feat: FX Multi-Agent Validator (TradingAgents-inspired) - Session/Macro/Bull-Bear/Trader

2026-07-27 23:47:46 +00:00 · 2026-03-22 21:57:03 +01:00
parent 27b2c82d55
commit 3b21238590
9 changed files with 451 additions and 0 deletions
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+from .fx_graph import validate_factor, create_fx_validator
+
+__all__ = ["validate_factor", "create_fx_validator"]
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+"""
+FX Macro Analyst — analysiert makroökonomische FX-Faktoren
+statt Aktien-Fundamentals
+"""
+import yfinance as yf
+import pandas as pd
+from datetime import datetime, timedelta
+
+
+def get_fx_macro_data() -> str:
+    """Holt DXY, EUR/USD, Volatilität als Makro-Kontext"""
+    try:
+        end = datetime.now()
+        start = end - timedelta(days=5)
+
+        eurusd = yf.download("EURUSD=X", start=start, end=end, interval="1h", progress=False)
+        dxy = yf.download("DX-Y.NYB", start=start, end=end, interval="1h", progress=False)
+
+        eurusd_last = eurusd['Close'].iloc[-1] if not eurusd.empty else "N/A"
+        eurusd_change = ((eurusd['Close'].iloc[-1] / eurusd['Close'].iloc[-24]) - 1) * 100 if len(eurusd) > 24 else "N/A"
+        dxy_last = dxy['Close'].iloc[-1] if not dxy.empty else "N/A"
+
+        # Realized volatility (24h)
+        if len(eurusd) > 1:
+            returns = eurusd['Close'].pct_change().dropna()
+            vol_24h = returns.tail(24).std() * (24**0.5) * 100
+        else:
+            vol_24h = "N/A"
+
+        return f"""
+EURUSD Current: {eurusd_last:.5f if isinstance(eurusd_last, float) else eurusd_last}
+EURUSD 24h Change: {eurusd_change:.3f}% if isinstance(eurusd_change, float) else eurusd_change}
+DXY (Dollar Index): {dxy_last:.2f if isinstance(dxy_last, float) else dxy_last}
+Realized Volatility 24h: {vol_24h:.4f}% if isinstance(vol_24h, float) else vol_24h}
+"""
+    except Exception as e:
+        return f"Macro data unavailable: {e}"
+
+
+def create_macro_analyst(llm):
+    def macro_analyst_node(state):
+        factor_report = state.get("factor_report", "")
+        current_date = state.get("trade_date", "")
+
+        macro_data = get_fx_macro_data()
+
+        prompt = f"""You are an FX Macro Analyst specialized in EURUSD trading.
+
+Current Date: {current_date}
+
+Live Macro Data:
+{macro_data}
+
+Factor Report from Predix RD-Agent:
+{factor_report}
+
+Analyze the macro environment and its impact on the proposed factor:
+1. DXY trend and correlation with EURUSD
+2. Current volatility regime (low/normal/high) and what it means for the factor
+3. Any macro risks that could invalidate the factor signal
+4. Recommended position sizing given current volatility
+
+End with a markdown table of key macro indicators and their signal implications.
+"""
+        response = llm.invoke(prompt)
+        report = response.content if hasattr(response, 'content') else str(response)
+
+        return {
+            "messages": [response],
+            "macro_report": report,
+        }
+
+    return macro_analyst_node
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+"""
+Session Analyst — analysiert welche FX Session gerade aktiv ist
+und gibt Momentum vs Mean-Reversion Empfehlung
+"""
+from datetime import datetime, timezone
+import pandas as pd
+
+
+def create_session_analyst(llm):
+    def session_analyst_node(state):
+        factor_report = state.get("factor_report", "")
+        current_date = state.get("trade_date", datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M"))
+
+        # Aktuelle UTC Stunde bestimmen
+        try:
+            dt = datetime.fromisoformat(current_date.replace("Z", "+00:00"))
+            hour_utc = dt.hour
+        except Exception:
+            hour_utc = datetime.now(timezone.utc).hour
+
+        # Session bestimmen
+        if 0 <= hour_utc < 8:
+            session = "Asian"
+            regime = "Mean Reversion"
+            session_note = "Low volume, ranging market. Mean reversion factors perform better. Avoid momentum strategies."
+        elif 8 <= hour_utc < 13:
+            session = "London"
+            regime = "Momentum/Trending"
+            session_note = "High volume, trending market. Momentum factors perform better. London open breakouts common."
+        elif 13 <= hour_utc < 16:
+            session = "London-NY Overlap"
+            regime = "Strong Momentum"
+            session_note = "Highest volume of the day. Strong directional moves. Best session for momentum factors."
+        elif 16 <= hour_utc < 21:
+            session = "NY"
+            regime = "Momentum/Reversal"
+            session_note = "Moderate volume. Watch for reversals after London close at 16:00."
+        else:
+            session = "After Hours"
+            regime = "Low Liquidity"
+            session_note = "Very low volume. Spreads widen. Avoid trading."
+
+        prompt = f"""You are an FX Session Analyst specialized in EURUSD intraday dynamics.
+
+Current UTC time: {current_date}
+Active Session: {session}
+Expected Regime: {regime}
+Session Notes: {session_note}
+
+Factor Report from Predix RD-Agent:
+{factor_report}
+
+Analyze whether the proposed factor is suitable for the current session regime.
+Provide a detailed report covering:
+1. Session characteristics and expected price behavior
+2. Whether the factor aligns with the current session regime
+3. Recommended adjustments if needed (e.g., apply only during London hours)
+4. Risk considerations for current session
+
+End with a markdown table summarizing your findings.
+"""
+        response = llm.invoke(prompt)
+        report = response.content if hasattr(response, 'content') else str(response)
+
+        return {
+            "messages": [response],
+            "session_report": report,
+            "current_session": session,
+            "current_regime": regime,
+        }
+
+    return session_analyst_node
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+"""
+Bear FX Researcher — argumentiert GEGEN den vorgeschlagenen Faktor
+"""
+
+
+def create_fx_bear_researcher(llm):
+    def bear_node(state):
+        debate_state = state.get("fx_debate_state", {})
+        history = debate_state.get("history", "")
+        current_response = debate_state.get("current_response", "")
+
+        factor_report = state.get("factor_report", "")
+        session_report = state.get("session_report", "")
+        macro_report = state.get("macro_report", "")
+
+        prompt = f"""You are a Bear FX Analyst arguing AGAINST using this trading factor on EURUSD.
+
+Factor Analysis: {factor_report}
+Session Analysis: {session_report}
+Macro Analysis: {macro_report}
+Debate History: {history}
+Bull's Last Argument: {current_response}
+
+Build a critical case AGAINST this factor:
+1. Overfitting risk — does IC hold out-of-sample?
+2. Spread cost impact — does 1.5 bps erode the edge?
+3. Session limitations — does the factor fail in Asian session?
+4. Macro regime risk — does the factor break in high-volatility environments?
+5. Counter the bull's specific claims with data
+
+Be specific and critical. Challenge every assumption.
+"""
+        response = llm.invoke(prompt)
+        argument = f"Bear Analyst: {response.content if hasattr(response, 'content') else str(response)}"
+
+        new_debate_state = {
+            "history": history + "\n" + argument,
+            "bear_history": debate_state.get("bear_history", "") + "\n" + argument,
+            "bull_history": debate_state.get("bull_history", ""),
+            "current_response": argument,
+            "count": debate_state.get("count", 0) + 1,
+        }
+
+        return {"fx_debate_state": new_debate_state}
+
+    return bear_node
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+"""
+Bull FX Researcher — argumentiert FÜR den vorgeschlagenen Faktor
+"""
+
+
+def create_fx_bull_researcher(llm):
+    def bull_node(state):
+        debate_state = state.get("fx_debate_state", {})
+        history = debate_state.get("history", "")
+        current_response = debate_state.get("current_response", "")
+
+        factor_report = state.get("factor_report", "")
+        session_report = state.get("session_report", "")
+        macro_report = state.get("macro_report", "")
+
+        prompt = f"""You are a Bull FX Analyst advocating FOR using this trading factor on EURUSD.
+
+Factor Analysis: {factor_report}
+Session Analysis: {session_report}
+Macro Analysis: {macro_report}
+Debate History: {history}
+Bear's Last Argument: {current_response}
+
+Build a strong evidence-based case FOR this factor:
+1. Why the IC and ARR justify using this factor
+2. How session timing makes this factor especially effective
+3. Why spread costs are manageable given the signal strength
+4. Counter the bear's specific concerns with data
+
+Be specific, use numbers from the reports. Engage directly with bear arguments.
+"""
+        response = llm.invoke(prompt)
+        argument = f"Bull Analyst: {response.content if hasattr(response, 'content') else str(response)}"
+
+        new_debate_state = {
+            "history": history + "\n" + argument,
+            "bull_history": debate_state.get("bull_history", "") + "\n" + argument,
+            "bear_history": debate_state.get("bear_history", ""),
+            "current_response": argument,
+            "count": debate_state.get("count", 0) + 1,
+        }
+
+        return {"fx_debate_state": new_debate_state}
+
+    return bull_node
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+"""
+FX Trader — trifft finale BUY/HOLD/SELL Entscheidung
+basierend auf allen Analyst- und Researcher-Reports
+"""
+
+
+def create_fx_trader(llm):
+    def trader_node(state):
+        factor_report = state.get("factor_report", "")
+        session_report = state.get("session_report", "")
+        macro_report = state.get("macro_report", "")
+        debate_state = state.get("fx_debate_state", {})
+        debate_history = debate_state.get("history", "")
+        risk_report = state.get("risk_report", "")
+
+        prompt = f"""You are an FX Trading Decision Agent for EURUSD 15min intraday trading.
+
+You have received reports from your team:
+
+FACTOR ANALYSIS (Predix RD-Agent):
+{factor_report}
+
+SESSION ANALYSIS:
+{session_report}
+
+MACRO ANALYSIS:
+{macro_report}
+
+BULL vs BEAR DEBATE:
+{debate_history}
+
+RISK ASSESSMENT:
+{risk_report}
+
+Based on all available information, make a final trading decision:
+- APPROVE: Factor is valid, deploy it in the live loop
+- REJECT: Factor has critical flaws, do not deploy
+- CONDITIONAL: Deploy only under specific conditions (specify which)
+
+Consider:
+1. IC > 0.02 threshold
+2. ARR > 9.62% target
+3. Spread costs vs signal strength
+4. Session-specific validity
+5. Macro regime alignment
+
+End your response with exactly one of:
+FINAL DECISION: **APPROVE**
+FINAL DECISION: **REJECT**
+FINAL DECISION: **CONDITIONAL: [conditions]**
+"""
+        response = llm.invoke(prompt)
+        content = response.content if hasattr(response, 'content') else str(response)
+
+        # Decision extrahieren
+        decision = "UNKNOWN"
+        if "FINAL DECISION: **APPROVE**" in content:
+            decision = "APPROVE"
+        elif "FINAL DECISION: **REJECT**" in content:
+            decision = "REJECT"
+        elif "FINAL DECISION: **CONDITIONAL" in content:
+            decision = "CONDITIONAL"
+
+        return {
+            "messages": [response],
+            "trader_decision": content,
+            "final_decision": decision,
+        }
+
+    return trader_node
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+"""
+FX Validator Configuration
+Angepasst für EURUSD 15min intraday trading
+"""
+import os
+
+FX_CONFIG = {
+    "instrument": "EURUSD=X",
+    "frequency": "15min",
+    "llm_provider": "openai",
+    "backend_url": os.getenv("OPENAI_API_BASE", "http://localhost:8081/v1"),
+    "api_key": os.getenv("OPENAI_API_KEY", "local"),
+    "deep_think_llm": os.getenv("CHAT_MODEL", "openai/qwen3.5-35b"),
+    "quick_think_llm": os.getenv("CHAT_MODEL", "openai/qwen3.5-35b"),
+    "max_debate_rounds": 2,
+    "max_risk_discuss_rounds": 1,
+    # FX-spezifisch
+    "spread_bps": 1.5,
+    "target_arr": 9.62,
+    "max_drawdown": 20.0,
+    "sessions": {
+        "asian":   ("00:00", "08:00"),
+        "london":  ("08:00", "16:00"),
+        "ny":      ("13:00", "21:00"),
+        "overlap": ("13:00", "16:00"),
+    },
+}
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+"""
+FX Validator Graph — Multi-Agent Validierung für Predix Faktoren
+Inspiriert von TradingAgents, angepasst für EURUSD 15min
+"""
+from typing import TypedDict, Optional
+from langgraph.graph import StateGraph, END
+from langchain_openai import ChatOpenAI
+import os
+
+from .agents.analysts.session_analyst import create_session_analyst
+from .agents.analysts.macro_analyst import create_macro_analyst
+from .agents.researchers.bull_researcher import create_fx_bull_researcher
+from .agents.researchers.bear_researcher import create_fx_bear_researcher
+from .agents.trader.fx_trader import create_fx_trader
+from .config import FX_CONFIG
+
+
+class FXValidatorState(TypedDict):
+    factor_report: str
+    trade_date: str
+    session_report: str
+    macro_report: str
+    fx_debate_state: dict
+    risk_report: str
+    trader_decision: str
+    final_decision: str
+    messages: list
+
+
+def create_fx_validator(config: dict = None):
+    cfg = config or FX_CONFIG
+
+    llm = ChatOpenAI(
+        model=cfg["deep_think_llm"].replace("openai/", ""),
+        base_url=cfg["backend_url"],
+        api_key=cfg["api_key"],
+        temperature=0.5,
+    )
+
+    # Agenten erstellen
+    session_analyst = create_session_analyst(llm)
+    macro_analyst = create_macro_analyst(llm)
+    bull_researcher = create_fx_bull_researcher(llm)
+    bear_researcher = create_fx_bear_researcher(llm)
+    fx_trader = create_fx_trader(llm)
+
+    # Debate Loop
+    def should_continue_debate(state):
+        count = state.get("fx_debate_state", {}).get("count", 0)
+        max_rounds = cfg.get("max_debate_rounds", 2) * 2
+        if count >= max_rounds:
+            return "trader"
+        return "bear" if count % 2 == 0 else "bull"
+
+    # Graph bauen
+    graph = StateGraph(FXValidatorState)
+
+    graph.add_node("session_analyst", session_analyst)
+    graph.add_node("macro_analyst", macro_analyst)
+    graph.add_node("bull", bull_researcher)
+    graph.add_node("bear", bear_researcher)
+    graph.add_node("trader", fx_trader)
+
+    graph.set_entry_point("session_analyst")
+    graph.add_edge("session_analyst", "macro_analyst")
+    graph.add_edge("macro_analyst", "bull")
+
+    graph.add_conditional_edges(
+        "bull",
+        should_continue_debate,
+        {"bear": "bear", "bull": "bull", "trader": "trader"}
+    )
+    graph.add_conditional_edges(
+        "bear",
+        should_continue_debate,
+        {"bear": "bear", "bull": "bull", "trader": "trader"}
+    )
+
+    graph.add_edge("trader", END)
+
+    return graph.compile()
+
+
+def validate_factor(factor_report: str, trade_date: str = None) -> dict:
+    """
+    Hauptfunktion — validiert einen Predix-Faktor durch Multi-Agent Debatte
+
+    Args:
+        factor_report: Der Faktor-Report von Predix RD-Agent
+        trade_date: Datum/Zeit in ISO Format (default: jetzt)
+
+    Returns:
+        dict mit final_decision (APPROVE/REJECT/CONDITIONAL) und Reports
+    """
+    from datetime import datetime, timezone
+
+    if trade_date is None:
+        trade_date = datetime.now(timezone.utc).isoformat()
+
+    validator = create_fx_validator()
+
+    initial_state = {
+        "factor_report": factor_report,
+        "trade_date": trade_date,
+        "session_report": "",
+        "macro_report": "",
+        "fx_debate_state": {"history": "", "bull_history": "", "bear_history": "", "current_response": "", "count": 0},
+        "risk_report": "",
+        "trader_decision": "",
+        "final_decision": "",
+        "messages": [],
+    }
+
+    result = validator.invoke(initial_state)
+    return result