KOL feeds: fix dead/blocked sources, drop stale feeds (29→25)

Feed-health pass over KOL_FEEDS:
- raoulpal: stale Substack (last 2024-05) → Real Vision podcast feed
- dampedspring: paywalled (0 entries) → free "Damped Spring 101" Substack
- unchained: Cloudflare 403 → canonical Megaphone podcast feed
- lynalden: Cloudflare 202 → FeedBurner mirror
- glassnode: recovered via httpx http2=True (was 403 on HTTP/1.1)
- browser User-Agent + Accept headers on feed fetch
- removed dead feeds with no active replacement: placeholder,
  dragonfly, niccarter, eugene
- pin h2==4.3.0 (required by http2=True)

All 25 remaining feeds verified fetching real body content; newest
post per feed ≤88d. Bundles in-flight KOL-module work already in the
working tree (kol_x ingest, migration 027, tests).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

app/services/telegram_digest.py

@@ -29,6 +29,7 @@ so a coalesced cron or worker restart can't double-send within a day.
 
 from __future__ import annotations
 
+import html
 import logging
 from dataclasses import dataclass
 from datetime import datetime, timedelta, timezone
@@ -138,26 +139,43 @@ async def build_global_digest(now: Optional[datetime] = None) -> GlobalDigest:
         )
 
         # ── KOL: last 24h posts + divergences ────────────────────────────
+        # Exclude twitter noise (gm / RT / jokes) from the count. Substack
+        # posts have tier=NULL (kept); twitter signal posts have tier!='noise'
+        # (kept); twitter noise is dropped. Without this filter a KOL's gm/RT
+        # spam inflates the daily "N KOL posts" stat. Mirrors the /kol/posts
+        # signals_only filter and the frontend.
         kol_posts = (await db.execute(
-            select(KolPost).where(KolPost.published_at >= cutoff_24h)
+            select(KolPost).where(
+                KolPost.published_at >= cutoff_24h,
+                (KolPost.tier.is_(None)) | (KolPost.tier != "noise"),
+            )
         )).scalars().all()
-        # action lives inside tickers_json; easier: classify on summary text.
-        # The structured action is on KolDivergence rows; for the bare count
-        # we just split posts by whether divergence rows reference them.
+        # Count bullish/bearish/neutral at POST level, not divergence-pair level.
+        # A single post can match multiple on-chain events (one per ticker/wallet),
+        # producing several KolDivergence rows. Counting rows would inflate the
+        # numbers — a post mentioning 3 tickers was previously counted 3× (bug).
         bullish = bearish = neutral = 0
         divergence_rows = (await db.execute(
             select(KolDivergence)
             .where(KolDivergence.post_at >= cutoff_24h)
             .order_by(KolDivergence.created_at.desc())
         )).scalars().all()
+        # Deduplicate by post_id; for posts with mixed directions take "long"
+        # first (a bullish call that also hedges short is net bullish).
+        post_direction: dict[int, str] = {}
         for d in divergence_rows:
-            if d.direction == "long":
+            if d.post_id not in post_direction:
+                post_direction[d.post_id] = d.direction
+            elif d.direction == "long":
+                post_direction[d.post_id] = "long"   # long overrides neutral
+        for direction in post_direction.values():
+            if direction == "long":
                 bullish += 1
-            elif d.direction == "short":
+            elif direction == "short":
                 bearish += 1
             else:
                 neutral += 1
-        # Posts not yet classified into divergences just count as neutral.
+        # Posts not yet cross-referenced with on-chain data count as neutral.
         neutral += max(0, len(kol_posts) - (bullish + bearish + neutral))
 
         divergences_24h = sum(
@@ -168,7 +186,9 @@ async def build_global_digest(now: Optional[datetime] = None) -> GlobalDigest:
             if d.signal_type != "divergence":
                 continue
             # "Hayes publicly bullish BTC, on-chain selling"
-            sample_divergence = (
+            # Escape — ticker is AI-extracted (kol_analysis) so not guaranteed
+            # HTML-safe; the digest is sent with parse_mode=HTML.
+            sample_divergence = html.escape(
                 f"{d.handle} publicly {d.post_action} {d.ticker}, "
                 f"on-chain {d.onchain_action}"
             )