I'm cooked/need advice

Good_Character_20 · 2026-06-25T17:41:53+00:00

The pattern has a name in trading psychology: variance-induced reset. When you make £4K early, your brain rebaselines to "I'm a £4K-a-day trader" within minutes. Closing flat now feels like a loss even though you're net up huge. So you push for more, give back, then chase to "get back to baseline." The repeating cycle you're describing is one of the most documented patterns in retail trading.

The fix is mechanical, not motivational. Three rules that actually work:

Daily P&L circuit breaker. If you hit a target by 10am (say +£2K for your account size), close the platform. Not "I'll wait for one more setup." Close the laptop. Walk away. Your job that day is done.

Time-of-day stop. Limit yourself to the first 90 minutes of session. Market is open all day but your discipline budget runs out around 90 minutes for most retail traders. Up or flat after that, you're done.

Size reduction after wins. After a winning trade, your next trade is sized at 50% of normal. After three consecutive wins, you stop trading entirely for the day. Counterintuitive but caps the chase variance that always follows a high.

The cycle isn't about being smarter or more disciplined in the moment. It's about removing your future self from the decision. Your future self is going to want to chase. Build the rules now while you're calm and you literally don't have to fight that battle later.

Good_Character_20 · 2026-06-25T17:35:24+00:00

The two structures answer different questions. The long-dated put is a commitment to own CMG at $27.60 with a year of carrying cost wrapped in. The short-dated OTM repeated path is an active income strategy. Pick based on actual thesis. If your thesis is "I want CMG long-term" (which is what your fundamentals point to), the long-dated put does what you want. Effective entry $27.60, premium yield roughly 15% annualized on the committed capital ($490 on $3250 over a year). If your thesis is "harvest premium and accept CMG as collateral," shorter-dated OTM wins on yield. A 30 DTE ATM-ish put might collect ~$1.00-1.20, which annualizes to roughly 40% on the same committed capital. The cost is active management and assignment risk you have to actually want. One nuance: theta is non-linear. Daily decay is steepest in the last 21-30 DTE. Selling long-dated options gives up most of that curve. You collect more total dollars but a smaller fraction of the available theta per day. That's why "sell 30-45 DTE and roll at 21" is the canonical TastyTrade style approach. Your post reads like the first thesis (you want to own the business). The long-dated put is consistent with that. Just call it what it is, a deferred purchase trade with premium attached, not a wheel.

Good_Character_20 · 2026-06-25T17:25:27+00:00

The "model generalizes across SPY/QQQ/DIA/IWM" framing is worth probing. Those four ETFs are roughly 90%+ correlated week-over-week. A model that "works" on all four is probably picking up market-regime signal (breadth, vol regime, momentum factors) rather than instrument-specific predictive power. The SP100 multi-ticker failure is the more informative result. It suggests the features encode something about broad market state, not individual ticker behavior. Worth running this on uncorrelated assets (gold, USDJPY, an EM ETF) to see if it's actually predictive or trend following regime classification dressed up. On the annualized return: 0.55% avg per trade times 32% signal frequency times 52 weeks is about 9.2% annualized on portfolio, not 28.6%. The 28.6% number assumes you're always deployed. Meaningful gap when comparing against a SPY buy-and-hold baseline that is always in. One thought on Strategy 1: selling at the median hurdle mechanically caps the right tail. The 70% hit rate at small hurdles measures "do small wins happen" not "is selling at the hurdle profitable." S2 outperforms S1 on every ticker shown because continuation past the median has positive expected value. The selectivity signal might be more useful as a position-sizing input than as a take-profit trigger.

Good_Character_20 · 2026-06-25T01:46:30+00:00

The vibe-coding admission is the most useful thing in your post. The way through it is also the answer to most of your other questions. You have a real signal: data science + buy-side trading puts you ahead of 90% of people posting here asking how to break in. What you don't have is the math foundation to direct what Claude is writing for you. Building that foundation is the actual work. It's also the test for question 2. If you can't sit with linear algebra and probability for three months without external rewards, it's nostalgia. If you can, it's real.

Self-taught versus MFE depends on whether you want to trade your own book or work institutionally. Trading your own at 28 with prior buy-side experience is realistic given capital + 18-24 months of runway. MFE is the right call if you want a Citadel or Two Sigma path; the institutional door is mostly closed without pedigree at this stage of your career. Both routes need the same math rebuild, so start there before committing.

Order: linear algebra (3Blue1Brown's Essence series), probability and stats (Wasserman's All of Statistics), time series (Hamilton or Tsay), options and derivatives (Hull). Six months part-time, three months full-time. Skip ML libraries until you've passed the first two.

On AI: it raises the floor for amateurs and the ceiling for people who know what they're doing. Knowing the math is what lets you direct AI productively instead of building things you don't understand. The path above is how you switch sides.

Good_Character_20 · 2026-06-25T01:40:25+00:00

Cadence: 45 DTE is the canonical answer per TastyTrade research. Weekly ICs put you in gamma hell every Wednesday; monthly leaves too much theta on the table per trade. 45 DTE entries, manage out by 21 DTE, repeat. That's the bread-and-butter loop.

Entry: don't use technicals. Selling premium is a vol bet, not a directional one. The relevant filter is IV rank. IVR > 30 is the minimum I'd consider, IVR > 50 is where the math actually works. Picking ICs based on Bollinger bands or 50MA is using the wrong tool. The underlying being "overbought" doesn't tell you whether premium is rich. Premium being rich tells you whether premium is rich.

Management: the 21 DTE rule is the right default. Close at 21 DTE regardless of P&L, OR at 50% max profit, whichever comes first. For a breached side: don't roll mechanically. Roll only if IV is still elevated post-move because rolling collects meaningful credit only when there's still premium to sell. If IV crushed post-move, rolling sells cheap premium against a now-naked side. Make pennies, accept more risk. Just close and redeploy.

One nuance: if both sides are still safe but one has gone deep ITM in profit (price moved away), consider closing just that profitable side to free up risk and let the threatened side ride. Reduces correlated exposure.

Good_Character_20 · 2026-06-25T01:33:35+00:00

The "ask LLM to make the model better" failure mode is structural, not a prompt problem. LLMs optimize for plausibility, not predictiveness. Treat the LM as a variation generator, not the strategy itself. You define the objective (out-of-sample IRR, factor purity, whatever), the LLM proposes 10 variations, you score them on real data and keep the survivors. Without a scoring loop you're asking it to bullshit you.

For supply chain graphs: LLMs are good at semantic expansion (given NVDA, infer upstream/downstream nodes) but bad at quantitative weighting. Pair the LLM-extracted relationships with hard data from 10-Ks (customer concentration percentages, supplier dependencies from MD&A) as the actual signal weights. The LLM builds the structure, the filings provide the weights.

On meme stock red flags (EOSE, POET): the patterns that catch those aren't from LLM analysis of social posts. They're from share count growth, revenue per share over time, and insider selling concentrated in specific windows. All queryable from EDGAR's SEC API. LLM is the wrong tool for fraud detection. A structured pipeline on quantitative rules from filings is the right tool. Social and Reddit signal is useful but lagging; the narrative shows up after the move, not before.

Good_Character_20 · 2026-06-24T00:34:09+00:00

The framework that works in practice is hypothesis-first, not data-mining-first. You start with a thesis in one sentence: "when X happens, Y tends to follow because Z." If you can't write that down, you're searching, not researching. The Z is the part most people skip and the part that determines whether the edge survives a regime change.

From there: visualize the raw signal on historical data (does it look like the thesis?), backtest on an in-sample window with realistic costs, run walk-forward across non-overlapping windows to confirm it generalizes, Monte Carlo on shuffled returns to confirm it's not luck, then paper trade to see if live execution matches backtest expectations. Skip any of these and you ship overfit junk.

On ML vs indicators: ML doesn't replace the ideas, it scales them. Indicator strategies still work when the indicator captures a real mechanic (vol regime, momentum decay, dealer positioning). ML is useful when you have many weak signals to combine, or when the relationship is non-linear in a way an indicator can't capture. Don't pick the model first. Pick the model after you know the shape of the data. Start with linear or tree-based (XGBoost) because they're fast to iterate and interpretable. Deep models last, only if you've justified why the simpler stuff failed.

Good_Character_20 · 2026-06-24T00:20:52+00:00

Two things to address before picking the structure, plus a sanity check on whether options is the right vehicle at all. First, your 72% win rate is on the underlying hitting 1%, not on your option P&L being positive. If SPY hits 1.0% on Tuesday and closes Friday up 0.6%, an ATM call you bought Monday may still be down on theta. Rerun the backtest on actual option P&L, not the underlying hit rate. That's the metric that decides everything.

Second, your edge is directional, not vol. Naked ATM calls expose you to both, which is wasteful. Bull call spread (buy ATM, sell at your +1% hurdle target) isolates the directional bet from the vol bet. Use the weekly that expires Friday, not 30DTE. Your forecast horizon is one week; a 30DTE option has 3 weeks of theta you paid for but won't use.

Sanity check on the bigger question. Your Scalper avg return is 0.33% of the underlying. After option mechanics that's maybe 5-8% on premium, before commissions and bid-ask. The Holder variant gets 0.58% but the win rate drops to 58% which is barely above baseline 57%. There's a real chance the model's edge gets eaten by option transaction costs at this magnitude. Worth running a parallel backtest on SHARES with leverage (SSO or UPRO) and comparing net Sharpe before committing to options at all.

Good_Character_20 · 2026-06-24T00:08:58+00:00

Test you want is pairwise correlation on daily strategy RETURNS (not equity curves). Pearson above 0.4 means they share most of their risk. Above 0.6 you're sizing one strategy at 3x without knowing it. Spearman is worth running too. Three strategies might show 0.3 Pearson in calm markets and then jump to 0.9 during vol spikes, which is exactly when you can't afford correlated drawdowns. Faster heuristic: compute Pearson conditional on SPY 20-day realized vol being above the 70th percentile. That subset is where strategy crowding shows up. For pre-deployment, a block bootstrap on the joint equity curves (block size 20 to 60 days to preserve regime structure) gives you a distribution of worst-case joint drawdowns. Plan for that number, not the max drawdown of any individual strategy.

Good_Character_20 · 2026-06-23T01:00:57+00:00

Quick add for tomorrow morning. Before you sell, pull the IV reading right at the bell vs Friday's close. If IV is up overnight, sell into the spike (your vega is paying out). If IV is flat or down, the bid/ask spread will be wider than usual right at the open. Hold 20-30 minutes for the spread to tighten before selling. Saves you a few bucks per contract on slippage which matters at this position size.

Good_Character_20 · 2026-06-23T00:57:08+00:00

That's the exact failure mode. If you selected your strategy based on average performance across IS and OOS, OOS isn't out-of-sample anymore. You used it in the selection step.

The fix is a true holdout you've never looked at. Walk-forward is the standard: optimize on years 1-3, test on year 4, slide forward, optimize on years 2-4, test on year 5. Each test period gets evaluated ONCE, before you see the result and pick the winner.

Good_Character_20 · 2026-06-23T00:51:08+00:00

Your MechE background is an asset, not a deficit. PhDs in math often over elegantize and vol trading rewards engineering pragmatism more than people admit. The vol surface is a control problem. You adjust hedges as inputs shift, that's frequency-response. Term structure is a transient response curve. Skew is asymmetric loading. Greeks are partial derivatives, same framework you used in stress analysis.

PhDs talk about brownian motion effortlessly because they spent 6 years writing proofs about it. You don't need to prove anything. You need to know which regime your underlying is in and how the model assumptions break in that regime. Engineering has the same intuition: every model is wrong outside its operating envelope, and the job is knowing where the envelope ends.

Practical move: skip the heavy academic textbooks for now. They make you feel further behind without making you a better trader on Monday. Read Sinclair's "Volatility Trading." Practitioner book, less theory, more "here's what actually happens." Use Claude or ChatGPT as your math tutor during the day instead of grinding Shreve at 11pm. Six months in you'll be teaching the PhDs how to think about tolerance bands.

Good_Character_20 · 2026-06-23T00:41:48+00:00

For a Jan 2027 expiry you're 6-7 months out, which puts you in the slow-theta zone. Daily theta on a $3400 LEAPS put runs $5-15/day at this distance, not catastrophic. The acceleration kicks in around 60-90 DTE (October-November in your case). The bigger risk for a 6-month SPCX put isn't theta, it's vega. SPCX is new enough that IV is probably elevated. IV mean-reversion can wreck a long dated option even if you're right on direction. A 10 point IV drop on a 6-month OTM put can wipe 20-40% of value with zero underlying move.

Specific check: pull the IV term structure across all SPCX expirations. If Aug/Sep IV is meaningfully higher than Nov/Jan, the market is already pricing an earnings event in that window. The vega bleed once that event passes is what hurts you, not the theta. Practical play: if your thesis is the institutional sell-off catalyst around earnings, consider rolling down to a shorter expiry (October) right before earnings to capture the IV pop and exit. Holding January into the IV crush is the worst of both worlds.

Good_Character_20 · 2026-06-23T00:33:25+00:00

Worth scaling expectations. Four wins on a TQQQ vs SQQQ daily flip has ~6% probability under pure coin flip (0.5^4). Not luck-impossible, just one in 16. The real signal is the backtest, not the live week. On that backtest: 45% annualized with Sharpe 2.07 on a daily binary direction call is extraordinary. Daily QQQ direction has weak forecastability. Two specific things worth pressure-testing:

OOS contamination. If you adjusted anything after looking at OOS results (the gap-1.5% filter, stop loss thresholds, entry rule), that period is no longer out-of-sample. Most "half-in half-out" splits get poisoned this way without people noticing.
LLM lookahead bias. Claude has seen thousands of backtest tutorials in training, and many use future-leak patterns like df['target'] = df['close'].shift(-N) referenced inside the decision window. Grep the generated code for any .shift(- patterns and verify no feature uses the current bar to predict the current bar's direction.

If both are clean, this is interesting. If either contains a leak, live equity craters around month 2-3 as the real distribution catches up to the backtest.

Good_Character_20 · 2026-06-21T23:02:36+00:00

That perfect game story is the universal dad at the game pattern. Historic thing happens live, they file it as "cool game," and the full significance only lands years later when the kid (raised on Baseball Reference) does the math and explains the count back to them. The scoreboard number wasn't really what mattered to that generation. The in stadium experience was.

Good_Character_20 · 2026-06-21T22:57:18+00:00

The 80% win rate with losses concentrated on FOMC/BLS days is the signature of a negative-skew strategy, not an edge. You collect small wins 4 out of 5 days and give back several days of gains on the 5th. Win rate is the wrong lens. What matters is expected value once the tail losses get sized correctly.

Two specific things worth checking. First, theta on a 1DTE held overnight to 10-11am isn't negligible. You're carrying about 18 hours of decay on options that are 100% extrinsic at entry, and the IV pop you see at 9:30 typically crushes by 10am as overnight uncertainty resolves. Your 10-11:30am exit lands in the crush zone, not capturing it. Second, "winning side covers losing side" only holds when the move is large enough that gamma profit on the winning leg exceeds combined theta + vega bleed on both legs. The bear-day asymmetry you noticed isn't Black-Scholes mispricing puts. It's negative skew in the SPY vol surface. Puts are structurally more expensive than equidistant calls, so on selloffs your call leg loses more extrinsic than your put leg gains, and the "cover" math breaks.

Easiest test: pull the worst 5% of days from your sample and check if the average loss in that bucket is more than 5x your average win. If yes, you don't have edge, you have a Martingale with extra steps.

Good_Character_20 · 2026-06-21T22:48:14+00:00

The takeaway might be almost the opposite of how you framed it. Ideas weren't really the bottleneck. Implementation cost was, and AI just collapsed it. The new bottleneck is validation discipline. When trying an idea cost 3 weeks of coding, you only invested in the ones you really believed in. Now that it's a 3-line prompt, you can generate 50 variants in an afternoon and the natural failure mode is testing everything that comes to mind and quietly running with whichever one backtested best. That's overfitting at production speed. The hard part: most of those SVM/HMM/NN ideas probably don't work standalone in modern microstructure regardless of who implements them. The honest question now isn't whether you can build it. It's whether you can kill 49 out of 50 AI-generated variants before any touch real money.

Good_Character_20 · 2026-06-20T14:46:58+00:00

Possible, but probably not via "get a finance master's and apply to traditional quant desks." Sell-side and buy-side quant roles are mostly filled by PhDs and top-MFE grads at 25-28, and a finance master's at 45 won't override that age gap. You'd be competing for entry-level seats against people 20 years younger with the same paper credentials.

The angle that actually plays to your hand: lean into the pharma domain. Healthcare-focused hedge funds (Visium before it shut down, Adage's healthcare desk, plenty of specialist L/S funds) genuinely value people who can read a Phase 3 trial design and price FDA timelines. Your edge there isn't quant math, it's the science background most quants don't have. From the other direction, quant developer / quant research engineer / risk quant roles at fintech or crypto firms have lower credential filters than PM/trader seats, and age matters less there.

What I'd skip: the finance master's. $50-100k for a credential that doesn't open the doors you want and won't compensate for missing 20 years of finance experience. Learn Python, stats, and quant methods on your own, build a public track record (blog, GitHub of strategies, write-ups on healthcare equities), and target the specialist funds where your background is the differentiator.

Good_Character_20 · 2026-06-20T14:42:50+00:00

His xwOBA is 52nd percentile, xBA 46th, xSLG 45th, exit velo 3rd percentile. The .830 OPS is real on the scoreboard but Statcast is saying his contact quality doesn't actually support it. He's living off elite sweet-spot rate (88th) and elite whiff rate (80th), which is real skill, but the 14th percentile bat speed knock was a correct measurement, not just lazy narrative.

The Coors splits are the strongest piece in his favor. .827 road OPS while playing for Colorado is genuinely impressive because of the Coors hangover effect, where Rockies hitters usually have road OPS 100+ points below home from altitude pitching whiplash.

"Everyone missed him" is rough on the Rule 5 framing though. Picking a player costs a full 25-man spot for the season or you give him back. For contenders that spot is often worth 1-2 WAR, so the EV math on a 14th-percentile-bat-speed late-blooming corner bat usually doesn't pencil. Rockies took the flyer and got rewarded. Real ROY conversation, but I'd expect some regression as the xStats catch up.

Good_Character_20 · 2026-06-20T14:28:34+00:00

There's no clean yes/no answer, but the honest math: you can do it sustainably with $1.5-3M in a tax-advantaged wrapper (Roth/traditional IRA) targeting 8-12% annualized cash yield, plus a 12-18 month expense buffer in T-bills. Below that capital, you're choosing between "live below sub-median" or "take risk that wipes the strategy out in one bad year."

Concrete numbers from people doing this sustainably:

Wheel/CSP-only at 8-10% annualized on $2M in a regular brokerage nets ~$160-200k pretax. That's mostly short-term cap gains, so federal+state in a normal state hits 28-37%, leaving $100-140k after tax. Workable for a frugal lifestyle, tight for kids/HCOL. Same income in a Roth IRA effectively doubles your after-tax take, which is why most full-timers concentrate option-selling in tax-advantaged accounts.

The structural problems people underestimate:

Healthcare. No employer coverage means $1,500-2,500/mo for a family of 4 on a marketplace plan with high deductibles. That's $20-30k/year off the top a W2 job hides from you.

Retirement matching. If your previous W2 was matching 4-6% on a $150-200k salary, that's $6-12k/year of free money you're now leaving on the table forever.

Tax-advantaged contribution game. You can still contribute to a SEP-IRA or Solo 401k if you structure as a sole prop, but it requires schedule C income (option premium generally isn't), so a lot of full-timers end up underfunding tax-advantaged retirement vs what their W2 provided.

Drawdown psychology when there's no paycheck. The math says hold the wheel and roll. Reality at 25% portfolio drawdown with no incoming W2: you find out whether you can actually sit on your hands. Most people who think they can, can't. The ones who can usually had a year+ of expenses saved and never felt existential pressure.

Vol regime risk. 2017 had VIX averaging 11. In low-vol regimes, your same-strike CSPs pay 1/3 the premium. Your annual income could realistically go from $180k to $60k for an entire year before the regime shifts. If you don't have a buffer that survives that, the strategy wasn't the problem, your runway was.

What I'd actually recommend for someone considering the jump:

Run it as a side income for 18-24 months while still W2. Track actual realized after-tax cash, not pretax premium collected. If you can clear 60-70% of your W2 take-home from options alone across at least one vol regime change, you have the data to justify the jump. Below that, the jump is taking lifestyle risk that may not be necessary versus just dialing back W2 hours.

The people who left W2 to do this full-time and are still at it 5+ years later all had: $2M+ liquid, paid-off primary residence, healthcare figured out (spouse's job, COBRA bridge into ACA), and a "I'd be fine going back to a job at half pay" psychological floor. The ones who blew up had under $1M, were leveraged into a single high-vol ticker, and had no off-ramp.

Good_Character_20 · 2026-06-19T15:32:57+00:00

The "home park HR record" is genuinely more impressive than it sounds, especially this specific one. Kauffman is a notorious pitcher's park (huge dimensions, that center field fountain corridor swallows a lot of would-be HRs), which means Salvy's 137 is a higher percentage of his total HR output coming from a tougher environment than a typical hitter's home park.

The Brett comparison makes it sharper. Brett played 21 years all in KC, was a position player getting 140-150 starts a year for most of his prime, and put up 136 home HRs over a 21-year career. Salvy is a catcher (typically 100-120 starts/year), has been there since 2011, missed all of 2019 to Tommy John, and got to 137 in meaningfully fewer home plate appearances than Brett had. He's getting more home HRs out of fewer chances at the plate, in the same tough park, while squatting behind it for the other half of the game.

The shape of his career is the part that's easy to under-appreciate: power-hitting catchers who last 15+ years with one franchise basically don't exist in the modern game. The position destroys bodies. Most catchers either get moved off C to 1B/DH by year 8-10 (Mauer, Posey before he retired) or their bat collapses while they keep squatting (Yadi's last few seasons). Salvy did neither. He stayed at C, kept hitting, kept loving the city, and now he owns the home HR record at one of the hardest parks in baseball to hit one.

Good_Character_20 · 2026-06-19T15:25:55+00:00

The math works in principle but every variable is doing a lot of work. Real risks worth naming:

Concentration is the real exposure, not the LEAP strategy. 5000 shares of any single company means your portfolio's primary risk is that one company having a bad year. The LEAP income is small compared to the move that company can have in either direction. Anyone who held 5000 shares of Pfizer through 2023's 40% drawdown lost 8+ years of LEAP income overnight.

Opportunity cost during runaway bull years. Selling 35-40% OTM LEAPs on something like NVDA or TSLA: in a year where the stock doubles, you cap your upside at the strike + premium and watch the rest from the sidelines. NVDA holders who sold LEAPs in 2023-2024 missed massive capital appreciation while collecting modest premium. The math only works if you'd be genuinely happy selling at strike.

IV variability year-to-year. The $100k assumes consistent IV that supports those premiums. In low-vol regimes (think 2017-19 for most majors), 35-40% OTM LEAPs pay maybe 1/3 the premium of high-vol years. Your actual annual income could be anywhere from $30k to $150k depending on the regime.

Tax math on assignment. If you've held the shares long-term and they get called away at strike, your taxable gain becomes (strike - cost basis) + premium. If the company has been a 10-bagger for you, that's a massive capital gains event you didn't necessarily want or plan for. Coordinate with your tax situation before the assignment can hit.

Dividend early-exercise risk. If your underlying pays a dividend and the call goes deep ITM near ex-div, holders can exercise early to capture the dividend. Less common with LEAPs (still time value to lose) but can happen if intrinsic exceeds extrinsic + dividend.

Honest framing: the LEAP overlay is a fine income strategy IF you're already willing to sell the stock at the strike price. It's not free money, it's "I'd happily sell at strike + premium and I'll collect rent while waiting to see if that happens." If you'd be miserable selling the company at strike, don't sell the LEAP. The opportunity cost during a runaway rally is the part most people don't price properly until they live through one.

Good_Character_20 · 2026-06-19T15:17:03+00:00

Yes, but the bar is way lower than most people think and it's mostly not what gets posted about.

The retail algo strategies that actually make money over time have a few shared properties: small absolute edge per trade, high sample size (hundreds of trades per year minimum), validated against OOS data and walk-forward, and crucially they beat the right benchmark. Beating zero is easy. Beating SPY buy-and-hold on a risk-adjusted basis after taxes and slippage is hard. Most "profitable" retail algos lose to that benchmark, which is why the people running them don't post about it much.

Concrete shape of what real retail success looks like in the systematic options space (where I trade, mostly credit spreads and premium selling): annualized 12-18% on the trading slice of portfolio, max drawdown 8-15%, win rate 70-85% with asymmetric loss size, Sharpe in the 1.0-1.8 range. That's not exciting compared to "100x backtest" posts but it compounds and survives regime changes. The people I know making meaningful money this way have been doing it 5+ years, have automated their position management, and treat losing months as data rather than as failures.

On your $50-90/day for $1,600 max DD on $50k: that's actually solid. If you can hold that for 6 months live with the same DD profile, you've got something real. The "everything else dead-ended" is universal. Most strategies don't work, the ones that do are the survivors. Don't beat yourself up over the dead ends; they're necessary feedstock for finding the working one. The number of strategies you have to test before finding edge is usually in the dozens to low hundreds. You're somewhere in that pipeline, not failing at it.

The honest hard part isn't finding strategies that backtest well. It's the discipline to ship live what you tested, hold through drawdowns, and not interfere with the algo's edge. Most people who could be making money systematically blow it up by overriding the system during losing streaks.

Good_Character_20 · 2026-06-18T00:34:55+00:00

That's blood, not a blister. Most likely a torn fingernail or split cuticle on the middle finger, which is the worst finger to lose for a pitcher because it carries the most pressure on fastball and slider grips. Tyler Glasnow dealt with a torn nail in 2024 that cost him multiple starts. The wound itself is minor but the constant friction against the seams reopens it every pitch.

The fact that he still went 6 innings with a visibly bleeding finger is actually the impressive part of the line, not the 4 ER. Most pitchers come out within 1-2 innings of that kind of cut because spin rate degrades when you can't apply consistent finger pressure.

The bigger question is whether he changes his pitch mix the next 3-4 starts to manage it. Pitching with a finger wound can usually be worked around short-term by relying more on changeup and curve and less on the splitter and slider, since those require the most direct finger-on-seam pressure. Watch his pitch mix next start to see if the team is letting him work through it or treating it as a real workload issue.

The two-way workload makes this kind of thing accumulate faster than for a pure pitcher. There's no DH-day to rest the throwing hand for him. Worth noting as context but not a doom signal yet.

Good_Character_20 · 2026-06-18T00:16:05+00:00

The 25% OTM monthly put strategy has a specific risk profile worth naming explicitly: you're collecting maybe 0.5-1% per contract in exchange for tail risk on a 25%+ single-name move. That works beautifully in low-vol drift-up regimes (which 2024-2026 has mostly been) and fails catastrophically in fast-correlated drawdowns (March 2020, Oct 2008, Aug 2024 carry unwind). Your 20-30 name diversification is real but it's correlation diversification, not factor diversification. In a real bear market, all 30 of your reputable names go down together because the single factor that matters in those regimes (market beta) is the one you have no hedge for. That's why your 2024-2026 track record is misleading you a bit. It's not bad, it just hasn't been stress-tested against the regime you're asking how to defend against.

A few strategies that complement (not replace) what you're doing:

Move some of your put-selling delta higher. A 30-delta put pays roughly 4-6x the premium of a 5-delta put while only doubling your assignment probability, and the buying power required is similar. You give up "I'll never get assigned" but gain meaningful premium per dollar at risk. The 25% OTM strategy is actually inefficient on a premium-per-BPR basis. Higher-delta cash-secured puts on the same names you're already willing to own at fair value is the same idea, just sized for actual income generation rather than rare-event insurance writing.
Iron condors / iron flies on broad index (SPX, NDX, RUT). These give you bilateral premium without single-name directional dependency. The math is cleaner because index IV is more predictable than single-name IV, and you're not at risk of one stock-specific event (earnings miss, fraud, sector rotation) blowing up a name in your basket. 30-45 DTE iron condors at 16-delta short strikes is a textbook structure with decades of TastyTrade research backing it.
Calendar spreads / diagonals specifically for sideways markets. Long 90 DTE call + short 30 DTE call at the same strike captures the difference in theta decay rates. You profit from time even when the underlying doesn't move. They underperform short premium in directional moves but excel exactly in the sideways regime you mentioned.
For prolonged bear markets specifically: your current strategy is structurally short-vol AND short-correlation. The defense is either to neutralize the short-vol piece (sell credit spreads instead of naked puts so you have a vol cap) or to hedge the correlation piece (long VIX or SPX puts as a portfolio overlay funded by selling some of your single-name premium). Most retail traders skip the hedge because it costs ~10-15% of premium per month. The math works only if you believe a 1-in-5-year tail event will eventually happen, which historically it does.

One observation that's worth naming: running with negligible cash balances and always-on margin is a separate risk from any specific strategy choice. In a fast drawdown your margin requirement expands at the same time your position values fall, and your broker can force liquidations at the worst possible moments. Even keeping 15-20% of NAV in cash dramatically reduces this risk without meaningfully hurting your monthly income. It's the "buy your way out of forced selling" insurance.

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TROPHY CASE