Crypto Trading Decision Frameworkv1.0.0

A retail trader finds a promising breakout setup on an altcoin and needs to decide how much capital to allocate. They run the setup through Gate 1 sizing formula (1% risk, 1.5% stop distance) and the Gate 2 entry checklist before placing any order. The framework prevents them from oversizing on a conviction play and forces them to pre-define hard stop, time stop, and TP ladder.

A funded trader managing a small book of strategies uses the halt decision tree to evaluate whether an underperforming mean-reversion strategy should be paused, shrunk, or killed. The 3R drawdown and PF degradation checks give objective thresholds rather than emotional calls. Escalation rules ensure any new live capital deployment routes through human approval before execution.

An analyst at a small digital-asset fund prepares an entry recommendation and must attach confidence and research-depth tags per the framework's requirements. The 4-model consensus rule kicks in because the deployment size qualifies as significant capital. If two of the four LLMs disagree, the decision defers 24 hours rather than forcing a trade.

A quant builder runs a new signal through Gate 2's ten-item checklist, checking n≥20 sample size, out-of-sample profit factor ≥1.3, and MDD ≤20%. The scoring band (10/10 proceed, <8/10 don't enter, <6/10 kill) gives hard go/no-go thresholds. This catches curve-fit strategies before they reach live capital.

A trading educator uses the three-gate framework as curriculum material to teach students why they keep blowing accounts. The 'What NOT to do' list (moving stops, averaging down, early exits) directly targets the most common behavioral failures. Students practice running each hypothetical trade through the halt tree until the process becomes second nature.

A paid Discord or Substack where the operator publishes entry and exit calls that already passed the framework's gates. Subscribers pay monthly for disciplined, pre-screened setups with confidence tags attached. The framework's structure becomes the service's differentiator versus unstructured call channels.

A prop-firm-style business that uses the halt decision tree and escalation rules as its risk policy engine. Traders on the platform must pass a framework-based ruleset before scaling account size. The firm monetizes via challenge fees and profit splits on funded accounts.

A B2B tool that embeds Gate 1 sizing, Gate 2 checklist scoring, and halt-tree automation into an existing trading terminal or portfolio dashboard. Small funds and family offices license it to enforce consistent process across multiple traders. The 4-model consensus rule can be offered as an add-on API integration.