BTC Trap Playbook

A structured artifact for agents that need more than a one-line trap warning.

paidartifactBTCArtifact

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Artifact delivery after claim token

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Use this when

Add a downside-risk-aware BTC playbook to a research stack.
Compare breakout and bulltrap logic side by side.
Use in human review or semi-automated decision loops.

Do not use this for

Users expecting custody, brokerage, or execution.
High-frequency trading systems.
One-click profit promises.

Evidence and positioning

Designed as a monetizable artifact layer rather than a raw signal feed.

Artifact delivery

Trap detection framework
Invalidation map
Risk commentary
Example outputs
Claim-token-based delivery

Package versionartifact-0.1

The package should explain the risk framework first, then optionally attach reference code for teams that want implementation guidance.

Asset thesis

What the buyer actually receives

BTC rebounds become dangerous when participation fades while structure is still vulnerable to rejection, especially after euphoric narrative resets.

Primary formatsjson / markdown

Suggested filesmanifest.json, README.md, examples.json, reference_strategy.py (optional)

Decision framework

Treat fading volume and channel rejection as the highest-priority trap clues.
Use downside magnet zones as scenario planning tools, not guaranteed targets.
Require multiple pieces of context before upgrading a rebound into a durable trend change.

Operating modes

contained

Trap risk is present but not dominant when acceptance and participation improve.

A fresh rejection or fading participation should downgrade the thesis again.

moderate

Stay tactical and avoid assuming a clean trend change.

The thesis improves only if participation expands and structural acceptance holds.

elevated

Use a defensive posture and expect a larger unwind to remain possible.

Only reduce trap risk when structure and participation both materially improve.

Parameter suggestions

primary_timeframe4h

caution_signalsfading volume / channel rejection / failed support acceptance

downside_magnet_reference58000 / 60000

supporting_filteravoid long-only bias when trap risk is elevated

Machine payload

Preferred inputs

price, volume_trend, channel_status

Recommended follow-up

Call /api/v1/strategies/btc-bulltrap-detector/invoke for deterministic trap classification. Use strategy outputs as structured overlays inside a larger BTC decision system.

Sample package

What the V1 artifact package can look like

This is the delivery shape we should standardize around in V1: machine-readable metadata, human-readable explanation, structured examples, and optional Python reference code.

Package filemanifest.json

Package fileREADME.md

Package fileexamples.json

Package filereference_strategy.py

{
  "package_name": "btc-trap-playbook",
  "package_version": "artifact-0.1",
  "strategy_slug": "btc-trap-playbook",
  "strategy_name": "BTC Trap Playbook",
  "delivery_type": "artifact",
  "pricing_tier": "paid",
  "market_scope": "BTC",
  "primary_timeframe": "4h"
}