112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
from datetime import datetime
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from dotenv import load_dotenv
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from langchain_core.messages import HumanMessage
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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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# Load environment variables
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load_dotenv()
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# Patch failing tools
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from langchain_core.tools import tool
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import tradingagents.agents.utils.agent_utils as agent_utils
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@tool
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def mock_get_insider_transactions(ticker: str, curr_date: str = None):
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"""Mock insider transactions."""
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return "Insider transactions: None significant."
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@tool
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def mock_get_indicators(ticker: str, curr_date: str = None):
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"""Mock indicators."""
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return "Indicators: RSI 50, MACD positive."
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@tool
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def mock_get_market_movers():
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"""Mock market movers."""
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return "Market Movers: NVDA +5%, TSLA -2%."
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@tool
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def mock_get_earnings_calendar(curr_date: str = None):
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"""Mock earnings calendar."""
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return "Earnings: NVDA reporting soon."
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@tool
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def mock_get_trending_social():
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"""Mock trending social."""
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return "Trending: NVDA, TSLA."
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agent_utils.get_insider_transactions = mock_get_insider_transactions
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agent_utils.get_indicators = mock_get_indicators
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agent_utils.get_market_movers = mock_get_market_movers
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agent_utils.get_earnings_calendar = mock_get_earnings_calendar
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agent_utils.get_trending_social = mock_get_trending_social
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def run_full_system(ticker: str = None):
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print("--- Starting Full Agent System ---")
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# Configure to use screening
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config = DEFAULT_CONFIG.copy()
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config["deep_think_llm"] = "gpt-4o-mini"
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config["quick_think_llm"] = "gpt-4o-mini"
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# Determine if we should include screening
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include_screening = (ticker is None)
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# Initialize graph with screening enabled
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print(f"Initializing TradingAgentsGraph with screening={include_screening}...")
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ta = TradingAgentsGraph(
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include_screening=include_screening,
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config=config,
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debug=True # Enable debug to see the trace
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)
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# Create initial state
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# We use a placeholder ticker since screening will find the real one
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trade_date = datetime.now().strftime("%Y-%m-%d")
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# If ticker is provided, use it; otherwise use placeholder for screening
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initial_ticker = ticker if ticker else "PENDING"
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initial_state = ta.propagator.create_initial_state(initial_ticker, trade_date)
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# Override the initial message to trigger screening ONLY if no ticker provided
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if include_screening:
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initial_state["messages"] = [HumanMessage(content="Find a promising stock to analyze based on today's market movers.")]
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else:
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print(f"Bypassing screening, analyzing ticker: {ticker}")
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# Ensure it is a HumanMessage object for consistency and printing
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initial_state["messages"] = [HumanMessage(content=f"Analyze {ticker}")]
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print(f"Invoking graph with initial prompt: {initial_state['messages'][0].content}")
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# Run the graph
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# We use stream to see progress
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try:
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for chunk in ta.graph.stream(initial_state, config={"recursion_limit": 50}):
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for node, values in chunk.items():
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print(f"--- Node: {node} ---")
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if "messages" in values:
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last_msg = values["messages"][-1]
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if last_msg.content:
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print(f"Output: {last_msg.content}")
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if hasattr(last_msg, 'tool_calls') and last_msg.tool_calls:
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for tc in last_msg.tool_calls:
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print(f"Tool Call: {tc['name']} (Args: {tc['args']})")
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if "company_of_interest" in values:
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print(f"Current Ticker: {values['company_of_interest']}")
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print("\n--- Execution Completed ---")
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except Exception as e:
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print(f"Execution failed: {e}")
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description='Run the Trading Agent System')
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parser.add_argument('--ticker', type=str, help='Stock ticker to analyze (bypasses screening)')
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args = parser.parse_args()
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run_full_system(ticker=args.ticker)
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