31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
from tradingagents.graph.trading_graph import TradingAgentsGraph
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from tradingagents.config import get_config, update_config
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# Get the centralized config (already includes .env loading)
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config = get_config()
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# Customize config if needed
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updates = {
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"deep_think_llm": "gpt-4o-mini", # Use a different model
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"quick_think_llm": "gpt-4o-mini", # Use a different model
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"max_debate_rounds": 1, # Increase debate rounds
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# Configure data vendors
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"data_vendors": {
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"core_stock_apis": "yfinance", # Options: yfinance, alpha_vantage, local
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"technical_indicators": "yfinance", # Options: yfinance, alpha_vantage, local
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"fundamental_data": "alpha_vantage", # Options: openai, alpha_vantage, local
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"news_data": "alpha_vantage", # Options: openai, alpha_vantage, google, local
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}
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}
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update_config(updates)
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# Initialize with custom config
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ta = TradingAgentsGraph(debug=True, config=get_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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