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.

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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.

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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).

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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.

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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”).

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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.

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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.

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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.

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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.

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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.

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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).

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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

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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)