).

While short puts increase cash balance, hopefully it’s now clear they are subject to MR limitations regarding max quantity with direct implications for risk.

Initial MR for long options is 100% of the purchase price. Cash balance decreases upon purchase and no maintenance MR subsequently applies.

As a reminder, synthetic long stock (SLS) is a combination of long call(s) and short put(s) at the same strike [price].

That light at the tunnel is getting quite bright!

*–MR is not to be confused with margin loan (“debit balance” in [4]) that occurs when cash balance goes negative.

Investing in T-bills (Part 11)

The original plan was to finish discussing considerations about synthetic long stock (SLS) + T-bills versus long shares and then crown a winner. Due to the Part 8 revelation that advantage of the former is < 0.5% (further discounted by fees as discussed last time), I now regard other considerations to be moot. I will continue going through them for educational purposes only.

In the spirit of completeness, the <0.5% calculation should be replicated. I did this using data for the 5275 strike with the underlying around 5235. For a better sampling, I would like to look at strikes 50 – 200 points above and below the market by increments of 50. New data should be used because more than 10 days have passed and SPX no longer trades at the same level. This exercise is important to ultimately crown a winner.

I discussed liquidity of SPY options last time but also expressed assignment concerns in this third paragraph—a concern minimized by trading SPX options (see that second-to-last paragraph).

With regard to taxes,* two issues become comingled: SPY versus SPX options and holding period.

Pertaining to holding period, realize SLS includes a long call and short put that are each taxed differently. This Schwab article says capital gains** on the former will be long-term (short-term) when held for over (equal to or less than) one year. Income from the short put will be short-term (“ordinary income” tax rates, which are higher than long-term capital gains rates). The combined SLS will be taxed less for a long-term holding period (albeit implying longer-term options that face more slippage as mentioned in the fourth paragraph of Part 10).

Next, realize that SPX options (Section 1256 contracts) get special treatment regardless of holding period: a blended 60% long-term / 40% short-term capital gains tax rate. The SPX short put will benefit when profitable from the blended tax rate. A profitable SPX long call will benefit when held for less than one year (else the blended tax rate may actually be higher).

Even with SPX options taxed less than SPY options, both will be taxed more than buy-and-hold (i.e. holding period over one year) SPY shares. The latter will be taxed at long-term capital gains rates and only when sold. The blended 60% / 40% rate for SPX options is higher and will be assessed every year (mark-to-market accounting for Section 1256 contracts). Thinking about the RMD disadvantage of a traditional IRA compared to a Roth IRA (see Forbes article here) makes me realize the tax efficiency of waiting until sale to be taxed on the whole enchilada rather than being taxed continually on component parts.

As a general statement, I believe taxes make a meaningful difference based on the number of articles I read about them (e.g. on tax cost ratio, on tax-loss harvesting, on long-term capital gains rates vs. ordinary income, on qualified dividends, etc.).

While difficult to quantify (especially until one decides how SLS holding period and DTE at inception will be managed), I’m giving the tax advantage to long shares. Along with fees, this further erodes the sub-0.5% SLS edge mentioned above.

I will continue next time.

* — I hereby share my personal experience with issues of taxation. Your circumstances
      may differ and loopholes or mitigating details may exist unbeknownst to me. Please
      consult a tax advisor for the definitive word on these matters.
** — Also capital losses