mirror of
https://github.com/TauricResearch/TradingAgents.git
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622f99d28a
- prompt advertised get_news(query, ...) but the tool takes a ticker, so the model hallucinated free-text query calls - advertise get_news(ticker, start_date, end_date) #1116
70 lines
3.5 KiB
Python
70 lines
3.5 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.agent_utils import (
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get_global_news,
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get_instrument_context_from_state,
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get_language_instruction,
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get_macro_indicators,
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get_news,
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get_prediction_markets,
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)
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def create_news_analyst(llm):
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def news_analyst_node(state):
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current_date = state["trade_date"]
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asset_type = state.get("asset_type", "stock")
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asset_label = "company" if asset_type == "stock" else "asset"
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instrument_context = get_instrument_context_from_state(state)
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tools = [
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get_news,
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get_global_news,
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get_macro_indicators,
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get_prediction_markets,
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]
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system_message = (
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f"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(ticker, start_date, end_date) for {asset_label}-specific news by ticker symbol, get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news, get_macro_indicators(indicator, curr_date, look_back_days) to ground macro commentary in actual data from FRED (e.g. 'cpi', 'core_pce', 'unemployment', 'fed_funds_rate', '10y_treasury', 'yield_curve'), and get_prediction_markets(topic, limit) for live market-implied probabilities of forward-looking events (e.g. 'Fed rate cut', 'recession 2026', geopolitical or sector events). Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
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+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
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+ get_language_instruction()
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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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" Use the provided tools to progress towards answering the question."
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" If you are unable to fully answer, that's OK; another assistant with different tools"
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" will help where you left off. Execute what you can to make progress."
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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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" You have access to the following tools: {tool_names}."
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" Today's date is {current_date}; treat it as 'now' for all analysis and tool-call date ranges. {instrument_context}\n"
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"{system_message}",
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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(tool_names=", ".join([tool.name for tool in tools]))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(instrument_context=instrument_context)
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chain = prompt | llm.bind_tools(tools)
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result = chain.invoke(state["messages"])
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report = ""
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if len(result.tool_calls) == 0:
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report = result.content
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return {
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"messages": [result],
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"news_report": report,
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}
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return news_analyst_node
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