Two small follow-ups from the session:
- kevin-analyze.sh: always cp the local analyze_kevin_video.py into
the pod before exec, so the wrapper works even before CI ships an
image with scripts/ baked in (saw this hit the user with
ModuleNotFoundError on stale images).
- reanalyze_kevin_videos.py: switch from DELETE+INSERT to
append-only. The old behaviour violated the FK constraint on
kevin_signal_bridge_state.mention_id; the new behaviour adds a
fresh v2 analysis alongside the v1 row and lets the API surface
the latest. Old v1 rows preserved for audit.
Given a YouTube video ID or URL, runs the same caption-extraction +
LLM-analysis pipeline the watcher uses in prod, then prints the
extracted tickers + actions sorted by action priority and conviction
descending. No DB writes, no Redis publish — strictly observational.
Two entry points:
- scripts/analyze_kevin_video.py — pure Python; needs yt-dlp +
Anthropic OAuth token in env. Local laptop yt-dlp tends to hit
"Sign in to confirm you're not a bot" rate-limits; running inside
a cluster pod avoids this.
- scripts/kevin-analyze.sh — wrapper that finds the running
trading-bot-workers pod and execs the Python script in the
meet-kevin-watcher container. Easiest invocation.
Example:
$ ./scripts/kevin-analyze.sh poUJIZRmFew
=== Meet Kevin analysis — poUJIZRmFew ===
Market outlook: bullish
TICKERS (12):
SYMBOL ACTION CONV HORIZON RATIONALE
APPF buy 85.0% long_term Apploven's looking fantastic ...
SOX buy 80.0% months Semiconductor ETF momentum ...
...
