3182-regime-aware-entry-engine

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Build a regime-aware entry plan that outputs a 9-column execution table with first buy, add levels, stop, target, and action tags.

Starchild-ai-agent By Starchild-ai-agent schedule Updated 6/6/2026

name: "@3182/regime-aware-entry-engine" version: 1.0.0 description: Build a regime-aware entry plan that outputs a 9-column execution table with first buy, add levels, stop, target, and action tags. author: Agentway tags: [trading, risk-management, entries]

Regime-Aware Entry Engine

When to use

Use this workflow when the user asks:

  • "Is this dip a buy?"
  • "What are the opportunities after a selloff?"
  • "Give me entry/add/stop/target levels"
  • "Build me a tactical watchlist"

Works for equities/ETFs (US/HK/A-share) with market data support.

Output format (mandatory)

Always produce a table with:

  • Ticker
  • First Buy
  • Add1 (-8%)
  • Add2 (-15%)
  • Stop
  • Target
  • Upside
  • Regime Tag (RISK_ON, RISK_OFF, PANIC)
  • Action (BUY_NOW, WATCH, AVOID)

Workflow

1) Detect regime first

Use broad-market proxies before single-name calls:

  • Growth beta proxy (e.g., QQQ)
  • Broad market proxy (e.g., SPY)
  • Sector stress proxy (e.g., SOXX/SMH when tech-led)
  • Defensive relative strength (e.g., XLU/XLV vs XLK)

Classify:

  • RISK_ON: breadth stable, growth leading, no stress spike
  • RISK_OFF: broad pullback, cyclicals weak, defensives holding up
  • PANIC: disorderly selloff / correlation spike / large intraday dislocations

2) Build candidate pool

Start with user’s watchlist first. If not provided, build from:

  • prior winners with healthy pullback
  • sector leaders with intact structure
  • avoid low-liquidity names in stress regimes

3) Score each candidate

Use a simple composite score (0–100):

  • Trend quality (35%): still above key medium trend context; no structural break
  • Pullback quality (30%): meaningful discount from recent high, but not freefall
  • Risk efficiency (20%): stop distance vs expected upside
  • Liquidity/Execution (15%): spreads/volume/volatility practicality

4) Generate execution levels

  • First Buy: current value zone (no chasing)
  • Add1: approx -8% from first buy reference
  • Add2: approx -15% from first buy reference
  • Stop: invalidation level (structure break)
  • Target: base-case recovery objective
  • Upside: (Target / current - 1)

5) Apply regime guardrails

  • In RISK_ON: normal position sizing, allow 2-stage adds
  • In RISK_OFF: reduce first tranche size, prioritize quality balance sheets
  • In PANIC: smaller probes only; keep larger cash buffer; avoid forced averaging

6) Produce clear actions

  • BUY_NOW: in zone + structure acceptable
  • WATCH: close but not yet in range / waiting confirmation
  • AVOID: structure broken, governance risk, or asymmetry poor

Messaging style

  • Keep it execution-first, concise, and numeric.
  • Do not promise certainty.
  • Always include a one-line risk note: "Not investment advice; follow stop discipline."

Quality checks before finalizing

  • Levels are internally consistent (Add2 < Add1 < First Buy)
  • Stop is below buy ladders for long setups
  • Upside is computed from current price, not stale references
  • Action tag matches regime + structure

Optional extension (risk layer)

If governance/event risk is detected (management/legal/disclosure shock), force downgrade:

  • Action cannot be BUY_NOW until risk is clarified
  • Cap size at half normal risk budget

Reuse pattern

For repeated daily usage:

  1. Pull latest broad market + watchlist quotes
  2. Recompute regime
  3. Update table only where trigger state changed
  4. Push only changed rows to avoid alert spam
Install via CLI
npx skills add https://github.com/Starchild-ai-agent/community-skills --skill 3182-regime-aware-entry-engine
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