Summary of "HOW TO USE THE BIG GREEN MONSTER AND THE SKEWDRIVER TO MAKE DECISIONS!"
Business / decision-making focus (what the “Big Green Monster” + “Skewdriver” are used for)
The video primarily covers how to use two decision-support tools—a market decision dashboard (options/market fair-value context) plus a market fair-value framework—to decide whether to take or avoid trades. This is especially relevant for option-selling trades such as the “Rick” trade in SPX/ES.
Frameworks / playbooks mentioned (as operational tools)
Big Green Monster (fair value / overbought-oversold zoning)
Purpose: Determine whether current price is fairly priced, oversold, or overbought relative to a projected fair-value band.
Inputs / method (as described):
- Uses a volume profile + market profile (time-at-price) concept.
- Projects levels into the current month using prior month price action.
- Uses custom Fibonacci retracements/extensions (with coefficients changed vs default) to define bands.
- Overlays moving averages:
- 20 EMA
- 50 SMA/MA
- 200-period MA
Decision logic (rule-of-thumb):
- If price is in the 50 line / fair value zone → market is “fairly priced” (no strong edge that it must revert).
- If price moves into an oversold band → expectation: revert upward toward fair value.
- If price moves into an overbought band → expectation: revert downward toward fair value.
- If price continues beyond the “fair” band into deeper bands, it flips to overbought/oversold relative to the next fair-value zone.
Skewdriver (options skew tail-indicator)
Purpose: Provide an “early warning / tail indicator” from SPX option pricing—i.e., whether skew implies a drop is more or less likely soon.
Inputs / method (as described):
- Focuses on SPX options (not ES) at about ~30 days out.
- Looks at the 20-point spread put side and identifies the contract trading around $5.20.
- Measures how far that strike is from current price (in “points away”).
Decision thresholds (explicit):
- ~25–35 points away = normal conditions.
- If distance moves down toward ~20 points away → implies a drop may be coming sooner.
- If it moves below normal (e.g., around 15 points away) → stronger indication a correction is coming sooner.
- If it moves far above normal (example given: ~86 points away) → skew implies the opposite (market likely to go upward).
Combined trade filter (“objective qualifiers”)
For taking selling trades (the “Rick” trade / put spreads style):
- Use Big Green Monster to check whether price is fair vs overbought/oversold.
- Use Skewdriver to check whether skew is normal vs signaling imminent downside/upside.
- Avoid trades when the two indicators conflict with the intended direction.
Concrete example: “Rick trade” workflow (execution logic + sizing concepts)
The host models a systematic “Rick trade” entry process using objective criteria.
“Rick” trade setup (as described)
- Frequency: Monday / Wednesday / Friday
- Timing: often placed in the last ~15 minutes of the day
- Structure: SPX option spread targeting strikes around ~5 delta / ~5% probability.
- Example values mentioned:
- Sell around strikes near a 5% probability of being out of the money (examples: ~42.90 / 42.95 class of strikes).
- Target spread width: about ~20 points
- Premium target: about ~$0.35–$0.45 credit per contract
- Example buying power cited: around ~$2,000
Management / rolling:
- If the trade is threatened, Rick rolls the spread out to later expirations, seeking credit.
- If unable to roll for credit, the example says the trade may be closed for a loss (“sucked it up buttercup”).
Performance / track record metrics mentioned (high level)
Rick mentions (framed more as educational than strict business KPIs):
- ~147 trades in the year so far (and almost 250 across multiple accounts)
- Rolled only ~6 times in the recent period
- Many rolls were timing/price-movement-related; generally able to roll for credit, except for a specific abnormal day.
Decision example: “Good day to sell Rick trade?”
A scenario describes the host concluding it’s likely a good day because:
- Big Green Monster: price is near fair value / point of control (not deeply overbought).
- Skewdriver: skew is in the normal range (~25–35 points away) (not signaling imminent downside shock).
It’s also emphasized that a crash can still happen: these tools are probabilistic filters, not guarantees.
Product / ops-ish “automation” example (portfolio “AIM” spreadsheet)
Despite the title focus on Big Green Monster & Skewdriver, there’s also a major section on automated portfolio management via a spreadsheet.
AIM (Robert Lucello’s Automatic Investment Management)
Portfolio configuration described:
- Uses a basket of 12 underlyings:
- 10 leveraged ETFs
- plus O (real estate investment trust paying monthly dividends)
- plus a bond fund
- Timing: started in the portfolio on the 19th (month detail not fully consistent in the text, but the start date is stated as the 19th).
Allocation rule described:
- Put ~80% of the portfolio into those underlyings.
- Keep ~20% in cash.
Signal philosophy (explicit rule):
- Designed to never sell at a loss (only sells when it can book profit).
- Designed to tell you to buy when the asset drops, based on triggers.
Performance metric mentioned (example):
- On a $50,000 account, described as up about ~$2,400 since the start date (“since the 19th”).
Operational cautions:
- Spreadsheet must match actual broker share counts.
- Example issue: broker account showed a different share count for UMDD and YDU-type tickers (Tastyworks showed more shares than the spreadsheet), implying the need for order reconciliation.
- Execution dependency: spreadsheet signals are “hooked up” to Thinkorswim; mobile may not bring in triggers unless integration/data sync works.
Custom “governor” tweak (operational control mechanism)
The host adds a throttle based on consecutive buys/sells:
- If there’s a streak of 2 (buying or selling streak), it adjusts a governor multiplier (example: 0.1 → 0.15).
- If there were 9 sales in a row, it changes to 0.5, which slows further activity.
Other decision framework mentioned: “Intrinsic value” + LEAPS (options investment approach)
This section is more investing-focused than business ops, but includes a structured approach.
LEAPS “book premise” (long-term calls on big down days)
Core idea: Buy LEAPS calls on quality tech companies after a major correction day (the host emphasizes “wait for a big down day”).
Example constraints:
- Break-even price should be ≤ ~5% above the current stock price (described via an Apple example).
- Choose an option with a high probability of being ITM (framed around ~74–75% in examples).
Follow-on tactic (host adds “sell calls against it”):
- On subsequent big up days, sell nearer-dated calls to reduce basis.
- Timing mentioned includes ~7 days out or around ~45 days out depending on workflow.
Concrete actionable recommendations (as stated or implied)
For trading decisions (SPX / ES)
- Consider the “Rick trade” only when:
- Big Green Monster indicates fair value (not strongly overbought/oversold vs the intended exposure).
- Skewdriver shows normal skew (~25–35 points away).
- Avoid/stand down when:
- Big Green Monster suggests overbought where mean reversion risk is high.
- Skewdriver indicates skew moving toward ~20 or ~15 points away (downside likely sooner).
For automated portfolio management (AIM spreadsheet)
- Reconcile share quantities between the spreadsheet and broker regularly to prevent bad signals.
- Use the system at different check frequencies (the host suggests weekly/monthly checks may be sufficient).
For the LEAPS approach
- Don’t initiate LEAPS unless there is a meaningful down day/correction.
- Consider basis reduction by selling calls on strength (if compatible with your strategy).
Key metrics / KPIs explicitly mentioned
Market / trading indicators
- Skewdriver distance thresholds:
- Normal: ~25–35 points away
- Warning: ~20
- Stronger warning: ~15
- Extreme opposite example: ~86
- “Rick trade” target characteristics:
- ~5 delta
- ~5% probability of expiring out of the money
- Premium target: ~$0.35–$0.45 credit
- Spread width example: ~20 points
- Example risk/buying power: ~$2,000
Portfolio automation (AIM)
- Allocation: 80% invested / 20% cash
- Performance example: +$2,400 on a $50,000 account since starting “on the 19th”
- Governor multiplier examples:
- 0.1 → 0.15 for streak length 2
- 0.15 → 0.5 for streak length 9
Presenters / sources mentioned
- Presenters / hosts referenced:
- Bobby (speaker; “Sweet Bobby” referenced)
- Rick
- Dwayne
- Drew
- Lauren Rick / lauren rick (attendee)
- Rod
- Jason (director of technology / programmer)
- Ken
- Method/source credits:
- Blake Young (credited for “volume/market profile / voodoo” method, plus daily chart adaptation mention)
- Robert Lucello (source of AIM strategy; also book: How to Make a Million Dollars in the Stock Market Automatically)
- Scott Ruble (Stratums and Trade) (credited for the skewdriver observation)
- Books/resources mentioned:
- Stacey (attendee)
- Book: “Intrinsic” (author referenced indirectly)
- Broker critique classics referenced: “Where Are All the Customers’ Yachts?” / “Here Is Where…” (title partially mangled by subtitles)
Category
Business
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