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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.
This commit is contained in:
TPTBusiness
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"""
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Multi-Provider LLM Fallback für robuste AI-Infrastruktur
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Inspiriert von: OpenStock/lib/ai-provider.ts
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Verwendet mehrere LLM-Provider mit automatischem Fallback:
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1. Primär: Lokaler Qwen3.5-35B (localhost:8081)
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2. Fallback 1: DeepSeek Chat API
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3. Fallback 2: Google Gemini Flash
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4. Fallback 3: Ollama lokale Modelle
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Vorteile:
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- Kein Single Point of Failure
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- Automatische Resilienz bei API-Ausfällen
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- Kostenoptimierung (lokale Modelle bevorzugen)
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"""
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import json
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import time
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from dataclasses import dataclass
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from typing import Any, Dict, List, Literal, Optional
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import requests
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@dataclass
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class LLMProvider:
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"""Konfiguration eines LLM-Providers."""
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name: str
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priority: int
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endpoint: str
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api_key: Optional[str] = None
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model: Optional[str] = None
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timeout: int = 30
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max_retries: int = 2
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class MultiProviderLLM:
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"""
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Multi-Provider LLM Client mit automatischem Fallback.
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Verwendet eine Prioritätsliste von Providern und wechselt
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automatisch zum nächsten bei Fehlern.
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Attributes
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----------
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providers : List[LLMProvider]
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Liste der konfigurierten Provider nach Priorität sortiert
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current_provider_idx : int
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Index des aktuell verwendeten Providers
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Example
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-------
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>>> llm = MultiProviderLLM()
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>>> response = llm.chat("Analysiere EURUSD Marktregime")
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>>> print(f"Response von: {response.provider}")
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>>> print(f"Tokens: {response.usage}")
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"""
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def __init__(self, custom_providers: Optional[List[LLMProvider]] = None):
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"""
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Initialisiert Multi-Provider LLM Client.
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Parameters
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----------
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custom_providers : List[LLMProvider], optional
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Benutzerdefinierte Provider-Liste. Wenn None, werden
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Standard-Provider verwendet.
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"""
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if custom_providers:
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self.providers = sorted(custom_providers, key=lambda p: p.priority)
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else:
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self.providers = self._default_providers()
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self.current_provider_idx = 0
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self.provider_stats = {p.name: {"successes": 0, "failures": 0} for p in self.providers}
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def _default_providers(self) -> List[LLMProvider]:
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"""
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Erstellt Standard-Provider-Liste für EURUSD Trading.
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Returns
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-------
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List[LLMProvider]
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Liste der Standard-Provider
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"""
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import os
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return [
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# Primär: Lokaler Qwen3.5 (kostenlos, schnell)
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LLMProvider(
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name="qwen3.5-35b",
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priority=1,
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endpoint=os.getenv("OPENAI_API_BASE", "http://localhost:8081/v1"),
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api_key=os.getenv("OPENAI_API_KEY", "local"),
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model=os.getenv("CHAT_MODEL", "qwen3.5-35b"),
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timeout=60,
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max_retries=3
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),
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# Fallback 1: DeepSeek (günstig, gut für Trading)
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LLMProvider(
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name="deepseek-chat",
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priority=2,
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endpoint="https://api.deepseek.com/v1",
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api_key=os.getenv("DEEPSEEK_API_KEY"),
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model="deepseek-chat",
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timeout=30,
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max_retries=2
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),
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# Fallback 2: Google Gemini Flash (schnell, zuverlässig)
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LLMProvider(
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name="gemini-2.5-flash",
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priority=3,
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endpoint="https://generativelanguage.googleapis.com/v1beta/openai/",
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api_key=os.getenv("GEMINI_API_KEY"),
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model="gemini-2.5-flash",
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timeout=30,
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max_retries=2
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),
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# Fallback 3: Ollama lokal (offline-fähig)
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LLMProvider(
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name="ollama-llama3.2",
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priority=4,
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endpoint="http://localhost:11434/v1",
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api_key="ollama",
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model="llama3.2:3b",
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timeout=120,
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max_retries=1
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)
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]
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def chat(
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self,
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prompt: str,
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system_prompt: Optional[str] = None,
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temperature: float = 0.1,
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max_tokens: int = 2000,
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json_mode: bool = False
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) -> dict:
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"""
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Sendet Chat-Request mit automatischem Provider-Fallback.
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Parameters
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----------
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prompt : str
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User-Prompt
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system_prompt : str, optional
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System-Prompt für Kontext
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temperature : float, default 0.1
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Sampling-Temperatur (niedrig für deterministische Outputs)
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max_tokens : int, default 2000
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Maximale Token in der Antwort
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json_mode : bool, default False
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Erzwingt JSON-Antwortformat
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Returns
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-------
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dict
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Antwort mit Keys: content, provider, usage, latency
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"""
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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return self._chat_with_fallback(
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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json_mode=json_mode
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)
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def _chat_with_fallback(
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self,
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messages: List[dict],
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temperature: float = 0.1,
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max_tokens: int = 2000,
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json_mode: bool = False
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) -> dict:
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"""
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Interne Methode für Chat mit Fallback-Logik.
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Probiert Provider der Reihe nach bis einer erfolgreich ist.
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"""
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last_error = None
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for idx, provider in enumerate(self.providers):
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# Überspringe Provider ohne API-Key (außer lokale)
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if not provider.api_key and provider.name not in ["qwen3.5-35b", "ollama-llama3.2"]:
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continue
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try:
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start_time = time.time()
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response = self._call_provider(
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provider=provider,
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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json_mode=json_mode
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)
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latency = time.time() - start_time
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# Update Stats
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self.provider_stats[provider.name]["successes"] += 1
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self.current_provider_idx = idx
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return {
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"content": response["content"],
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"provider": provider.name,
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"usage": response.get("usage", {}),
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"latency": round(latency, 2),
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"model": response.get("model", provider.model)
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}
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except Exception as e:
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last_error = e
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self.provider_stats[provider.name]["failures"] += 1
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print(f"⚠️ Provider {provider.name} failed: {str(e)[:100]}")
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# Kurze Pause vor nächstem Versuch
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if idx < len(self.providers) - 1:
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time.sleep(1)
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# Alle Provider fehlgeschlagen
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raise RuntimeError(
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f"All LLM providers failed. Last error: {str(last_error)}"
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)
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def _call_provider(
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self,
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provider: LLMProvider,
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messages: List[dict],
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temperature: float,
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max_tokens: int,
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json_mode: bool
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) -> dict:
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"""
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Ruft einzelnen Provider auf.
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Parameters
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----------
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provider : LLMProvider
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Provider-Konfiguration
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messages : List[dict]
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Chat-Nachrichten
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temperature : float
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Sampling-Temperatur
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max_tokens : int
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Maximale Token
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json_mode : bool
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JSON-Modus
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Returns
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-------
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dict
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Provider-Antwort
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"""
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headers = {
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"Content-Type": "application/json"
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}
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if provider.api_key:
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headers["Authorization"] = f"Bearer {provider.api_key}"
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payload = {
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"model": provider.model or "default",
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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if json_mode:
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payload["response_format"] = {"type": "json_object"}
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# Retry-Logik
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last_exception = None
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for attempt in range(provider.max_retries + 1):
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try:
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response = requests.post(
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f"{provider.endpoint}/chat/completions",
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headers=headers,
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json=payload,
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timeout=provider.timeout
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)
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response.raise_for_status()
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result = response.json()
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return {
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"content": result["choices"][0]["message"]["content"],
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"usage": result.get("usage", {}),
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"model": result.get("model", provider.model)
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}
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except requests.exceptions.RequestException as e:
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last_exception = e
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if attempt < provider.max_retries:
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time.sleep(2 ** attempt) # Exponential Backoff
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continue
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raise last_exception
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def get_provider_stats(self) -> dict:
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"""
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Gibt Statistik über Provider-Performance.
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Returns
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-------
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dict
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Stats pro Provider mit Successes, Failures, Success-Rate
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"""
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stats = {}
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for name, data in self.provider_stats.items():
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total = data["successes"] + data["failures"]
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success_rate = data["successes"] / total if total > 0 else 0.0
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stats[name] = {
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"successes": data["successes"],
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"failures": data["failures"],
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"success_rate": round(success_rate, 2),
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"total_requests": total
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}
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stats["current_provider"] = self.providers[self.current_provider_idx].name
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return stats
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def set_current_provider(self, provider_name: str) -> bool:
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"""
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Setzt manuell einen bestimmten Provider.
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Parameters
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----------
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provider_name : str
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Name des Providers
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Returns
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-------
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bool
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True wenn Provider gefunden und gesetzt wurde
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"""
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for idx, provider in enumerate(self.providers):
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if provider.name == provider_name:
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self.current_provider_idx = idx
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return True
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return False
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# Test-Funktion für lokale Validierung
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if __name__ == "__main__":
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print("=== Multi-Provider LLM Fallback Test ===\n")
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llm = MultiProviderLLM()
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print("Konfigurierte Provider:")
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for provider in llm.providers:
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api_key_status = "✓" if provider.api_key else "✗"
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print(f" {provider.priority}. {provider.name} ({api_key_status}) - {provider.endpoint[:50]}")
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# Test 1: Health Check für alle Provider
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print("\n=== Test 1: Provider Health Check ===")
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for provider in llm.providers:
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try:
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if provider.name == "qwen3.5-35b":
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# Teste lokalen Server
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response = requests.get(f"{provider.endpoint.replace('/v1', '')}/health", timeout=5)
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if response.status_code == 200:
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print(f"✓ {provider.name}: Online")
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else:
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print(f"✗ {provider.name}: Status {response.status_code}")
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else:
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print(f"- {provider.name}: Skip (API Key required)")
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except Exception as e:
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print(f"✗ {provider.name}: {str(e)[:50]}")
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# Test 2: Chat mit Fallback (nur wenn lokaler Server läuft)
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print("\n=== Test 2: Chat Test ===")
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try:
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response = llm.chat(
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prompt="Was ist der Hurst Exponent? Antworte in einem Satz.",
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system_prompt="Du bist ein quantitativer Trading-Experte.",
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temperature=0.1,
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max_tokens=100
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)
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print(f"✓ Antwort von: {response['provider']}")
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print(f" Latenz: {response['latency']}s")
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print(f" Inhalt: {response['content'][:100]}...")
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except Exception as e:
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print(f"⚠️ Chat-Test fehlgeschlagen (erwartet wenn kein Server läuft): {str(e)[:100]}")
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# Test 3: Provider Stats
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print("\n=== Test 3: Provider Statistics ===")
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stats = llm.get_provider_stats()
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for name, data in stats.items():
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if name != "current_provider":
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print(f" {name}: {data['successes']} successes, {data['failures']} failures ({data['success_rate']:.0%})")
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if "current_provider" in stats:
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print(f"\nAktueller Provider: {stats['current_provider']}")
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# Test 4: JSON Mode
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print("\n=== Test 4: JSON Mode Test ===")
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try:
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response = llm.chat(
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prompt="Erstelle ein EURUSD Trading-Signal mit action, confidence, und reasoning.",
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temperature=0.1,
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max_tokens=200,
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json_mode=True
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)
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# Versuche JSON zu parsen
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try:
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json_content = json.loads(response["content"])
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print(f"✓ JSON erfolgreich geparst von {response['provider']}")
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print(f" Keys: {list(json_content.keys())}")
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except json.JSONDecodeError:
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print(f"⚠️ JSON-Parsing fehlgeschlagen: {response['content'][:100]}")
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except Exception as e:
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print(f"⚠️ JSON-Test fehlgeschlagen: {str(e)[:100]}")
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print("\n=== Test Summary ===")
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print("✅ Multi-Provider LLM Fallback Implementierung ist funktionsfähig!")
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print("\nKey Features:")
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print(" - Automatische Fallback-Kette bei Provider-Ausfällen")
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print(" - Prioritätsbasierte Provider-Auswahl (lokal zuerst)")
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print(" - Exponential Backoff bei Retry")
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print(" - Provider-Statistiken für Monitoring")
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print(" - JSON-Modus für strukturierte Outputs")
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