98 lines
4.2 KiB
Python
98 lines
4.2 KiB
Python
import argparse
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import datetime
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from pathlib import Path
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from dotenv import load_dotenv
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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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# 引入原版CLI自带的保存报告的方法
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from cli.main import save_report_to_disk
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def parse_args():
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parser = argparse.ArgumentParser(description="Run TradingAgents Analysis via Command Line")
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parser.add_argument("-t", "--ticker", type=str, required=True,
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help="分析的股票代码（如 NVDA, MU）")
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parser.add_argument("-d", "--date", type=str, default=datetime.datetime.now().strftime("%Y-%m-%d"),
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help="分析日期（格式 YYYY-MM-DD），默认是当天")
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parser.add_argument("-a", "--analysts", type=str, default="market,social,news,fundamentals",
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help="分析师列表，用逗号分隔（可选值: market, social, news, fundamentals）。默认全部包括。")
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parser.add_argument("--depth", type=int, default=1, choices=[1, 2, 3, 4, 5],
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help="研究深度/辩论轮数（推荐 1, 3 或 5），默认使用1")
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parser.add_argument("-p", "--provider", type=str, default="google",
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choices=["openai", "anthropic", "google", "openrouter", "ollama", "xai"],
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help="LLM 提供商，默认是 google")
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parser.add_argument("--shallow-model", type=str, default="gemini-3.1-flash-lite-preview",
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help="指定用于快速思考的模型，默认是 gemini-3.1-flash-lite-preview")
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parser.add_argument("--deep-model", type=str, default="gemini-3.1-pro-preview",
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help="指定用于深度推理的模型，默认是 gemini-3.1-pro-preview")
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parser.add_argument("-n", "--non-interactive", action="store_true", required=True,
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help="必须加上此参数以确认非交互模式运行")
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return parser.parse_args()
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def main():
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load_dotenv()
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args = parse_args()
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print(f"\n[System] Starting analysis for {args.ticker} on {args.date} (Non-Interactive Mode)")
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valid_analyst_keys = ["market", "social", "news", "fundamentals"]
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selected_analysts = [
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a.strip().lower() for a in args.analysts.split(",")
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if a.strip().lower() in valid_analyst_keys
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]
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if not selected_analysts:
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print("[Error] No valid analysts selected. Please check your --analysts argument.")
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return
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print(f"[System] Selected Analysts: {', '.join(selected_analysts)}")
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config = DEFAULT_CONFIG.copy()
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config["llm_provider"] = args.provider
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config["quick_think_llm"] = args.shallow_model
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config["deep_think_llm"] = args.deep_model
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config["max_debate_rounds"] = args.depth
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config["max_risk_discuss_rounds"] = args.depth
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print(f"[System] LLM Provider: {args.provider}")
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print(f"[System] Shallow Model: {args.shallow_model} | Deep Model: {args.deep_model}")
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print(f"[System] Research Depth: {args.depth}")
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print("\n[System] Initializing Trading Agents Graph...")
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ta = TradingAgentsGraph(selected_analysts, debug=False, config=config)
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print("\n[System] Propagating analysis... (This may take a while depending on depth and models)")
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try:
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# 获取包含所有过程记录的 final_state
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final_state, decision = ta.propagate(args.ticker, args.date)
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print("\n" + "="*50)
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print("FINAL TRADING DECISION")
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print("="*50)
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print(decision)
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print("="*50)
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# ====== 使用原版完整的方法保存报告 ======
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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# 存放在项目根目录下的 reports 文件夹，按股票和时间分类
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save_dir = Path.cwd() / "reports" / f"{args.ticker}_{timestamp}"
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report_file = save_report_to_disk(final_state, args.ticker, save_dir)
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print(f"\n[System] Complete reports successfully saved to: {save_dir.resolve()}")
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print(f"[System] Aggregated report file: {report_file.name}")
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except Exception as e:
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print(f"\n[Error] Analysis failed: {e}")
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if __name__ == "__main__":
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main()
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