AI Bakeoff: GPT-5.4

Look at my watchlist and current market conditions, design a profitable options trading strategy that I should use at Monday at open. My goal is to double my $25,000 Public Portfolio live-trading account this year.

I’ll treat this as a Category 4b: Research-Then-Design options exploration with live-deployment intent. Because you want an options strategy, want it informed by your watchlist + current market conditions, and want something for Monday at open in a $25,000 live account with a 100% annual return goal, the plan must be research-first, use your actual account constraints, include a feasibility check, and end with a deployable winner plus a monitor.

Plan

1. Fetch your watchlist symbols
   Use Fetch User Watchlists to retrieve your saved watchlists and identify the symbols we should treat as your candidate universe. This grounds the strategy in your names instead of assuming a generic AI/options universe.
   Tool: Fetch User Watchlists

2. Analyze the current regime and watchlist context
   Use Stock Screener to assess current market conditions relevant to Monday-at-open options positioning: SPY/QQQ vs 50/200-day SMAs, drawdown from highs, RSI, realized volatility, and Index(VIX) level. In the same research pass, evaluate your watchlist names for current momentum / mean-reversion context so we know what type of options exposure is appropriate right now.
   Tool: Stock Screener

3. Research historical analogues for this regime
   Use Stock Screener to find prior periods where the market looked similar to today based on the regime metrics from Step 2. Following prior-knowledge constraints, explicitly include at least one bear regime (2022) and one recovery regime (2023) in the analysis so we don’t overfit to a single market type.
   Tool: Stock Screener

4. Study forward outcomes and option-entry frequency before designing trades
   Use Stock Screener to measure what happened next in those analogue periods: forward returns over 30/60/90 days, breach behavior, expectancy, and entry-trigger frequency on the watchlist names. Per prior knowledge, validate looser thresholds first and make sure candidate signals actually generate trades before building portfolios.
   Tool: Stock Screener

5. Spawn parallel options-strategy subagents to explore distinct theses
   Because this is an options exploration request, I will spawn multiple optionsStrategy subagents in parallel, each focused on a different thesis discovered from Steps 2–4 rather than pre-committing to one structure upfront. The tracks will be designed around distinct ideas such as:

   • a momentum-oriented options thesis for names showing strong continuation
   • a mean-reversion-oriented options thesis for oversold but strong watchlist names
   • a hybrid / regime-gated defined-risk thesis for names where market conditions matter most
     Each subagent will respect the $25,000 account constraint, favor defined-risk structures, cap trade frequency at roughly monthly, and avoid previously failed approaches from prior knowledge where appropriate.
     Tool: Subagents (optionsStrategy)

6. Wait for subagents, then preserve winners by track
   After the subagents complete, synthesize the results by keeping the top 2-3 portfolios from each exploration track rather than collapsing everything into one “super strategy.” This preserves diversity across momentum, mean reversion, and hybrid/regime-sensitive approaches.
   Tool: waitForSubagents / synthesis

7. Create mechanical-only candidate options portfolios from the winning ideas
   Using the best subagent findings, create 4-8 mechanical candidate portfolios with OpenOption/CloseOption rules only — no LaunchAgent in the backtest candidates. The candidates will:

   • use your watchlist-derived names or the best subset from them
   • use defined-risk structures suitable for $25,000
   • be sized realistically for a small account
   • target no more than about one trade per month
   • include proper exits via CloseOption
   • avoid exact portfolio recreations already listed in prior knowledge
     Tool: Create YAML Portfolios

8. Backtest across multiple regimes, including current-period relevance
   Backtest all candidate portfolios across multiple distinct periods, explicitly including:

   • 2022 bear regime
   • 2023 recovery / AI-led regime
   • 2024 continuation regime
   • 2025–present / recent conditions
     This is mandatory given the extreme regime dependence in prior runs.
     Tool: Backtest Portfolios

9. Analyze results with goal and robustness first, not just best-year return
   Use Read Backtest to rank candidates by Sortino, maximum drawdown, worst-regime return, average return across tested regimes, and current-regime performance. If any tested signal shows very high upside in a single year, iterate on drawdown reduction before discarding it. If any candidate has zero trades across all regimes, repair by widening thresholds rather than promoting it.
   Tool: Read Backtest

10. Goal Feasibility Check + Monday-at-open eligibility check
    After identifying the best candidate, explicitly compare:

• Best single-year return
• Average annualized / average regime return
• Worst regime return
• Your goal: 100% this year
  If average performance is materially below target, the recommendation must flag that gap honestly. Also check the winning strategy’s current activation filters against the Step 2 regime data and state whether it is ACTIVE for Monday open or armed but inactive.
  Tool: Read Backtest / session synthesis

11. Attach a portfolio monitor to the winner for ongoing management
    Because this is a recurring live goal and you want it managed if the market turns, add one LaunchAgent portfolio monitor to the winning deployed portfolio. This monitor is a watchdog / reviewer, not the primary trader. It should review open positions, SPY vs 200-day SMA, VIX, aggregate drawdown, and any option position showing unusually adverse P&L, then propose defensive changes if needed.
    Tool: Edit Portfolio

12. Deploy the final portfolio appropriately
    Deploy the winning portfolio to your Public Portfolio live-trading account only if the feasibility and eligibility checks support doing so. If the strategy is not currently eligible for Monday open, present it as armed but inactive rather than pretending it will trade immediately.
    Tool: Fetch User Portfolios, Edit Portfolio

Fetch all of my saved watchlists and their symbols so I can use them as the candidate universe for designing an options strategy for my $25,000 Public Portfolio live-trading account.

Here is your 1 watchlist, in the same order as in the app:

1. All my favorites

META (stock), GOOG (stock), NVDA (stock), BTC-USD (crypto), HOOD (stock), LLY (stock), SPY (stock), QQQ (stock), AMD (stock), AVGO (stock), TSM (stock), ANET (stock), GS (stock), DUOL (stock), XOM (stock), COP (stock), POWL (stock)

Using the user's watchlist as the candidate universe for a $25,000 options strategy, analyze current market conditions and current watchlist context in one pass. I need: (1) current SPY and QQQ price vs 50-day and 200-day simple moving averages, current 14-day RSI, drawdown from 52-week high, and current VIX level; and (2) for these watchlist symbols — META, GOOG, NVDA, HOOD, LLY, SPY, QQQ, AMD, AVGO, TSM, ANET, GS, DUOL, XOM, COP, POWL — provide current price, 14-day RSI, 30-day price rate of change, 20-day and 60-day price standard deviation or other recent volatility measure available, and identify which names currently look strongest for momentum versus most oversold for mean reversion. Exclude BTC-USD because this request is for stock/options strategy design.

Using the current market snapshot already established (SPY and QQQ below both 50-day and 200-day SMAs, VIX 23.87, and the watchlist context showing relative strength in COP, AMD, XOM, POWL and oversold weakness in META, LLY, ANET, AVGO, NVDA, HOOD, DUOL), research historical analogues and forward outcomes for this regime in one pass. I need a SQL-style research summary that does all of the following: (1) explicitly analyze at least one 2022 bear period, one 2023 recovery period, plus 2024 and 2025-to-date context; (2) measure 30-day, 60-day, and 90-day forward returns after days when SPY was below both its 50-day and 200-day SMAs and VIX was elevated around the low-to-mid 20s; (3) for watchlist names META, GOOG, NVDA, HOOD, LLY, SPY, QQQ, AMD, AVGO, TSM, ANET, GS, DUOL, XOM, COP, POWL, estimate entry frequency / breach rates / expectancy for two loose signal families suitable for options design: a momentum continuation setup (for stronger names such as COP/XOM/AMD/POWL) and a mean-reversion setup (for oversold names such as META/LLY/ANET/AVGO/NVDA/HOOD/DUOL); and (4) identify which symbols and which thesis types appear most viable for a $25,000 defined-risk options strategy at Monday open. This is a DIFFERENT intent from the prior screener call: the prior call described the current snapshot; this call is for historical analogues, forward outcomes, and signal frequency / expectancy research to support options strategy design.

I have completed the research phase through Step 4 of the plan: we fetched your watchlist, analyzed the current regime, and studied historical analogues plus signal expectancy. The next planned step is to explore multiple distinct options theses in parallel using optionsStrategy subagents, since this is explicitly an options request and we now have enough evidence to seed focused tracks.

Research-driven takeaways to feed the subagents:

• Current regime: SPY and QQQ are below both 50-day and 200-day SMAs, with VIX 23.87. That is a risk-off / elevated-volatility backdrop, so we should prefer defined-risk structures and avoid naked premium.
• Momentum continuation looked weak in the analogue study for the stronger names you flagged:
  • POWL only modestly positive
  • XOM, COP, AMD momentum expectancy was weak to negative in this regime sample
• Mean reversion looked more viable in this regime sample for oversold names:
  • META was a statistical outlier and needs caution, not blind trust
  • HOOD and NVDA looked more usable
  • LLY / AVGO / ANET were weaker but still candidates for looser defined-risk mean reversion exploration
• For a $25,000 account, prior knowledge strongly favors 5-point defined-risk spreads, looser entry filters first, and explicit testing across 2022 / 2023 / 2024 / 2025-present before any live recommendation.

So the next action is to spawn 3 optionsStrategy subagents for distinct theses:

1. Mean-reversion bull call spread track on oversold watchlist names (focused on HOOD, NVDA, META, AVGO, ANET, LLY; avoid recreating exact prior failed structures)
2. Momentum defined-risk continuation track on the relative-strength names (COP, XOM, AMD, POWL), but with looser triggers and realistic skepticism because the regime research was weaker
3. Hybrid regime-gated track combining market filter + defined-risk spreads on the most viable names from both camps, designed specifically for Monday-open eligibility in a bearish / elevated-VIX environment

Please proceed by launching those subagents. The correct next JSON would be a createSubagents command, not another screener call or final recommendation yet.

I have completed the research phase through Step 4 of the plan: we fetched your watchlist, analyzed the current regime, and studied historical analogues plus signal expectancy. The next planned step is to explore multiple distinct options theses in parallel using optionsStrategy subagents, since this is explicitly an options request and we now have enough evidence to seed focused tracks.

Research-driven takeaways to feed the subagents:

• Current regime: SPY and QQQ are below both 50-day and 200-day SMAs, with VIX 23.87. That is a risk-off / elevated-volatility backdrop, so we should prefer defined-risk structures and avoid naked premium.
• Momentum continuation looked weak in the analogue study for the stronger names you flagged:
  • POWL only modestly positive
  • XOM, COP, AMD momentum expectancy was weak to negative in this regime sample
• Mean reversion looked more viable in this regime sample for oversold names:
  • META was a statistical outlier and needs caution, not blind trust
  • HOOD and NVDA looked more usable
  • LLY / AVGO / ANET were weaker but still candidates for looser defined-risk mean reversion exploration
• For a $25,000 account, prior knowledge strongly favors 5-point defined-risk spreads, looser entry filters first, and explicit testing across 2022 / 2023 / 2024 / 2025-present before any live recommendation.

So the next action is to spawn 3 optionsStrategy subagents for distinct theses:

1. Mean-reversion bull call spread track on oversold watchlist names (focused on HOOD, NVDA, META, AVGO, ANET, LLY; avoid recreating exact prior failed structures)
2. Momentum defined-risk continuation track on the relative-strength names (COP, XOM, AMD, POWL), but with looser triggers and realistic skepticism because the regime research was weaker
3. Hybrid regime-gated track combining market filter + defined-risk spreads on the most viable names from both camps, designed specifically for Monday-open eligibility in a bearish / elevated-VIX environment

Please proceed by launching those subagents. The correct next JSON would be a createSubagents command, not another screener call or final recommendation yet.