134 lines
5.5 KiB
Python
134 lines
5.5 KiB
Python
import json
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import re
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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_segment_fundamentals,
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get_segment_income_statement,
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get_segment_news,
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)
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def _extract_segment_payload(report: str) -> dict:
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if not report:
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return {}
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# Prefer fenced JSON payloads (supports ```json, ```JSON, and unlabeled ```).
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for match in re.finditer(
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r"```(?:\s*([A-Za-z]+))?\s*(\{.*?\})\s*```",
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report,
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re.DOTALL,
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):
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label = (match.group(1) or "").strip().lower()
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if label and label != "json":
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continue
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try:
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payload = json.loads(match.group(2))
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except json.JSONDecodeError:
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continue
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if isinstance(payload, dict):
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return payload
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# Fallback: tolerate raw JSON object embedded in body text.
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decoder = json.JSONDecoder()
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for brace_match in re.finditer(r"\{", report):
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candidate = report[brace_match.start() :].lstrip()
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try:
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payload, _ = decoder.raw_decode(candidate)
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except json.JSONDecodeError:
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continue
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if isinstance(payload, dict):
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return payload
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return {}
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def _build_segment_data(ticker: str, analysis_date: str, report: str) -> dict:
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payload = _extract_segment_payload(report)
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business_unit_decomposition = payload.get("business_unit_decomposition", [])
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segment_economics = payload.get("segment_economics", {})
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value_driver_map = payload.get("value_driver_map", [])
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if not isinstance(business_unit_decomposition, list):
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business_unit_decomposition = []
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if not isinstance(segment_economics, dict):
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segment_economics = {}
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if not isinstance(value_driver_map, list):
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value_driver_map = []
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return {
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"ticker": ticker,
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"analysis_date": analysis_date,
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"business_unit_decomposition": business_unit_decomposition,
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"segment_economics": segment_economics,
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"value_driver_map": value_driver_map,
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}
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def create_segment_analyst(llm):
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def segment_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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instrument_context = build_instrument_context(ticker)
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tools = [
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get_segment_fundamentals,
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get_segment_income_statement,
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get_segment_news,
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]
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system_message = (
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"You are a segment analyst focused on business-mix quality and revenue durability. "
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"Use `get_segment_fundamentals` to summarize business lines and strategic positioning, "
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"`get_segment_income_statement` to infer segment-level margin direction from reported trends, "
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"and `get_segment_news` to identify demand, pricing, and competitive catalysts for key segments. "
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"Deliver a concise segment-by-segment view, highlight concentration risks, and append a Markdown "
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"table that maps each major segment to growth outlook, margin trend, and trading implication. "
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"Your response must contain two parts: "
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"(1) a Markdown narrative summary and table, followed by "
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"(2) a fenced JSON block (```json ... ```) with exactly these top-level keys: "
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"`business_unit_decomposition` (list of objects with `segment`, `revenue_share_pct`, "
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"`growth_trend`, `strategic_role`), `segment_economics` (object summarizing margin profile, "
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"capital intensity, cyclicality), and `value_driver_map` (list of objects with `driver`, "
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"`impacted_segments`, `direction`, `horizon`, `evidence`). "
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"If data is unavailable, still include all keys using empty lists/objects."
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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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tool_calls = getattr(result, "tool_calls", None) or []
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report = result.content if len(tool_calls) == 0 else ""
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return {
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"messages": [result],
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"segment_report": report,
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"segment_data": _build_segment_data(ticker, current_date, report),
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}
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return segment_analyst_node
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