25 lines
1.0 KiB
Python
25 lines
1.0 KiB
Python
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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import os
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load_dotenv()
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# Create a custom config
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config = DEFAULT_CONFIG.copy()
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config["llm_provider"] = os.getenv("LLM_PROVIDER", "openai") # Use a different model
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config["backend_url"] = os.getenv("BACKEND_URL", "https://api.openai.com/v1") # Use a different backend
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config["deep_think_llm"] = os.getenv("DEEP_THINK_LLM", "o4-mini") # Use a different model
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config["quick_think_llm"] = os.getenv("QUICK_THINK_LLM", "gpt-4o-mini") # Use a different model
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config["max_debate_rounds"] = int(os.getenv("MAX_DEBATE_ROUNDS", 1)) # Increase debate rounds
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config["online_tools"] = bool(os.getenv("ONLINE_TOOLS", "True")) # Increase debate rounds
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# Initialize with custom 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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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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