32 lines
1.1 KiB
Python
32 lines
1.1 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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# Load environment variables from .env file
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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["deep_think_llm"] = "gpt-5.4-mini" # Use a different model
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config["quick_think_llm"] = "gpt-5.4-mini" # Use a different model
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config["max_debate_rounds"] = 1 # Increase debate rounds
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# Configure data vendors (default uses yfinance, no extra API keys needed)
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config["data_vendors"] = {
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"core_stock_apis": "yfinance", # Options: alpha_vantage, yfinance
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"technical_indicators": "yfinance", # Options: alpha_vantage, yfinance
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"fundamental_data": "yfinance", # Options: alpha_vantage, yfinance
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"news_data": "yfinance", # Options: alpha_vantage, yfinance
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}
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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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