mirror of
https://github.com/TauricResearch/TradingAgents.git
synced 2025-06-27 00:41:34 +00:00
78ea029a0b
- Added support for running CLI and Ollama server via Docker - Introduced tests for local embeddings model and standalone Docker setup - Enabled conditional Ollama server launch via LLM_PROVIDER
47 lines
1.9 KiB
Python
47 lines
1.9 KiB
Python
import os
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from tradingagents.graph.trading_graph import TradingAgentsGraph
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from tradingagents.default_config import DEFAULT_CONFIG
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from dotenv import load_dotenv
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def run_analysis(config_overrides=None):
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"""
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Initializes and runs a trading cycle with configurable overrides.
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"""
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load_dotenv() # Load .env file variables
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config = DEFAULT_CONFIG.copy()
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# Override with environment variables if set
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config["llm_provider"] = os.environ.get("LLM_PROVIDER", config.get("llm_provider", "google"))
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config["backend_url"] = os.environ.get("LLM_BACKEND_URL", config.get("backend_url", "https://generativelanguage.googleapis.com/v1"))
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config["deep_think_llm"] = os.environ.get("LLM_DEEP_THINK_MODEL", config.get("deep_think_llm", "gemini-2.0-flash"))
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config["quick_think_llm"] = os.environ.get("LLM_QUICK_THINK_MODEL", config.get("quick_think_llm", "gemini-2.0-flash"))
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config["max_debate_rounds"] = int(os.environ.get("MAX_DEBATE_ROUNDS", config.get("max_debate_rounds", 1)))
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config["online_tools"] = os.environ.get("ONLINE_TOOLS", str(config.get("online_tools", True))).lower() == 'true'
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# Apply overrides from function argument
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if config_overrides:
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config.update(config_overrides)
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print("Using configuration:")
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for key, value in config.items():
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print(f"{key}: {value}")
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# Initialize with the final config
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ta = TradingAgentsGraph(debug=True, config=config)
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# Forward propagate
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_, decision = ta.propagate("NVDA", "2024-05-10")
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return decision
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if __name__ == "__main__":
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# Example of running the trading analysis
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# You can override specific configurations here if needed, e.g.:
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# decision = run_trading_cyrun_analysiscle(config_overrides={"max_debate_rounds": 2})
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decision = run_analysis()
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print(decision)
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# Memorize mistakes and reflect
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# ta.reflect_and_remember(1000) # parameter is the position returns
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