Yijia-Xiao
0fcf13624e
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
185 lines
8.3 KiB
Python
185 lines
8.3 KiB
Python
"""Sentiment analyst — multi-source sentiment analysis for a target ticker.
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Previously named ``social_media_analyst``. Renamed and redesigned because
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the old version had a prompt that demanded social-media analysis but the
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only tool available was Yahoo Finance news — which led LLMs to fabricate
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Reddit/X/StockTwits content under prompt pressure (verified live).
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The redesigned agent pre-fetches three complementary data sources before
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the LLM is invoked and injects them into the prompt as structured blocks:
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1. News headlines — Yahoo Finance (institutional framing)
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2. StockTwits messages — retail-trader posts indexed by cashtag, with
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user-labeled Bullish/Bearish sentiment tags
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3. Reddit posts — r/wallstreetbets, r/stocks, r/investing
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The agent does not use tool-calling; the data is in the prompt from
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turn 0. The LLM produces the sentiment report in a single invocation.
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See: https://github.com/TauricResearch/TradingAgents/issues/557
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"""
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from datetime import datetime, timedelta
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.agent_utils import (
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build_instrument_context,
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get_language_instruction,
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get_news,
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)
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from tradingagents.dataflows.reddit import fetch_reddit_posts
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from tradingagents.dataflows.stocktwits import fetch_stocktwits_messages
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def _seven_days_back(trade_date: str) -> str:
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return (datetime.strptime(trade_date, "%Y-%m-%d") - timedelta(days=7)).strftime("%Y-%m-%d")
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def create_sentiment_analyst(llm):
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"""Create a sentiment analyst node for the trading graph.
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Pre-fetches news + StockTwits + Reddit data, injects them into the
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prompt as structured blocks, and produces a sentiment report in a
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single LLM call.
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"""
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def sentiment_analyst_node(state):
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ticker = state["company_of_interest"]
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end_date = state["trade_date"]
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start_date = _seven_days_back(end_date)
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instrument_context = build_instrument_context(ticker)
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# Pre-fetch all three sources. Each fetcher degrades gracefully and
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# returns a string (no exceptions surface from here), so the LLM
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# always sees something — either real data or a clear placeholder.
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news_block = get_news.func(ticker, start_date, end_date)
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stocktwits_block = fetch_stocktwits_messages(ticker, limit=30)
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reddit_block = fetch_reddit_posts(ticker)
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system_message = _build_system_message(
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ticker=ticker,
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start_date=start_date,
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end_date=end_date,
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news_block=news_block,
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stocktwits_block=stocktwits_block,
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reddit_block=reddit_block,
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful AI assistant, collaborating with other assistants."
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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"\n{system_message}\n"
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"For your reference, the current date is {current_date}. {instrument_context}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(current_date=end_date)
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prompt = prompt.partial(instrument_context=instrument_context)
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# No bind_tools — the data is already in the prompt; a single LLM
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# call produces the report directly.
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chain = prompt | llm
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result = chain.invoke(state["messages"])
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return {
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"messages": [result],
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"sentiment_report": result.content,
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}
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return sentiment_analyst_node
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def _build_system_message(
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*,
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ticker: str,
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start_date: str,
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end_date: str,
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news_block: str,
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stocktwits_block: str,
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reddit_block: str,
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) -> str:
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"""Assemble the sentiment-analyst system message with structured data blocks."""
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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.
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## Data sources (pre-fetched, in this prompt)
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### News headlines — Yahoo Finance, past 7 days
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Institutional framing. Fact-driven, slower-moving signal.
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<start_of_news>
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{news_block}
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<end_of_news>
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### StockTwits messages — retail-trader social platform indexed by cashtag
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Fast-moving signal. Each message carries a user-labeled sentiment tag (Bullish / Bearish / no-label) plus the message body.
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<start_of_stocktwits>
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{stocktwits_block}
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<end_of_stocktwits>
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### Reddit posts — r/wallstreetbets, r/stocks, r/investing (past 7 days)
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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).
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<start_of_reddit>
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{reddit_block}
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<end_of_reddit>
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## How to analyze this data (best practices)
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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.
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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).
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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.
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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.
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5. **Identify recurring narrative themes.** What topic keeps coming up across sources? That's the dominant narrative driving current sentiment.
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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.
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7. **Identify catalysts and risks** that emerge across sources — news of upcoming earnings, product launches, competitive threats, macro headlines, etc.
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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.
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## Output
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Produce a sentiment report covering, in order:
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1. **Overall sentiment direction** — Bullish / Bearish / Neutral / Mixed — with a brief confidence note based on data quality and sample size.
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2. **Source-by-source breakdown** — what each of news / StockTwits / Reddit is telling you, with specific evidence (cite message counts, ratios, notable posts).
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3. **Divergences, alignments, and key narratives** across sources.
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4. **Catalysts and risks** surfaced by the data.
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5. **Markdown table** at the end summarizing key sentiment signals, their direction, source, and supporting evidence.
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{get_language_instruction()}"""
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# ---------------------------------------------------------------------------
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# Backwards-compatibility shim
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# ---------------------------------------------------------------------------
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def create_social_media_analyst(llm):
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"""Deprecated alias for :func:`create_sentiment_analyst`.
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Kept so existing code that imports ``create_social_media_analyst``
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continues to work.
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.. deprecated::
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Import :func:`create_sentiment_analyst` directly instead.
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"""
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import warnings
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warnings.warn(
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"create_social_media_analyst is deprecated and will be removed in a "
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"future version. Use create_sentiment_analyst instead.",
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DeprecationWarning,
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stacklevel=2,
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)
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return create_sentiment_analyst(llm)
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