100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
|
import time
|
|
import json
|
|
from tradingagents.tools.generator import get_agent_tools
|
|
from tradingagents.dataflows.config import get_config
|
|
|
|
|
|
def create_social_media_analyst(llm):
|
|
def social_media_analyst_node(state):
|
|
current_date = state["trade_date"]
|
|
ticker = state["company_of_interest"]
|
|
company_name = state["company_of_interest"]
|
|
|
|
tools = get_agent_tools("social")
|
|
|
|
system_message = """You are a Social Sentiment Analyst tracking {ticker}'s retail momentum for SHORT-TERM signals.
|
|
|
|
**Analysis Date:** {current_date}
|
|
|
|
## YOUR MISSION
|
|
QUANTIFY social sentiment and identify sentiment SHIFTS that could drive short-term price action.
|
|
|
|
## SENTIMENT TRACKING
|
|
**Measure:**
|
|
- Volume: Mention count (trend: up/down?)
|
|
- Sentiment: Bullish/Neutral/Bearish %
|
|
- Change: Improving or deteriorating?
|
|
- Quality: Data-backed or speculation?
|
|
|
|
## OUTPUT STRUCTURE (MANDATORY)
|
|
|
|
### Sentiment Summary
|
|
- **Current:** [Strongly Bullish/Bullish/Neutral/Bearish/Strongly Bearish]
|
|
- **Trend:** [Improving/Stable/Deteriorating]
|
|
- **Volume:** [Surging/Stable/Declining]
|
|
- **Quality:** [High/Med/Low] (data vs hype)
|
|
|
|
### Sentiment Timeline
|
|
| Date | Sentiment | Volume | Driver | Change |
|
|
|------|-----------|--------|--------|--------|
|
|
| Dec 3 | Bullish 70% | 1.2K posts | Earnings | +20% |
|
|
| Dec 4 | Mixed 50% | 800 posts | Selloff | -20% |
|
|
|
|
### Key Themes (Top 3-4)
|
|
- **Theme:** [E.g., "Earnings beat"]
|
|
- **Prevalence:** [40% of mentions]
|
|
- **Quality:** [Data-backed/Speculation]
|
|
- **Impact:** [Short-term implication]
|
|
|
|
### Trading Implications
|
|
- **Retail Flow:** [Buying/Selling/Mixed]
|
|
- **Momentum:** [Building/Fading]
|
|
- **Contrarian Signal:** [Extreme = reversal?]
|
|
|
|
## QUANTIFICATION RULES
|
|
- ✅ Use %: "70% bullish, 20% neutral"
|
|
- ✅ Show changes: "Improved from 45% to 70%"
|
|
- ✅ Count volume: "Mentions up 300%"
|
|
- ❌ Don't use vague "positive sentiment"
|
|
|
|
Date: {current_date} | Ticker: {ticker}"""
|
|
|
|
prompt = ChatPromptTemplate.from_messages(
|
|
[
|
|
(
|
|
"system",
|
|
"You are a helpful AI assistant, collaborating with other assistants."
|
|
" Use the provided tools to progress towards answering the question."
|
|
" If you are unable to fully answer, that's OK; another assistant with different tools"
|
|
" will help where you left off. Execute what you can to make progress."
|
|
" 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."
|
|
" You have access to the following tools: {tool_names}.\n{system_message}"
|
|
"For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}",
|
|
),
|
|
MessagesPlaceholder(variable_name="messages"),
|
|
]
|
|
)
|
|
|
|
prompt = prompt.partial(system_message=system_message)
|
|
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
|
|
prompt = prompt.partial(current_date=current_date)
|
|
prompt = prompt.partial(ticker=ticker)
|
|
|
|
chain = prompt | llm.bind_tools(tools)
|
|
|
|
result = chain.invoke(state["messages"])
|
|
|
|
report = ""
|
|
|
|
if len(result.tool_calls) == 0:
|
|
report = result.content
|
|
|
|
return {
|
|
"messages": [result],
|
|
"sentiment_report": report,
|
|
}
|
|
|
|
return social_media_analyst_node
|