111 lines
4.7 KiB
Python
111 lines
4.7 KiB
Python
import os
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from langchain_classic.agents import AgentExecutor, create_tool_calling_agent
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_openai import ChatOpenAI
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from dotenv import load_dotenv
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from adata import stock
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from langchain_core.tools import tool
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from telegram import Update
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from telegram.ext import ApplicationBuilder, ContextTypes, CommandHandler, MessageHandler, filters
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load_dotenv()
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# ================= 你的原有逻辑（完全保留） =================
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@tool
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def get_fina_info(stock_code: str) -> str:
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"""获取指定股票代码的财务数据。当需要获取财务数据的时使用此工具"""
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df = stock.finance.get_core_index(stock_code=stock_code)
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data = df[['report_date', 'report_type', 'total_rev', 'total_rev_yoy_gr', 'net_profit_attr_sh', 'net_profit_yoy_gr',
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'non_gaap_net_profit', 'non_gaap_net_profit_yoy_gr', 'roe_wtd', 'gross_margin', 'net_margin',
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'quick_ratio',
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'inv_turn_rate', 'acct_recv_turn_rate']]
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last_type = data['report_type'].to_list()[0]
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recent_df = data[data.report_type == last_type].iloc[:2, :].reset_index(drop=True)
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recent_df.rename(columns={"report_date": "报告日期", "report_type": "报告类型", "total_rev": "营业总收入",
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"total_rev_yoy_gr": "营业总收入同比增长率", "net_profit_attr_sh": "归母净利润",
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"net_profit_yoy_gr": "归母净利润同比增长率", "non_gaap_net_profit": "扣非净利润",
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"non_gaap_net_profit_yoy_gr": "扣非净利润同比增长率", "roe_wtd": "加权净资产收益率",
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"gross_margin": "销售毛利率", "net_margin": "销售净利率", "quick_ratio": "速动比率",
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"inv_turn_rate": "存货周转率", "acct_recv_turn_rate": "应收账款周转率"
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}, inplace=True)
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formatted_lines = []
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for _, row in recent_df.iterrows():
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line_items = []
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for col, val in zip(recent_df.columns, row):
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if isinstance(val, (int, float)):
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if "率" in col:
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formatted_val = f"{val:.2f}%"
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else:
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formatted_val = f"{val:,.2f}"
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else:
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formatted_val = str(val)
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line_items.append(f"{col}：{formatted_val}")
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formatted_lines.append("\n".join(line_items))
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final_report = f"近两期{last_type}主要财务数据如下：\n" + "\n\n".join(formatted_lines)
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return final_report
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class MyOutputParser(StrOutputParser):
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def parse(self, text) -> str:
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return text.strip()
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llm = ChatOpenAI(model="deepseek-v4-flash")
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tools = [get_fina_info]
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template = """
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你是一个专业的股票分析助手。用户会输入一个股票代码，你可以使用工具来获取其财务数据，之后根据获取到的财务数据来分析这家公司的基本面情况。
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"""
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chat_prompt = ChatPromptTemplate.from_messages([
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("system", template),
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("human", "{input}"),
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("placeholder", "{agent_scratchpad}"),
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])
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agent = create_tool_calling_agent(llm, tools, chat_prompt)
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
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# ================= 新增：Telegram 机器人交互逻辑 =================
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async def start(update: Update, context: ContextTypes.DEFAULT_TYPE):
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"""处理 /start 命令"""
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await update.message.reply_text("你好！我是你的股票分析助手，请直接发送类似“分析一下600699的基本面”的消息。")
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async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE):
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"""处理普通文本消息，调用你的 Agent"""
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user_input = update.message.text
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# 提示用户正在处理，因为 Agent 调用工具可能需要几秒钟
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await update.message.reply_text("🤔 正在分析，请稍候...")
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try:
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# 调用你原有的 Agent 逻辑
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res = agent_executor.invoke({"input": user_input})
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output = res["output"]
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except Exception as e:
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output = f"分析出错：{str(e)}"
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await update.message.reply_text(output)
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if __name__ == "__main__":
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# 从环境变量读取 Token
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token = os.getenv("TELEGRAM_BOT_TOKEN")
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if not token:
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raise ValueError("未在 .env 文件中找到 TELEGRAM_BOT_TOKEN")
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proxy_url = "http://127.0.0.1:8889"
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# 启动机器人
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application = ApplicationBuilder().token(token).proxy(proxy_url).get_updates_proxy(proxy_url).build()
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application.add_handler(CommandHandler("start", start))
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application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message))
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print("Telegram Bot 正在运行...")
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application.run_polling() |