🐯 LYNX v1.0.0 — Adaptive MIN_SCORE Self-Tuner

The Vulture v4.1 story productized. Lynx is a momentum agent that audits its own closed-trade history every 6 hours and raises its own MIN_SCORE when low-conviction buckets bleed.

Why this strategy exists

When Vulture's autonomous agent ran the 30-trade audit that led to v4.1, it manually identified that Score 7–8 trades had been net-negative and raised MIN_SCORE 7→9 by hand. The fix worked. The question Lynx asks: why is that operation manual?

Lynx bakes the same operation into a scheduled cron. Every auditEverySec (default 6h), it:

1. Pulls its own closed-trade history via audit_query(user_ids=[senpiUserId], action_type="close", limit=auditLimit)
2. Parses the entry score from each trade's ai_reasoning field
3. Buckets trades by score and computes {n, avg_roe_pct, win_rate_pct} per bucket
4. Identifies the highest-scoring bucket at or above the current floor that has n >= minBucketN AND avg_roe_pct < bucketBleedThresholdPct
5. If found, recommends MIN_SCORE = that_bucket + 1 (capped at maxMinScore)
6. If the recommendation moves by at least the hysteresis (1), persists the new MIN_SCORE and logs the adjustment

This is the first fleet agent that modifies its own behavior based on its own trade history. The same loop the Vulture team ran by hand — but every 6 hours, automatically, on every Lynx instance.