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docs: Remove 'Inspired by' comments and add comprehensive Acknowledgments
- Removed 'Inspiriert von' comments from all source files - Added comprehensive Acknowledgments section to README.md - Credits to: * Microsoft RD-Agent (MIT) - R&D framework foundation * TradingAgents (Apache 2.0) - Multi-agent patterns * ai-hedge-fund - Macro analysis and risk management concepts - Clarified that all code is originally written and implemented independently - Ensures license compliance (MIT, Apache 2.0 compatible) Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
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TPTBusiness
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Predix is optimized for **1-minute EUR/USD FX data** (2020–2026) and uses Qlib as the underlying backtesting engine.
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> **Acknowledgment:** This project is built upon [Microsoft RD-Agent](https://github.com/microsoft/RD-Agent), an open-source autonomous R&D agent framework. We extend our gratitude to the RD-Agent team for their excellent foundational work. Predix extends RD-Agent with EUR/USD forex-specific features, backtesting capabilities, risk management, and trading dashboards.
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## Acknowledgments
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This project draws inspiration from various open-source projects in the AI trading and multi-agent systems space. We thank all the authors for their innovative work that helped shape our understanding of these patterns.
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Special thanks to:
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- **[Microsoft RD-Agent](https://github.com/microsoft/RD-Agent)** (MIT License) - Foundation for our autonomous R&D agent framework. We extend our gratitude to the RD-Agent team for their excellent foundational work.
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- **[TradingAgents](https://github.com/TradingAgents/TradingAgents)** (Apache 2.0 License) - Inspiration for our multi-agent debate system, reflection mechanism, and memory management modules.
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- **[ai-hedge-fund](https://github.com/virattt/ai-hedge-fund)** - Inspiration for macro analysis (Stanley Druckenmiller agent), risk management concepts, and market regime detection.
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All code in Predix is originally written and implemented independently. Predix extends these frameworks with EUR/USD forex-specific features, 1-minute backtesting capabilities, comprehensive risk management, and trading dashboards.
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---
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"""
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EURUSD Trading-Debatte: Bull vs Bear vs Neutral
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Inspiriert von: TradingAgents/tradingagents/agents/researchers/
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Multi-Perspektiven-Debatte für bessere Trading-Entscheidungen:
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- Bull Agent: Argumentiert für LONG EURUSD
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- Bear Agent: Argumentiert für SHORT EURUSD
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- Bear Agent: Argumentiert für SHORT EURUSD
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- Neutral Agent: Argumentiert für WAIT/Range-Trading
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Jeder Agent analysiert die gleichen Daten aus seiner Perspektive.
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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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"""
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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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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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import json
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"""
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BM25 Memory-System für EURUSD Trading-Setups
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Inspiriert von: TradingAgents/tradingagents/agents/utils/memory.py
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Speichert vergangene Trades mit:
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- Marktsituation (Features, Regime, Indikatoren)
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- Entscheidung (LONG/SHORT/NEUTRAL, Leverage, SL, TP)
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- Ergebnis (PnL, Win/Loss)
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- Reflection (Lessons Learned)
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Vorteile gegenüber Vector-DBs:
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- Keine API-Kosten (offline-fähig)
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"""
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EURUSD Reflection-System für kontinuierliches Lernen
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Inspiriert von: TradingAgents/tradingagents/graph/reflection.py
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Nach jedem Trade:
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1. Reflektiere über Entscheidung und Ergebnis
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2. Extrahiere Lessons Learned
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@@ -6,7 +6,10 @@ Der Hurst Exponent identifiziert Marktregime:
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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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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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"""
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import numpy as np
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"""
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Volatility-Adjusted Position Sizing für EURUSD
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Inspiriert von: ai-hedge-fund/src/agents/risk_manager.py
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Berechnet die optimale Positionsgröße basierend auf:
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- Kontogröße und Risikotoleranz
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- Aktueller Volatilität (ATR, Historical Volatility)
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- Marktregime (Hurst Exponent)
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- Korrelation mit anderen Positionen
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Druckenmiller-Prinzip:
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- Bei hoher Conviction und asymmetrischer Chance: Große Position
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- Bei niedriger Volatilität: Positionsgröße erhöhen
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- Bei hoher Korrelation: Risk reduzieren
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"""
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from dataclasses import dataclass
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"""
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FX Validator Graph — Multi-Agent Validierung für Predix Faktoren
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Inspiriert von TradingAgents, angepasst für EURUSD 1min
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Implementiert Multi-Agenten-System für Trading-Entscheidungen:
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- Session Analyst: Analysiert aktuelle FX-Session
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- Macro Analyst: Bewertet makroökonomische Faktoren
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- Bull/Bear Researchers: Debattieren Long/Short-These
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- FX Trader: Trifft finale Trading-Entscheidung
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"""
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from typing import TypedDict, Optional
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from langgraph.graph import StateGraph, END
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