mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
52d2b89148
Add automatic dashboard launch options for trading loop: 1. CLI integration (rdagent/app/cli.py) - --with-dashboard/-d flag for web dashboard - --cli-dashboard/-c flag for terminal UI - --dashboard-port for custom port configuration - Automatic background process spawning 2. Dashboard auto-start - Web dashboard launches in background thread - CLI dashboard opens in separate terminal window - Graceful startup with 2-second delay 3. Process management - Dashboard runs as daemon thread - Automatic cleanup on main process exit - Error handling for dashboard startup failures 4. Documentation - Updated help text with examples - Usage instructions in README - Dashboard URLs displayed on startup Usage examples: rdagent fin_quant -d # Web dashboard rdagent fin_quant -c # CLI dashboard rdagent fin_quant -d -c # Both dashboards rdagent fin_quant -d --port 5001 # Custom port
579 lines
20 KiB
Python
579 lines
20 KiB
Python
"""
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EURUSD Macro Agent (Stanley Druckenmiller Stil)
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Inspiriert von: ai-hedge-fund/src/agents/stanley_druckenmiller.py
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Makro-Fokus für Forex-Trading:
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- Zinsdifferential (Fed vs EZB)
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- Wirtschaftswachstum (BIP, PMI, NFP)
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- Momentum (DXY Trend, EURUSD Trend)
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- Sentiment (COT Report, Risk Sentiment)
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- Asymmetrische Risk-Reward-Analyse
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"""
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import json
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import sys
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Dict, List, Literal, Optional
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import yfinance as yf
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sys.path.insert(0, str(Path(__file__).parent))
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from eurusd_llm import MultiProviderLLM
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from fx_config import get_fx_config
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@dataclass
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class MacroSignal:
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"""Makro-Signal mit Details."""
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action: Literal["LONG", "SHORT", "NEUTRAL"]
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confidence: int # 0-100
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reasoning: List[str]
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# Makro-Faktoren
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rate_differential: float = 0.0 # Fed - EZB Zinsen
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growth_differential: float = 0.0 # US - EU Wachstum
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momentum_score: float = 0.0 # -1 bis +1
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sentiment_score: float = 0.0 # -1 bis +1
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# Live-Daten
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eurusd_price: Optional[float] = None
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dxy_price: Optional[float] = None
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realized_volatility: Optional[float] = None
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eurusd_24h_change: Optional[float] = None
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# Risk-Reward
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expected_return: float = 0.0 # Erwartete Rendite in %
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risk_reward_ratio: float = 1.0 # R/R Verhältnis
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asymmetric_opportunity: bool = False # Gibt es asymmetrische Chance?
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# Trade-Parameter
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entry_price: Optional[float] = None
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stop_loss: Optional[float] = None
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take_profit: Optional[float] = None
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leverage: int = 20
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def get_live_fx_data() -> dict:
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"""
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Holt Live-FX-Daten via yfinance.
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Returns
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-------
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dict
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Live-Daten: EURUSD, DXY, Volatilität, 24h Change
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"""
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try:
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from datetime import datetime, timedelta
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end = datetime.now()
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start = end - timedelta(days=5)
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# EURUSD holen
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eurusd = yf.download("EURUSD=X", start=start, end=end, interval="1h", progress=False)
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# DXY holen (Dollar Index)
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dxy = yf.download("DX-Y.NYB", start=start, end=end, interval="1h", progress=False)
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# EURUSD Daten extrahieren
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if not eurusd.empty:
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eurusd_price = float(eurusd['Close'].iloc[-1])
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# 24h Change (24 Stunden = 24 Candles bei 1h Intervall)
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if len(eurusd) > 24:
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eurusd_24h_change = ((eurusd['Close'].iloc[-1] / eurusd['Close'].iloc[-24]) - 1) * 100
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else:
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eurusd_24h_change = 0.0
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# Realized Volatility (24h annualisiert)
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returns = eurusd['Close'].pct_change().dropna()
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if len(returns) > 1:
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realized_volatility = float(returns.tail(24).std() * (24 ** 0.5) * 100)
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else:
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realized_volatility = 0.0
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else:
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eurusd_price = None
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eurusd_24h_change = None
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realized_volatility = None
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# DXY Daten extrahieren
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if not dxy.empty:
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dxy_price = float(dxy['Close'].iloc[-1])
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else:
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dxy_price = None
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return {
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"eurusd_price": eurusd_price,
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"dxy_price": dxy_price,
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"realized_volatility": realized_volatility,
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"eurusd_24h_change": eurusd_24h_change,
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"success": True
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}
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except Exception as e:
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return {
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"eurusd_price": None,
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"dxy_price": None,
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"realized_volatility": None,
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"eurusd_24h_change": None,
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"success": False,
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"error": str(e)
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}
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class EURUSDMacroAgent:
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"""
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Macro Agent im Stanley Druckenmiller Stil für EURUSD.
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Analysiert makroökonomische Faktoren:
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1. Zinsdifferential (Fed vs EZB)
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2. Wirtschaftswachstum (BIP, PMI, NFP)
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3. Momentum (DXY, EURUSD Trends)
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4. Sentiment (COT, Risk-On/Off)
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5. Asymmetrische Risk-Reward-Analyse
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Druckenmiller-Prinzipien:
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- "It's not whether you're right or wrong, but how much you make when right"
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- Asymmetrische Chancen erkennen (begrenztes Downside, großes Upside)
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- Bei hoher Conviction großen Positionen eingehen
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- Makro-Trends folgen, nicht gegen sie handeln
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"""
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def __init__(self, llm: Optional[MultiProviderLLM] = None):
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self.llm = llm or MultiProviderLLM()
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def analyze(
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self,
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macro_data: dict,
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price_data: Optional[dict] = None,
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use_live_data: bool = True
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) -> MacroSignal:
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"""
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Analysiert makroökonomische Daten für EURUSD.
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Parameters
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----------
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macro_data : dict
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Makrodaten mit Keys:
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- fed_rate: US-Leitzins (%)
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- ecb_rate: EZB-Leitzins (%)
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- us_pmi: US PMI
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- eu_pmi: Eurozone PMI
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- us_gdp_growth: US BIP-Wachstum (%)
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- eu_gdp_growth: EU BIP-Wachstum (%)
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- dxy_trend: DXY Trend ("up", "down", "neutral")
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- risk_sentiment: Risk-On/Off ("risk-on", "risk-off", "neutral")
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- cot_report: COT Report Daten
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price_data : dict, optional
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Preisdaten für Entry/SL/TP Berechnung
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use_live_data : bool, default True
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Wenn True, werden Live-Daten via yfinance geladen
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Returns
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-------
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MacroSignal
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Makro-Signal mit Trading-Empfehlung
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"""
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# 1. Live-Daten holen wenn aktiviert
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live_data = {}
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if use_live_data:
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live_data = get_live_fx_data()
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if live_data.get("success"):
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# Override DXY Trend basierend auf Live-Daten
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if live_data.get("dxy_price"):
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# Einfacher DXY Trend aus letzten Daten
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macro_data["dxy_trend"] = "up" # Wird in get_live_fx_data erweitert
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# 2. Berechne fundamentale Differentiale
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rate_diff = macro_data.get("fed_rate", 5.0) - macro_data.get("ecb_rate", 4.0)
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growth_diff = macro_data.get("us_gdp_growth", 2.0) - macro_data.get("eu_gdp_growth", 1.5)
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pmi_diff = macro_data.get("us_pmi", 50) - macro_data.get("eu_pmi", 50)
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# 3. Berechne Momentum-Score
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dxy_trend = macro_data.get("dxy_trend", "neutral")
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if dxy_trend == "up":
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momentum_score = -0.5 # Starker DXY = schwacher EURUSD
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elif dxy_trend == "down":
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momentum_score = 0.5 # Schwacher DXY = starker EURUSD
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else:
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momentum_score = 0.0
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# 4. Berechne Sentiment-Score
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risk_sentiment = macro_data.get("risk_sentiment", "neutral")
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if risk_sentiment == "risk-on":
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sentiment_score = 0.3 # Risk-On begünstigt EUR
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elif risk_sentiment == "risk-off":
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sentiment_score = -0.3 # Risk-Off begünstigt USD
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else:
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sentiment_score = 0.0
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# 5. LLM-basierte Gesamtanalyse mit Live-Daten
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signal = self._llm_analysis(
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rate_diff=rate_diff,
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growth_diff=growth_diff,
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pmi_diff=pmi_diff,
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momentum_score=momentum_score,
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sentiment_score=sentiment_score,
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macro_data=macro_data,
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price_data=price_data,
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live_data=live_data
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)
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# 6. Füge berechnete Werte hinzu
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signal.rate_differential = rate_diff
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signal.growth_differential = growth_diff
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signal.momentum_score = momentum_score
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signal.sentiment_score = sentiment_score
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# 7. Füge Live-Daten hinzu
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if live_data.get("success"):
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signal.eurusd_price = live_data.get("eurusd_price")
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signal.dxy_price = live_data.get("dxy_price")
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signal.realized_volatility = live_data.get("realized_volatility")
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signal.eurusd_24h_change = live_data.get("eurusd_24h_change")
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return signal
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def _llm_analysis(
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self,
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rate_diff: float,
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growth_diff: float,
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pmi_diff: float,
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momentum_score: float,
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sentiment_score: float,
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macro_data: dict,
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price_data: Optional[dict]
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) -> MacroSignal:
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"""
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LLM-basierte Analyse mit Druckenmiller-Prinzipien.
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"""
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prompt = self._build_macro_prompt(
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rate_diff, growth_diff, pmi_diff,
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momentum_score, sentiment_score,
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macro_data, price_data
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)
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system_prompt = """Du bist ein makroökonomischer Analyst im Stil von Stanley Druckenmiller.
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Deine Aufgabe:
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1. Analysiere makroökonomische Differentiale (Zinsen, Wachstum, PMI)
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2. Bewerte Momentum und Sentiment
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3. Identifiziere asymmetrische Risk-Reward-Chancen
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4. Gib eine klare LONG/SHORT/NEUTRAL Empfehlung
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Druckenmiller-Prinzipien:
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- "It's not whether you're right or wrong, but how much you make when right"
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- Bei hoher Conviction: große Positionen
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- Asymmetrische Chancen suchen (1:3 R/R oder besser)
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- Makro-Trends folgen, nicht gegen sie handeln
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Antworte IMMER im JSON-Format."""
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try:
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response = self.llm.chat(
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prompt=prompt,
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system_prompt=system_prompt,
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temperature=0.1,
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max_tokens=800,
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json_mode=True
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)
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result = json.loads(response["content"])
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return MacroSignal(
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action=result.get("action", "NEUTRAL"),
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confidence=min(100, max(0, result.get("confidence", 50))),
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reasoning=result.get("reasons", []),
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rate_differential=rate_diff,
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growth_differential=growth_diff,
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momentum_score=momentum_score,
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sentiment_score=sentiment_score,
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expected_return=result.get("expected_return", 0.0),
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risk_reward_ratio=result.get("risk_reward_ratio", 1.0),
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asymmetric_opportunity=result.get("asymmetric_opportunity", False),
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entry_price=price_data.get("price") if price_data else None,
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stop_loss=result.get("stop_loss"),
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take_profit=result.get("take_profit"),
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leverage=result.get("leverage", 20)
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)
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except Exception as e:
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# Fallback bei Fehlern
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return MacroSignal(
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action="NEUTRAL",
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confidence=50,
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reasoning=[f"Macro-Analyse fehlgeschlagen: {str(e)}"],
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rate_differential=rate_diff,
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growth_differential=growth_diff,
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momentum_score=momentum_score,
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sentiment_score=sentiment_score,
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expected_return=0.0,
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risk_reward_ratio=1.0,
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asymmetric_opportunity=False
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)
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def _build_macro_prompt(
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self,
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rate_diff: float,
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growth_diff: float,
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pmi_diff: float,
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momentum_score: float,
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sentiment_score: float,
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macro_data: dict,
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price_data: Optional[dict],
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live_data: Optional[dict]
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) -> str:
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"""Erstellt makroökonomischen Prompt."""
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price_str = f"- Aktueller Preis: {price_data.get('price', 'N/A')}\n" if price_data else ""
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# Live-Daten einfügen
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live_str = ""
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if live_data and live_data.get("success"):
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live_str = f"""
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=== Live Markt-Daten (via yfinance) ===
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- EURUSD: {live_data.get('eurusd_price', 'N/A'):.5f}
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- EURUSD 24h Change: {live_data.get('eurusd_24h_change', 0):+.3f}%
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- DXY (Dollar Index): {live_data.get('dxy_price', 'N/A'):.2f}
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- Realized Volatility (24h): {live_data.get('realized_volatility', 0):.4f}%
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"""
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return f"""
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=== EURUSD Macro Analyse (Druckenmiller Stil) ===
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{live_str}
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=== Zinsdifferential ===
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- Fed Rate - EZB Rate: {rate_diff:+.2f}% ({'USD vorteil' if rate_diff > 0 else 'EUR vorteil' if rate_diff < 0 else 'neutral'})
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=== Wirtschaftswachstum ===
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- US vs EU Wachstum: {growth_diff:+.2f}%
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- US vs EU PMI: {pmi_diff:+.1f}
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=== Momentum & Sentiment ===
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- Momentum Score: {momentum_score:+.2f} ({'DXY schwach' if momentum_score > 0 else 'DXY stark' if momentum_score < 0 else 'neutral'})
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- Sentiment Score: {sentiment_score:+.2f} ({'Risk-On' if sentiment_score > 0 else 'Risk-Off' if sentiment_score < 0 else 'neutral'})
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{price_str}
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=== Zusätzliche Informationen ===
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- Wirtschaftsdaten: {macro_data.get('economic_data', 'N/A')}
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- COT Report: {macro_data.get('cot_report', 'N/A')}
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=== Aufgabe ===
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1. Bewerte die makroökonomische Situation
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2. Identifiziere asymmetrische Risk-Reward-Chancen
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3. Gib LONG/SHORT/NEUTRAL Empfehlung mit Confidence
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Antworte als JSON:
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{{
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"action": "LONG" oder "SHORT" oder "NEUTRAL",
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"confidence": 0-100,
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"reasons": ["Grund 1", "Grund 2", ...],
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"expected_return": 0.05, # 5% erwartet
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"risk_reward_ratio": 3.0, # 1:3 R/R
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"asymmetric_opportunity": true/false,
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"stop_loss": 1.0800,
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"take_profit": 1.0950,
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"leverage": 20
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}}
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"""
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class MacroDebateIntegration:
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"""
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Integriert Macro-Agent mit Bull/Bear/Neutral Debatte.
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Der Macro-Agent gibt zusätzliche makroökonomische Perspektive,
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die in die finale Debatte einfließt.
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"""
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def __init__(self, llm: Optional[MultiProviderLLM] = None):
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self.macro_agent = EURUSDMacroAgent(llm)
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def get_macro_perspective(self, macro_data: dict, price_data: dict) -> dict:
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"""
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Gibt makroökonomische Perspektive für Debatte.
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Returns
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-------
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dict
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Macro-Perspektive für Bull/Bear/Neutral Agenten
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"""
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signal = self.macro_agent.analyze(macro_data, price_data)
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return {
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"action": signal.action,
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"confidence": signal.confidence,
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"reasoning": signal.reasoning,
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"macro_factors": {
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"rate_differential": signal.rate_differential,
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"growth_differential": signal.growth_differential,
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"momentum_score": signal.momentum_score,
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"sentiment_score": signal.sentiment_score
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},
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"risk_reward": {
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"expected_return": signal.expected_return,
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"risk_reward_ratio": signal.risk_reward_ratio,
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"asymmetric_opportunity": signal.asymmetric_opportunity
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}
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}
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# Test-Funktion für lokale Validierung
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if __name__ == "__main__":
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print("=== EURUSD Macro Agent Test (Mock Mode) ===\n")
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# Test-Makrodaten
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test_macro_data = {
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"fed_rate": 5.25,
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"ecb_rate": 4.50,
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"us_pmi": 52.5,
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"eu_pmi": 48.2,
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"us_gdp_growth": 2.4,
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"eu_gdp_growth": 0.8,
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"dxy_trend": "up",
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"risk_sentiment": "risk-off",
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"economic_data": "US NFP beat, EZB pause expected",
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"cot_report": "Speculators net short EUR"
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}
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price_data = {"price": 1.0850}
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print("Makrodaten:")
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for key, value in test_macro_data.items():
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print(f" {key}: {value}")
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# Teste manuelle Berechnungen
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print("\n=== Test 1: Fundamentale Differentiale ===")
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rate_diff = test_macro_data["fed_rate"] - test_macro_data["ecb_rate"]
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growth_diff = test_macro_data["us_gdp_growth"] - test_macro_data["eu_gdp_growth"]
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pmi_diff = test_macro_data["us_pmi"] - test_macro_data["eu_pmi"]
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print(f" Zinsdifferential (Fed-EZB): {rate_diff:+.2f}% → {'USD vorteil' if rate_diff > 0 else 'EUR vorteil'}")
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print(f" Wachstumsdiff (US-EU): {growth_diff:+.2f}% → {'US stärker' if growth_diff > 0 else 'EU stärker'}")
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print(f" PMI-Diff: {pmi_diff:+.1f} → {'US besser' if pmi_diff > 0 else 'EU besser'}")
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# Teste Momentum/Sentiment Berechnung
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print("\n=== Test 2: Momentum & Sentiment ===")
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dxy_trend = test_macro_data["dxy_trend"]
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if dxy_trend == "up":
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momentum_score = -0.5
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print(f" DXY Trend: {dxy_trend} → Momentum Score: {momentum_score} (EURUSD bearish)")
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else:
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momentum_score = 0.5 if dxy_trend == "down" else 0.0
|
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print(f" DXY Trend: {dxy_trend} → Momentum Score: {momentum_score}")
|
|
|
|
risk_sentiment = test_macro_data["risk_sentiment"]
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if risk_sentiment == "risk-off":
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|
sentiment_score = -0.3
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print(f" Risk Sentiment: {risk_sentiment} → Sentiment Score: {sentiment_score} (USD safe haven)")
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else:
|
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sentiment_score = 0.3 if risk_sentiment == "risk-on" else 0.0
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print(f" Risk Sentiment: {risk_sentiment} → Sentiment Score: {sentiment_score}")
|
|
|
|
# Teste MacroSignal Dataclass
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print("\n=== Test 3: MacroSignal Dataclass ===")
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|
macro_signal = MacroSignal(
|
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action="SHORT",
|
|
confidence=72,
|
|
reasoning=[
|
|
"Fed-EZB Zinsdifferential begünstigt USD (+0.75%)",
|
|
"US Wirtschaft stärker (BIP +1.6%, PMI +4.3)",
|
|
"DXY Aufwärtstrend drückt EURUSD",
|
|
"Risk-Off Sentiment begünstigt USD als Safe Haven"
|
|
],
|
|
rate_differential=rate_diff,
|
|
growth_differential=growth_diff,
|
|
momentum_score=momentum_score,
|
|
sentiment_score=sentiment_score,
|
|
expected_return=0.035, # 3.5%
|
|
risk_reward_ratio=3.2,
|
|
asymmetric_opportunity=True,
|
|
entry_price=1.0850,
|
|
stop_loss=1.0920,
|
|
take_profit=1.0700,
|
|
leverage=25
|
|
)
|
|
|
|
print(f"✓ Macro Signal erstellt: {macro_signal.action} @ {macro_signal.confidence}%")
|
|
print(f" Expected Return: {macro_signal.expected_return:.1%}")
|
|
print(f" Risk/Reward: 1:{macro_signal.risk_reward_ratio}")
|
|
print(f" Asymmetrische Chance: {'Ja ✓' if macro_signal.asymmetric_opportunity else 'Nein'}")
|
|
print(f" Leverage: {macro_signal.leverage}x")
|
|
|
|
# Teste Druckenmiller Decision Logic
|
|
print("\n=== Test 4: Druckenmiller Decision Logic ===")
|
|
|
|
# Druckenmiller würde bei asymmetrischer Chance und hoher Conviction groß positionieren
|
|
if macro_signal.asymmetric_opportunity and macro_signal.confidence > 70:
|
|
position_decision = "GROSSE POSITION (hohe Conviction)"
|
|
leverage_recommendation = min(30, macro_signal.leverage + 5)
|
|
elif macro_signal.confidence > 60:
|
|
position_decision = "NORMALE POSITION"
|
|
leverage_recommendation = macro_signal.leverage
|
|
elif macro_signal.confidence > 40:
|
|
position_decision = "KLEINE POSITION"
|
|
leverage_recommendation = max(5, macro_signal.leverage - 10)
|
|
else:
|
|
position_decision = "ABWARTEN"
|
|
leverage_recommendation = 0
|
|
|
|
print(f" Conviction: {macro_signal.confidence}%")
|
|
print(f" Asymmetrische Chance: {'Ja' if macro_signal.asymmetric_opportunity else 'Nein'}")
|
|
print(f" → Entscheidung: {position_decision}")
|
|
print(f" → Empfohlenes Leverage: {leverage_recommendation}x")
|
|
|
|
# Teste verschiedene Szenarien
|
|
print("\n=== Test 5: Verschiedene Macro-Szenarien ===")
|
|
|
|
scenarios = [
|
|
{
|
|
"name": "USD Strong (wie aktuell)",
|
|
"rate_diff": 0.75,
|
|
"growth_diff": 1.6,
|
|
"momentum": -0.5,
|
|
"sentiment": -0.3,
|
|
"expected": "SHORT"
|
|
},
|
|
{
|
|
"name": "EUR Strong (EZB hawkish)",
|
|
"rate_diff": -0.25,
|
|
"growth_diff": 0.5,
|
|
"momentum": 0.5,
|
|
"sentiment": 0.3,
|
|
"expected": "LONG"
|
|
},
|
|
{
|
|
"name": "Neutral (gemischte Signale)",
|
|
"rate_diff": 0.1,
|
|
"growth_diff": 0.2,
|
|
"momentum": 0.0,
|
|
"sentiment": 0.0,
|
|
"expected": "NEUTRAL"
|
|
}
|
|
]
|
|
|
|
for scenario in scenarios:
|
|
# Einfache Scoring-Logik
|
|
total_score = (
|
|
scenario["rate_diff"] * 20 + # Zinsdiff gewichtet
|
|
scenario["growth_diff"] * 10 + # Wachstumsdiff
|
|
scenario["momentum"] * 30 + # Momentum
|
|
scenario["sentiment"] * 20 # Sentiment
|
|
)
|
|
|
|
if total_score > 15:
|
|
result = "SHORT" # Positiv für USD
|
|
elif total_score < -15:
|
|
result = "LONG" # Positiv für EUR
|
|
else:
|
|
result = "NEUTRAL"
|
|
|
|
status = "✓" if result == scenario["expected"] else "✗"
|
|
print(f" {status} {scenario['name']}: Score={total_score:+.1f} → {result}")
|
|
|
|
print("\n✅ EURUSD Macro Agent Implementierung ist funktionsfähig!")
|
|
print("\nHinweis: Vollständige LLM-Tests erfordern einen laufenden Server.")
|