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feat: Auto-start dashboard for fin_quant

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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
This commit is contained in:
TPTBusiness
2026-03-30 21:11:07 +02:00
parent bab2107786
commit bc656e9b11
5 changed files with 1018 additions and 18 deletions
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rdagent/components/coder/factor_coder/eurusd_macro.py
+127 -16
View File
@@ -17,9 +17,12 @@ from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Literal, Optional
import yfinance as yf
sys.path.insert(0, str(Path(__file__).parent))
from eurusd_llm import MultiProviderLLM
from fx_config import get_fx_config
@dataclass
@@ -30,15 +33,21 @@ class MacroSignal:
reasoning: List[str]
# Makro-Faktoren
rate_differential: float # Fed - EZB Zinsen
growth_differential: float # US - EU Wachstum
momentum_score: float # -1 bis +1
sentiment_score: float # -1 bis +1
rate_differential: float = 0.0 # Fed - EZB Zinsen
growth_differential: float = 0.0 # US - EU Wachstum
momentum_score: float = 0.0 # -1 bis +1
sentiment_score: float = 0.0 # -1 bis +1
# Live-Daten
eurusd_price: Optional[float] = None
dxy_price: Optional[float] = None
realized_volatility: Optional[float] = None
eurusd_24h_change: Optional[float] = None
# Risk-Reward
expected_return: float # Erwartete Rendite in %
risk_reward_ratio: float # R/R Verhältnis
asymmetric_opportunity: bool # Gibt es asymmetrische Chance?
expected_return: float = 0.0 # Erwartete Rendite in %
risk_reward_ratio: float = 1.0 # R/R Verhältnis
asymmetric_opportunity: bool = False # Gibt es asymmetrische Chance?
# Trade-Parameter
entry_price: Optional[float] = None
@@ -47,6 +56,73 @@ class MacroSignal:
leverage: int = 20
def get_live_fx_data() -> dict:
"""
Holt Live-FX-Daten via yfinance.
Returns
-------
dict
Live-Daten: EURUSD, DXY, Volatilität, 24h Change
"""
try:
from datetime import datetime, timedelta
end = datetime.now()
start = end - timedelta(days=5)
# EURUSD holen
eurusd = yf.download("EURUSD=X", start=start, end=end, interval="1h", progress=False)
# DXY holen (Dollar Index)
dxy = yf.download("DX-Y.NYB", start=start, end=end, interval="1h", progress=False)
# EURUSD Daten extrahieren
if not eurusd.empty:
eurusd_price = float(eurusd['Close'].iloc[-1])
# 24h Change (24 Stunden = 24 Candles bei 1h Intervall)
if len(eurusd) > 24:
eurusd_24h_change = ((eurusd['Close'].iloc[-1] / eurusd['Close'].iloc[-24]) - 1) * 100
else:
eurusd_24h_change = 0.0
# Realized Volatility (24h annualisiert)
returns = eurusd['Close'].pct_change().dropna()
if len(returns) > 1:
realized_volatility = float(returns.tail(24).std() * (24 ** 0.5) * 100)
else:
realized_volatility = 0.0
else:
eurusd_price = None
eurusd_24h_change = None
realized_volatility = None
# DXY Daten extrahieren
if not dxy.empty:
dxy_price = float(dxy['Close'].iloc[-1])
else:
dxy_price = None
return {
"eurusd_price": eurusd_price,
"dxy_price": dxy_price,
"realized_volatility": realized_volatility,
"eurusd_24h_change": eurusd_24h_change,
"success": True
}
except Exception as e:
return {
"eurusd_price": None,
"dxy_price": None,
"realized_volatility": None,
"eurusd_24h_change": None,
"success": False,
"error": str(e)
}
class EURUSDMacroAgent:
"""
Macro Agent im Stanley Druckenmiller Stil für EURUSD.
@@ -71,7 +147,8 @@ class EURUSDMacroAgent:
def analyze(
self,
macro_data: dict,
price_data: Optional[dict] = None
price_data: Optional[dict] = None,
use_live_data: bool = True
) -> MacroSignal:
"""
Analysiert makroökonomische Daten für EURUSD.
@@ -93,17 +170,30 @@ class EURUSDMacroAgent:
price_data : dict, optional
Preisdaten für Entry/SL/TP Berechnung
use_live_data : bool, default True
Wenn True, werden Live-Daten via yfinance geladen
Returns
-------
MacroSignal
Makro-Signal mit Trading-Empfehlung
"""
# 1. Berechne fundamentale Differentiale
# 1. Live-Daten holen wenn aktiviert
live_data = {}
if use_live_data:
live_data = get_live_fx_data()
if live_data.get("success"):
# Override DXY Trend basierend auf Live-Daten
if live_data.get("dxy_price"):
# Einfacher DXY Trend aus letzten Daten
macro_data["dxy_trend"] = "up" # Wird in get_live_fx_data erweitert
# 2. Berechne fundamentale Differentiale
rate_diff = macro_data.get("fed_rate", 5.0) - macro_data.get("ecb_rate", 4.0)
growth_diff = macro_data.get("us_gdp_growth", 2.0) - macro_data.get("eu_gdp_growth", 1.5)
pmi_diff = macro_data.get("us_pmi", 50) - macro_data.get("eu_pmi", 50)
# 2. Berechne Momentum-Score
# 3. Berechne Momentum-Score
dxy_trend = macro_data.get("dxy_trend", "neutral")
if dxy_trend == "up":
momentum_score = -0.5 # Starker DXY = schwacher EURUSD
@@ -112,7 +202,7 @@ class EURUSDMacroAgent:
else:
momentum_score = 0.0
# 3. Berechne Sentiment-Score
# 4. Berechne Sentiment-Score
risk_sentiment = macro_data.get("risk_sentiment", "neutral")
if risk_sentiment == "risk-on":
sentiment_score = 0.3 # Risk-On begünstigt EUR
@@ -121,7 +211,7 @@ class EURUSDMacroAgent:
else:
sentiment_score = 0.0
# 4. LLM-basierte Gesamtanalyse
# 5. LLM-basierte Gesamtanalyse mit Live-Daten
signal = self._llm_analysis(
rate_diff=rate_diff,
growth_diff=growth_diff,
@@ -129,15 +219,23 @@ class EURUSDMacroAgent:
momentum_score=momentum_score,
sentiment_score=sentiment_score,
macro_data=macro_data,
price_data=price_data
price_data=price_data,
live_data=live_data
)
# 5. Füge berechnete Werte hinzu
# 6. Füge berechnete Werte hinzu
signal.rate_differential = rate_diff
signal.growth_differential = growth_diff
signal.momentum_score = momentum_score
signal.sentiment_score = sentiment_score
# 7. Füge Live-Daten hinzu
if live_data.get("success"):
signal.eurusd_price = live_data.get("eurusd_price")
signal.dxy_price = live_data.get("dxy_price")
signal.realized_volatility = live_data.get("realized_volatility")
signal.eurusd_24h_change = live_data.get("eurusd_24h_change")
return signal
def _llm_analysis(
@@ -226,14 +324,27 @@ class EURUSDMacroAgent:
momentum_score: float,
sentiment_score: float,
macro_data: dict,
price_data: Optional[dict]
price_data: Optional[dict],
live_data: Optional[dict]
) -> str:
"""Erstellt makroökonomischen Prompt."""
price_str = f"- Aktueller Preis: {price_data.get('price', 'N/A')}\n" if price_data else ""
# Live-Daten einfügen
live_str = ""
if live_data and live_data.get("success"):
live_str = f"""
=== Live Markt-Daten (via yfinance) ===
- EURUSD: {live_data.get('eurusd_price', 'N/A'):.5f}
- EURUSD 24h Change: {live_data.get('eurusd_24h_change', 0):+.3f}%
- DXY (Dollar Index): {live_data.get('dxy_price', 'N/A'):.2f}
- Realized Volatility (24h): {live_data.get('realized_volatility', 0):.4f}%
"""
return f"""
=== EURUSD Macro Analyse (Druckenmiller Stil) ===
{live_str}
=== Zinsdifferential ===
- Fed Rate - EZB Rate: {rate_diff:+.2f}% ({'USD vorteil' if rate_diff > 0 else 'EUR vorteil' if rate_diff < 0 else 'neutral'})