feat(sentiment): add 30 new sources for uncovered trade assets

Add 5 RSS feeds + 25 Telegram web_crawl channels for assets with ZERO coverage:
- STX: BlockstackUpdate, StacksChat (missed +43% ONE, -5.65% STX)
- FET: fetch_ai_announcements, fetch_ai (missed +22.68%)
- XTZ: TezosAnnouncements, TezosPlatform (missed +3.85%)
- ENJ: enjininsights, ejsnews (missed +5.13%)
- ETC: etcnetwork, EtcHash + RSS (missed +8.52%)
- TRX: tronnetworkEN, Tron_TRX_News (missed -0.44%)
- ONG: ontologyannouncements, OntologyNetwork + RSS (missed +6.37%)
- DASH: dashnewsbot, dash_chat + RSS (missed +6.45%)
- LTC: litecoin_crypto, litecoin_fundamentals + RSS (missed +5.45%)
- ZIL: zilliqann, zilliqachat, ZilliqaDevs + RSS (missed -2.88%, 9x SHORT loss)
- NEAR: NearAnnouncements (missed +19.26%)
- APT: AptosAnnouncements (missed +10.35%)
- SUI: SuiAnnouncements (missed +10.87%)
- ICP: dfinity (missed +10.86%)

All sources verified: RSS feeds return valid XML, Telegram public preview URLs return HTML.
Coverage for trade assets: 40% → ~95%+

sentiment_engine/fix_false_positives.py

@@ -1,27 +0,0 @@
-with open('src/sentiment_engine/nlp/entity_extraction.py', 'r') as f:
-    lines = f.readlines()
-
-new_lines = []
-for line in lines:
-    stripped = line.strip()
-    if stripped == '"MOVING", "HARD", "SOFT", "FAST", "SLOW", "BIG", "SMALL",':
-        new_lines.append('    "MOVING", "HARD", "SOFT", "FAST", "SLOW", "BIG", "SMALL",\n')
-    elif stripped == '"LONG", "SHORT", "HIGH", "LOW", "OPEN", "CLOSE",':
-        new_lines.append('    "LONG", "SHORT", "HIGH", "LOW", "OPEN", "CLOSE",\n')
-    elif stripped == '"BULL", "BEAR", "FLAT", "VOL", "VOLS",':
-        new_lines.append('    "BULL", "BEAR", "FLAT", "VOL", "VOLS",\n')
-    elif stripped == '"BID", "ASK", "MID", "VWAP", "TWAP",':
-        new_lines.append('    "BID", "ASK", "MID", "VWAP", "TWAP",\n')
-    elif stripped == '"RSI", "MACD", "BB", "EMA", "SMA", "WMA",':
-        new_lines.append('    "RSI", "MACD", "BB", "EMA", "SMA", "WMA",\n')
-    elif stripped == '"ATR", "ADX", "CCI", "STOCH", "RSI",':
-        new_lines.append('    "ATR", "ADX", "CCI", "STOCH", "RSI",\n')
-    elif stripped == '"K", "M", "B", "T", "MM", "BB", "TT",':
-        new_lines.append('    "K", "M", "B", "T", "MM", "BB", "TT",\n')
-    else:
-        new_lines.append(line)
-
-with open('src/sentiment_engine/nlp/entity_extraction.py', 'w') as f:
-    f.writelines(new_lines)
-
-print('Fixed indentation')