mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 15:37:44 +00:00
TPTBusiness
cbe1c52e00
Rename all source files, scripts, tests, documentation, and configuration from Predix/predix to NexQuant/nexquant across the entire codebase.
121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
"""
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FX Validator Graph — Multi-Agent Validierung für NexQuant Faktoren
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Implementiert Multi-Agenten-System für Trading-Entscheidungen:
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- Session Analyst: Analysiert aktuelle FX-Session
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- Macro Analyst: Bewertet makroökonomische Faktoren
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- Bull/Bear Researchers: Debattieren Long/Short-These
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- FX Trader: Trifft finale Trading-Entscheidung
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"""
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from typing import TypedDict, Optional
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from langgraph.graph import StateGraph, END
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from langchain_openai import ChatOpenAI
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import os
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from .agents.analysts.session_analyst import create_session_analyst
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from .agents.analysts.macro_analyst import create_macro_analyst
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from .agents.researchers.bull_researcher import create_fx_bull_researcher
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from .agents.researchers.bear_researcher import create_fx_bear_researcher
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from .agents.trader.fx_trader import create_fx_trader
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from .config import FX_CONFIG
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class FXValidatorState(TypedDict):
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factor_report: str
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trade_date: str
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session_report: str
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macro_report: str
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fx_debate_state: dict
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risk_report: str
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trader_decision: str
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final_decision: str
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messages: list
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def create_fx_validator(config: dict = None):
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cfg = config or FX_CONFIG
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llm = ChatOpenAI(
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model=cfg["deep_think_llm"].replace("openai/", ""),
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base_url=cfg["backend_url"],
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api_key=cfg["api_key"],
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temperature=0.5,
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)
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# Agenten erstellen
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session_analyst = create_session_analyst(llm)
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macro_analyst = create_macro_analyst(llm)
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bull_researcher = create_fx_bull_researcher(llm)
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bear_researcher = create_fx_bear_researcher(llm)
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fx_trader = create_fx_trader(llm)
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# Debate Loop
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def should_continue_debate(state):
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count = state.get("fx_debate_state", {}).get("count", 0)
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max_rounds = cfg.get("max_debate_rounds", 2) * 2
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if count >= max_rounds:
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return "trader"
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return "bear" if count % 2 == 0 else "bull"
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# Graph bauen
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graph = StateGraph(FXValidatorState)
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graph.add_node("session_analyst", session_analyst)
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graph.add_node("macro_analyst", macro_analyst)
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graph.add_node("bull", bull_researcher)
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graph.add_node("bear", bear_researcher)
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graph.add_node("trader", fx_trader)
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graph.set_entry_point("session_analyst")
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graph.add_edge("session_analyst", "macro_analyst")
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graph.add_edge("macro_analyst", "bull")
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graph.add_conditional_edges(
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"bull",
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should_continue_debate,
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{"bear": "bear", "bull": "bull", "trader": "trader"}
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)
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graph.add_conditional_edges(
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"bear",
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should_continue_debate,
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{"bear": "bear", "bull": "bull", "trader": "trader"}
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)
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graph.add_edge("trader", END)
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return graph.compile()
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def validate_factor(factor_report: str, trade_date: str = None) -> dict:
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"""
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Hauptfunktion — validiert einen NexQuant-Faktor durch Multi-Agent Debatte
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Args:
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factor_report: Der Faktor-Report von NexQuant RD-Agent
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trade_date: Datum/Zeit in ISO Format (default: jetzt)
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Returns:
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dict mit final_decision (APPROVE/REJECT/CONDITIONAL) und Reports
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"""
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from datetime import datetime, timezone
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if trade_date is None:
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trade_date = datetime.now(timezone.utc).isoformat()
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validator = create_fx_validator()
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initial_state = {
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"factor_report": factor_report,
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"trade_date": trade_date,
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"session_report": "",
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"macro_report": "",
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"fx_debate_state": {"history": "", "bull_history": "", "bear_history": "", "current_response": "", "count": 0},
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"risk_report": "",
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"trader_decision": "",
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"final_decision": "",
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"messages": [],
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}
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result = validator.invoke(initial_state)
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return result
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