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
This commit is contained in:
Yijia-Xiao committed
1 parent
d0dd0420ad
commit
0fcf13624e
6 files changed
+401
-54
No files matched your search
@@ -0,0 +1,184 @@
|
||||
"""Sentiment analyst — multi-source sentiment analysis for a target ticker.
|
||||
|
||||
Previously named ``social_media_analyst``. Renamed and redesigned because
|
||||
the old version had a prompt that demanded social-media analysis but the
|
||||
only tool available was Yahoo Finance news — which led LLMs to fabricate
|
||||
Reddit/X/StockTwits content under prompt pressure (verified live).
|
||||
|
||||
The redesigned agent pre-fetches three complementary data sources before
|
||||
the LLM is invoked and injects them into the prompt as structured blocks:
|
||||
|
||||
1. News headlines — Yahoo Finance (institutional framing)
|
||||
2. StockTwits messages — retail-trader posts indexed by cashtag, with
|
||||
user-labeled Bullish/Bearish sentiment tags
|
||||
3. Reddit posts — r/wallstreetbets, r/stocks, r/investing
|
||||
|
||||
The agent does not use tool-calling; the data is in the prompt from
|
||||
turn 0. The LLM produces the sentiment report in a single invocation.
|
||||
|
||||
See: https://github.com/TauricResearch/TradingAgents/issues/557
|
||||
"""
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
||||
from tradingagents.agents.utils.agent_utils import (
|
||||
build_instrument_context,
|
||||
get_language_instruction,
|
||||
get_news,
|
||||
)
|
||||
from tradingagents.dataflows.reddit import fetch_reddit_posts
|
||||
from tradingagents.dataflows.stocktwits import fetch_stocktwits_messages
|
||||
|
||||
|
||||
def _seven_days_back(trade_date: str) -> str:
|
||||
return (datetime.strptime(trade_date, "%Y-%m-%d") - timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
|
||||
|
||||
def create_sentiment_analyst(llm):
|
||||
"""Create a sentiment analyst node for the trading graph.
|
||||
|
||||
Pre-fetches news + StockTwits + Reddit data, injects them into the
|
||||
prompt as structured blocks, and produces a sentiment report in a
|
||||
single LLM call.
|
||||
"""
|
||||
|
||||
def sentiment_analyst_node(state):
|
||||
ticker = state["company_of_interest"]
|
||||
end_date = state["trade_date"]
|
||||
start_date = _seven_days_back(end_date)
|
||||
instrument_context = build_instrument_context(ticker)
|
||||
|
||||
# Pre-fetch all three sources. Each fetcher degrades gracefully and
|
||||
# returns a string (no exceptions surface from here), so the LLM
|
||||
# always sees something — either real data or a clear placeholder.
|
||||
news_block = get_news.func(ticker, start_date, end_date)
|
||||
stocktwits_block = fetch_stocktwits_messages(ticker, limit=30)
|
||||
reddit_block = fetch_reddit_posts(ticker)
|
||||
|
||||
system_message = _build_system_message(
|
||||
ticker=ticker,
|
||||
start_date=start_date,
|
||||
end_date=end_date,
|
||||
news_block=news_block,
|
||||
stocktwits_block=stocktwits_block,
|
||||
reddit_block=reddit_block,
|
||||
)
|
||||
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
(
|
||||
"system",
|
||||
"You are a helpful AI assistant, collaborating with other assistants."
|
||||
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
|
||||
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
|
||||
"\n{system_message}\n"
|
||||
"For your reference, the current date is {current_date}. {instrument_context}",
|
||||
),
|
||||
MessagesPlaceholder(variable_name="messages"),
|
||||
]
|
||||
)
|
||||
|
||||
prompt = prompt.partial(system_message=system_message)
|
||||
prompt = prompt.partial(current_date=end_date)
|
||||
prompt = prompt.partial(instrument_context=instrument_context)
|
||||
|
||||
# No bind_tools — the data is already in the prompt; a single LLM
|
||||
# call produces the report directly.
|
||||
chain = prompt | llm
|
||||
result = chain.invoke(state["messages"])
|
||||
|
||||
return {
|
||||
"messages": [result],
|
||||
"sentiment_report": result.content,
|
||||
}
|
||||
|
||||
return sentiment_analyst_node
|
||||
|
||||
|
||||
def _build_system_message(
|
||||
*,
|
||||
ticker: str,
|
||||
start_date: str,
|
||||
end_date: str,
|
||||
news_block: str,
|
||||
stocktwits_block: str,
|
||||
reddit_block: str,
|
||||
) -> str:
|
||||
"""Assemble the sentiment-analyst system message with structured data blocks."""
|
||||
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)
|
||||
|
||||
### News headlines — Yahoo Finance, past 7 days
|
||||
Institutional framing. Fact-driven, slower-moving signal.
|
||||
|
||||
<start_of_news>
|
||||
{news_block}
|
||||
<end_of_news>
|
||||
|
||||
### StockTwits messages — retail-trader social platform indexed by cashtag
|
||||
Fast-moving signal. Each message carries a user-labeled sentiment tag (Bullish / Bearish / no-label) plus the message body.
|
||||
|
||||
<start_of_stocktwits>
|
||||
{stocktwits_block}
|
||||
<end_of_stocktwits>
|
||||
|
||||
### Reddit posts — r/wallstreetbets, r/stocks, r/investing (past 7 days)
|
||||
Community discussion. Engagement signal via upvote score and comment count. Subreddit character matters (r/wallstreetbets is often contrarian/exuberant; r/stocks more measured; r/investing longer-term).
|
||||
|
||||
<start_of_reddit>
|
||||
{reddit_block}
|
||||
<end_of_reddit>
|
||||
|
||||
## How to analyze this data (best practices)
|
||||
|
||||
1. **Read the StockTwits Bullish/Bearish ratio as a leading retail-sentiment signal.** A 70/30 bullish/bearish split is moderately bullish; ≥90/10 may indicate over-extension and contrarian risk; 50/50 is uncertainty. Sample size matters — base rates on the actual message count, not percentages alone.
|
||||
|
||||
2. **Look for cross-source divergences.** If news framing is bearish but StockTwits is overwhelmingly bullish, that mismatch is itself a signal — it can mean retail is leaning into a thesis the news flow hasn't caught up to (or vice versa, that retail is chasing while institutions are cautious).
|
||||
|
||||
3. **Weight Reddit posts by engagement.** A 400-upvote / 200-comment thread reflects community attention; a 3-upvote post is noise. Read the body excerpts for context — the title alone often misleads.
|
||||
|
||||
4. **Distinguish opinion from event.** A news headline ("Nvidia announces $500M Corning deal") is an event; a StockTwits post ("buying NVDA, this is going to moon") is opinion. Both are inputs but should be weighted differently in your conclusions.
|
||||
|
||||
5. **Identify recurring narrative themes.** What topic keeps coming up across sources? That's the dominant narrative driving current sentiment.
|
||||
|
||||
6. **Be honest about data limits.** If StockTwits returned only a handful of messages, or one or more sources returned an "<unavailable>" placeholder, the sentiment read is less robust — flag this caveat explicitly. If the sources are silent on a given subreddit, say so.
|
||||
|
||||
7. **Identify catalysts and risks** that emerge across sources — news of upcoming earnings, product launches, competitive threats, macro headlines, etc.
|
||||
|
||||
8. **Past sentiment is not predictive.** Frame your conclusions as signal for the trader to weigh alongside fundamentals and technicals, not as a price call.
|
||||
|
||||
## Output
|
||||
|
||||
Produce a sentiment report covering, in order:
|
||||
|
||||
1. **Overall sentiment direction** — Bullish / Bearish / Neutral / Mixed — with a brief confidence note based on data quality and sample size.
|
||||
2. **Source-by-source breakdown** — what each of news / StockTwits / Reddit is telling you, with specific evidence (cite message counts, ratios, notable posts).
|
||||
3. **Divergences, alignments, and key narratives** across sources.
|
||||
4. **Catalysts and risks** surfaced by the data.
|
||||
5. **Markdown table** at the end summarizing key sentiment signals, their direction, source, and supporting evidence.
|
||||
|
||||
{get_language_instruction()}"""
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Backwards-compatibility shim
|
||||
# ---------------------------------------------------------------------------
|
||||
def create_social_media_analyst(llm):
|
||||
"""Deprecated alias for :func:`create_sentiment_analyst`.
|
||||
|
||||
Kept so existing code that imports ``create_social_media_analyst``
|
||||
continues to work.
|
||||
|
||||
.. deprecated::
|
||||
Import :func:`create_sentiment_analyst` directly instead.
|
||||
"""
|
||||
import warnings
|
||||
warnings.warn(
|
||||
"create_social_media_analyst is deprecated and will be removed in a "
|
||||
"future version. Use create_sentiment_analyst instead.",
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
return create_sentiment_analyst(llm)
|
||||
Reference in new issue
Block a user