feat(sentiment): search crypto communities on Reddit for a crypto ticker (#1461)

- a crypto pair is searched in r/CryptoCurrency and r/CryptoMarkets, plus r/Bitcoin, r/ethereum or r/solana for those coins, still in one request
- the Sentiment Analyst's prompt names the subreddits actually searched

tests/test_reddit_fallback.py

@@ -311,3 +311,25 @@ def test_an_unavailable_screen_keeps_every_post_and_says_so():
         screened = reddit.fetch_reddit_posts("NVDA", subreddits=("a", "b"), screen=unavailable)
         plain = reddit.fetch_reddit_posts("NVDA", subreddits=("a", "b"))
     assert screened == "<Jev screening unavailable (HTTP 529); posts are unscreened>\n\n" + plain
+
+
+@pytest.mark.unit
+@pytest.mark.parametrize("ticker, expected", [
+    ("NVDA", ("wallstreetbets", "stocks", "investing")),
+    ("BTC-USD", ("Bitcoin", "CryptoCurrency", "CryptoMarkets")),
+    ("ETH-USD", ("ethereum", "CryptoCurrency", "CryptoMarkets")),
+    ("SOL-USD", ("solana", "CryptoCurrency", "CryptoMarkets")),
+    ("DOGE-USD", ("CryptoCurrency", "CryptoMarkets")),
+])
+def test_a_crypto_ticker_is_searched_in_crypto_communities(ticker, expected):
+    assert reddit.subreddits_for(ticker) == expected
+
+
+@pytest.mark.unit
+def test_a_crypto_pair_is_fetched_from_its_communities_in_one_request():
+    with patch.object(reddit, "_fetch_subreddit_rss", return_value=[]) as fetch:
+        out = reddit.fetch_reddit_posts("ETH-USD")
+    fetch.assert_called_once()
+    query, subs = fetch.call_args.args[:2]
+    assert (query, subs) == ("ETH", "ethereum+CryptoCurrency+CryptoMarkets")
+    assert "r/wallstreetbets" not in out

tests/test_structured_agent_prompts.py

@@ -146,3 +146,23 @@ def test_constraint_text_is_unambiguous():
     # No template braces: it is embedded in ChatPromptTemplate strings, where
     # braces would be parsed as input variables.
     assert "{" not in NO_EXTERNAL_TOOLS and "}" not in NO_EXTERNAL_TOOLS
+
+
+@pytest.mark.unit
+def test_the_sentiment_prompt_names_the_subreddits_searched(monkeypatch):
+    from tradingagents.agents.schemas import SentimentBand, SentimentReport
+
+    monkeypatch.setattr(sentiment, "fetch_stocktwits_messages", lambda *a, **k: "st")
+    monkeypatch.setattr(sentiment, "fetch_reddit_posts", lambda *a, **k: "rd")
+    monkeypatch.setattr(sentiment.get_news, "func", lambda *a, **k: "news", raising=False)
+    captured = {}
+    llm = _capturing_llm(captured, SentimentReport(
+        overall_band=SentimentBand.NEUTRAL, overall_score=5.0, confidence="low", narrative="n",
+    ))
+    sentiment.create_sentiment_analyst(llm)({
+        "company_of_interest": "BTC-USD", "trade_date": "2026-01-15",
+        "asset_type": "crypto", "messages": [],
+    })
+    text = _prompt_text(captured["prompt"])
+    assert "r/Bitcoin, r/CryptoCurrency, r/CryptoMarkets" in text
+    assert "wallstreetbets" not in text

tradingagents/agents/analysts/sentiment_analyst.py

@@ -5,7 +5,7 @@ prompt, so the model reports on data it was given rather than inventing posts:
 
   1. News headlines: Yahoo Finance
   2. StockTwits messages: the cashtag stream, with Bullish/Bearish tags
-  3. Reddit posts: r/wallstreetbets, r/stocks, r/investing
+  3. Reddit posts: r/wallstreetbets, r/stocks, r/investing, or crypto communities for a crypto pair
 
 Each source is trimmed to the analysis window. With a TypeSafe key, the social
 posts are screened by Jev first (see post_screen). These feeds serve recent items
@@ -31,7 +31,12 @@ from tradingagents.agents.structured import (
     invoke_structured_or_freetext,
 )
 from tradingagents.agents.tools import get_news
-from tradingagents.dataflows.vendors.reddit import fetch_reddit_posts
+from tradingagents.dataflows.vendors.reddit import (
+    CRYPTO_SUBREDDITS,
+    DEFAULT_SUBREDDITS,
+    fetch_reddit_posts,
+    subreddits_for,
+)
 from tradingagents.dataflows.vendors.stocktwits import fetch_stocktwits_messages
 
 
@@ -65,7 +70,10 @@ def create_sentiment_analyst(llm):
         stocktwits_block = fetch_stocktwits_messages(
             ticker, limit=30, start_date=start_date, end_date=end_date, screen=screen
         )
-        reddit_block = fetch_reddit_posts(ticker, start_date=start_date, end_date=end_date, screen=screen)
+        subreddits = subreddits_for(ticker)
+        reddit_block = fetch_reddit_posts(
+            ticker, subreddits, start_date=start_date, end_date=end_date, screen=screen
+        )
 
         system_message = _build_system_message(
             ticker=ticker,
@@ -74,6 +82,7 @@ def create_sentiment_analyst(llm):
             news_block=news_block,
             stocktwits_block=stocktwits_block,
             reddit_block=reddit_block,
+            subreddits=subreddits,
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -126,8 +135,15 @@ def _build_system_message(
     news_block: str,
     stocktwits_block: str,
     reddit_block: str,
+    subreddits: tuple[str, ...] = DEFAULT_SUBREDDITS,
 ) -> str:
     """Assemble the sentiment-analyst system message with structured data blocks."""
+    if subreddits == DEFAULT_SUBREDDITS:
+        character = "r/wallstreetbets is often contrarian/exuberant; r/stocks more measured; r/investing longer-term"
+    elif len(subreddits) > len(CRYPTO_SUBREDDITS):
+        character = f"r/{subreddits[0]} leans toward the coin's holders; r/CryptoCurrency and r/CryptoMarkets are broader"
+    else:
+        character = "r/CryptoCurrency and r/CryptoMarkets are broad crypto communities"
     return f"""You are a financial market sentiment analyst. Your task is to produce a comprehensive sentiment report for {ticker} covering the period from {start_date} to {end_date}, drawing on three complementary data sources that have already been collected for you.
 
 ## Data sources (pre-fetched, in this prompt)
@@ -146,8 +162,8 @@ Fast-moving signal. Each message carries a user-labeled sentiment tag (Bullish /
 {stocktwits_block}
 <end_of_stocktwits>
 
-### Reddit posts — r/wallstreetbets, r/stocks, r/investing (past 7 days)
-Community discussion, without vote or comment counts. Subreddit character matters (r/wallstreetbets is often contrarian/exuberant; r/stocks more measured; r/investing longer-term).
+### Reddit posts — {", ".join(f"r/{sub}" for sub in subreddits)} (past 7 days)
+Community discussion, without vote or comment counts. Subreddit character matters ({character}).
 
 <start_of_reddit>
 {reddit_block}

tradingagents/dataflows/vendors/reddit.py

@@ -78,6 +78,20 @@ _ATOM_NS = {"atom": "http://www.w3.org/2005/Atom"}
 # investing trend more measured. Caller can override.
 DEFAULT_SUBREDDITS = ("wallstreetbets", "stocks", "investing")
 
+# Crypto is discussed in crypto communities, where a coin's own subreddit carries
+# most of it; the stock subreddits above barely mention it.
+CRYPTO_SUBREDDITS = ("CryptoCurrency", "CryptoMarkets")
+_COIN_SUBREDDITS = {"BTC": "Bitcoin", "ETH": "ethereum", "SOL": "solana"}
+
+
+def subreddits_for(ticker: str) -> tuple[str, ...]:
+    """The subreddits searched for ``ticker``: crypto communities for a crypto pair."""
+    base = crypto_base(ticker)
+    if not base:
+        return DEFAULT_SUBREDDITS
+    coin = _COIN_SUBREDDITS.get(base)
+    return ((coin,) if coin else ()) + CRYPTO_SUBREDDITS
+
 # Reddit's maximum page size. A week of posts for a ticker across the default
 # subreddits fits well inside one page, which keeps a high-volume subreddit from
 # crowding the others out of a combined search.
@@ -233,7 +247,7 @@ def _fetch_subreddit_rss(
 
 def fetch_reddit_posts(
     ticker: str,
-    subreddits: Iterable[str] = DEFAULT_SUBREDDITS,
+    subreddits: Iterable[str] | None = None,
     *,
     limit_per_sub: int = 5,
     timeout: float = 10.0,
@@ -242,7 +256,8 @@ def fetch_reddit_posts(
     screen=None,
 ) -> str:
     """Fetch recent Reddit posts mentioning ``ticker`` across finance
-    subreddits and return them as a formatted plaintext block.
+    subreddits (by default ``subreddits_for(ticker)``) and return them as a
+    formatted plaintext block.
 
     All subreddits are searched in one combined feed (``r/a+b+c``): anonymous
     RSS allows about one request per minute per IP, so a request per subreddit
@@ -257,10 +272,10 @@ def fetch_reddit_posts(
     flag per post and a note line that heads the block. It runs before the
     per-subreddit cut, so the posts it keeps fill the slots.
     """
+    subreddits = list(subreddits or subreddits_for(ticker))
     # Crypto reaches us as a Yahoo pair (BTC-USD); search Reddit for the base
     # ("BTC") so the query actually matches discussion instead of near-nothing.
     ticker = crypto_base(ticker) or ticker
-    subreddits = list(subreddits)
     label = ", ".join(f"r/{s}" for s in subreddits)
     fetched = _fetch_subreddit_rss(ticker, "+".join(subreddits), _FEED_PAGE, timeout)
     if fetched is None: