feat(agents): rename to sentiment_analyst; integrate StockTwits + Reddit

Pre-fetches news + StockTwits + Reddit via no-auth public endpoints
and injects structured data blocks into the prompt with professional
analysis instructions. Replaces the prompt-vs-tool mismatch that
caused fabricated social-platform content. Backward-compat alias +
"social" CLI key preserved.

#557 #607

tradingagents/dataflows/reddit.py

@@ -0,0 +1,106 @@
+"""Reddit search fetcher for ticker-specific discussion posts.
+
+Uses Reddit's public JSON endpoints (``reddit.com/r/{sub}/search.json``)
+which do not require an API key. Public throughput is ~10 requests per
+minute per IP, well within budget for a single agent run that queries
+a handful of finance subreddits per ticker.
+
+Returns formatted plaintext blocks ready for prompt injection. Degrades
+gracefully — returns a placeholder string rather than raising, so callers
+never have to special-case missing data.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import time
+from typing import Iterable
+from urllib.error import HTTPError, URLError
+from urllib.parse import urlencode
+from urllib.request import Request, urlopen
+
+logger = logging.getLogger(__name__)
+
+_API = "https://www.reddit.com/r/{sub}/search.json?{qs}"
+_UA = "tradingagents/0.2 (+https://github.com/TauricResearch/TradingAgents)"
+
+# Default subreddits ordered roughly by signal density for ticker-specific
+# discussion. wallstreetbets has the most volume but most noise; stocks /
+# investing trend more measured. Caller can override.
+DEFAULT_SUBREDDITS = ("wallstreetbets", "stocks", "investing")
+
+
+def _fetch_subreddit(
+    ticker: str,
+    sub: str,
+    limit: int,
+    timeout: float,
+) -> list[dict]:
+    qs = urlencode({
+        "q": ticker,
+        "restrict_sr": "on",
+        "sort": "new",
+        "t": "week",  # last 7 days
+        "limit": limit,
+    })
+    url = _API.format(sub=sub, qs=qs)
+    req = Request(url, headers={"User-Agent": _UA, "Accept": "application/json"})
+    try:
+        with urlopen(req, timeout=timeout) as resp:
+            payload = json.loads(resp.read())
+    except (HTTPError, URLError, json.JSONDecodeError, TimeoutError) as exc:
+        logger.warning("Reddit fetch failed for r/%s · %s: %s", sub, ticker, exc)
+        return []
+    children = (payload.get("data") or {}).get("children") or []
+    return [c.get("data", {}) for c in children if isinstance(c, dict)]
+
+
+def fetch_reddit_posts(
+    ticker: str,
+    subreddits: Iterable[str] = DEFAULT_SUBREDDITS,
+    limit_per_sub: int = 5,
+    timeout: float = 10.0,
+    inter_request_delay: float = 0.4,
+) -> str:
+    """Fetch recent Reddit posts mentioning ``ticker`` across finance
+    subreddits and return them as a formatted plaintext block.
+
+    ``inter_request_delay`` keeps us under Reddit's public rate limit
+    (~10 req/min per IP) even if the caller queries many subreddits.
+    """
+    blocks = []
+    total_posts = 0
+    for i, sub in enumerate(subreddits):
+        if i > 0:
+            time.sleep(inter_request_delay)
+        posts = _fetch_subreddit(ticker, sub, limit_per_sub, timeout)
+        total_posts += len(posts)
+        if not posts:
+            blocks.append(f"r/{sub}: <no posts found mentioning {ticker.upper()} in the past 7 days>")
+            continue
+
+        lines = [f"r/{sub} — {len(posts)} recent posts mentioning {ticker.upper()}:"]
+        for p in posts:
+            title = (p.get("title") or "").replace("\n", " ").strip()
+            score = p.get("score", 0)
+            comments = p.get("num_comments", 0)
+            created = p.get("created_utc")
+            created_str = (
+                time.strftime("%Y-%m-%d", time.gmtime(created)) if created else "?"
+            )
+            selftext = (p.get("selftext") or "").replace("\n", " ").strip()
+            if len(selftext) > 240:
+                selftext = selftext[:240] + "…"
+            lines.append(
+                f"  [{created_str} · {score:>4}↑ · {comments:>3}c] {title}"
+                + (f"\n    body excerpt: {selftext}" if selftext else "")
+            )
+        blocks.append("\n".join(lines))
+
+    if total_posts == 0:
+        return (
+            f"<no Reddit posts found mentioning {ticker.upper()} across "
+            f"{', '.join(f'r/{s}' for s in subreddits)} in the past 7 days>"
+        )
+    return "\n\n".join(blocks)