95 lines
3.8 KiB
Python
95 lines
3.8 KiB
Python
import json
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import logging
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from tradingagents.agents.utils.json_utils import extract_json
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logger = logging.getLogger(__name__)
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def create_macro_synthesis(llm):
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def macro_synthesis_node(state):
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scan_date = state["scan_date"]
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# Inject all previous reports for synthesis — no tools, pure LLM reasoning
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all_reports_context = f"""## All Scanner and Research Reports
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### Geopolitical Report:
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{state.get("geopolitical_report", "Not available")}
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### Market Movers Report:
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{state.get("market_movers_report", "Not available")}
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### Sector Performance Report:
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{state.get("sector_performance_report", "Not available")}
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### Industry Deep Dive Report:
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{state.get("industry_deep_dive_report", "Not available")}
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"""
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system_message = (
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"You are a macro strategist synthesizing all scanner and research reports into a final investment thesis. "
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"You have received: geopolitical analysis, market movers analysis, sector performance analysis, "
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"and industry deep dive analysis. "
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"Synthesize these into a structured output with: "
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"(1) Executive summary of the macro environment, "
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"(2) Top macro themes with conviction levels, "
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"(3) A list of 8-10 specific stocks worth investigating with ticker, name, sector, rationale, "
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"thesis_angle (growth/value/catalyst/turnaround/defensive/momentum), conviction (high/medium/low), "
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"key_catalysts, and risks. "
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"Output your response as valid JSON matching this schema:\n"
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"{\n"
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' "timeframe": "1 month",\n'
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' "executive_summary": "...",\n'
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' "macro_context": { "economic_cycle": "...", "central_bank_stance": "...", "geopolitical_risks": [...] },\n'
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' "key_themes": [{ "theme": "...", "description": "...", "conviction": "high|medium|low", "timeframe": "..." }],\n'
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' "stocks_to_investigate": [{ "ticker": "...", "name": "...", "sector": "...", "rationale": "...", '
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'"thesis_angle": "...", "conviction": "high|medium|low", "key_catalysts": [...], "risks": [...] }],\n'
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' "risk_factors": ["..."]\n'
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"}"
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"\n\nIMPORTANT: Output ONLY valid JSON. Start your response with '{' and end with '}'. "
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"Do NOT use markdown code fences. Do NOT include any explanation or preamble before or after the JSON."
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f"\n\n{all_reports_context}"
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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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" 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}.",
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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="none")
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prompt = prompt.partial(current_date=scan_date)
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chain = prompt | llm
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result = chain.invoke(state["messages"])
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report = result.content
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# Sanitize LLM output: strip markdown fences / <think> blocks before storing
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try:
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parsed = extract_json(report)
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report = json.dumps(parsed)
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except (ValueError, json.JSONDecodeError):
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logger.warning(
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"macro_synthesis: could not extract JSON from LLM output; "
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"storing raw content (first 200 chars): %s",
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report[:200],
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)
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return {
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"messages": [result],
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"macro_scan_summary": report,
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"sender": "macro_synthesis",
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}
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return macro_synthesis_node
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