76 lines
3.2 KiB
Python
76 lines
3.2 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.agent_utils import (
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build_fundamentals_context,
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build_instrument_context,
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get_balance_sheet,
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get_cashflow,
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get_fundamentals,
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get_income_statement,
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get_insider_transactions,
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get_language_instruction,
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)
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from tradingagents.dataflows.config import get_config
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def create_fundamentals_analyst(llm):
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def fundamentals_analyst_node(state):
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current_date = state["trade_date"]
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symbol = state["company_of_interest"]
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instrument_context = build_instrument_context(symbol)
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fundamentals_context = build_fundamentals_context(symbol)
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tools = [
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get_fundamentals,
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get_balance_sheet,
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get_cashflow,
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get_income_statement,
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]
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system_message = (
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"You are a researcher tasked with analyzing fundamental information about the target trading instrument. "
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"Write a comprehensive report that helps traders understand intrinsic value drivers and key risks. "
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"Use as much concrete evidence as possible."
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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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+ " Use the available tools: `get_fundamentals` for overview data, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for financial statement detail when available."
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+ f" {fundamentals_context}"
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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}.\n{system_message}"
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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(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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"fundamentals_report": report,
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}
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return fundamentals_analyst_node
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