XTQuantAI

Instructions

Implementation Reference

• src/xtquantai/server.py:710-777 (handler)

  The core handler function that implements the tool logic. It parses input, calls xtdata.get_market_data to fetch historical market data for given stock codes, period, dates, and fields, converts numpy arrays to lists, and returns the data as a dictionary.
  async def get_history_market_data(input: GetMarketDataInput) -> Dict[str, Any]: """ 获取历史行情数据 Args: codes: 股票代码列表，用逗号分隔，例如 "000001.SZ,600519.SH" period: 周期，例如 "1d", "1m", "5m" 等 start_date: 开始日期，格式为 "YYYYMMDD" end_date: 结束日期，格式为 "YYYYMMDD"，为空表示当前日期 fields: 字段列表，用逗号分隔，为空表示所有字段 Returns: 历史行情数据 """ try: # 确保XTQuant数据中心已初始化 ensure_xtdc_initialized() if xtdata is None: return {"error": "xtdata模块未正确加载"} # 解析股票代码列表 codes = [code.strip() for code in input.codes.split(",") if code.strip()] if not codes: return {"error": "未提供有效的股票代码"} # 解析字段列表 fields = [] if input.fields: fields = [field.strip() for field in input.fields.split(",") if field.strip()] # 如果未指定字段，使用默认字段 if not fields: fields = ["open", "high", "low", "close", "volume"] print(f"获取历史行情数据: 股票={codes}, 周期={input.period}, 字段={fields}, 开始日期={input.start_date}, 结束日期={input.end_date}") try: # 获取历史行情数据 print(f"调用xtdata.get_market_data({fields}, {codes}, {input.period}, {input.start_date}, {input.end_date})") data = xtdata.get_market_data(fields, codes, period=input.period, start_time=input.start_date, end_time=input.end_date) # 处理返回值 if data is None: return {"error": "获取历史行情数据失败"} # 将数据转换为可序列化的格式 result = {} for code, stock_data in data.items(): code_result = {} for field, values in stock_data.items(): # 将numpy数组转换为列表 if hasattr(values, "tolist"): code_result[field] = values.tolist() else: code_result[field] = list(values) result[code] = code_result return result except Exception as e: print(f"获取历史行情数据出错: {str(e)}") traceback.print_exc() return {"error": f"获取历史行情数据失败: {str(e)}"} except Exception as e: print(f"处理历史行情数据请求出错: {str(e)}") traceback.print_exc() return {"error": str(e)}
• src/xtquantai/server.py:150-156 (schema)

  Pydantic model defining the input schema for the get_history_market_data tool, matching the inputSchema in registration.
  class GetMarketDataInput(BaseModel): codes: str # 股票代码列表，用逗号分隔，例如 "000001.SZ,600519.SH" period: str = "1d" # 周期，例如 "1d", "1m", "5m" 等 start_date: str = "" # 开始日期，格式为 "YYYYMMDD" end_date: str = "" # 结束日期，格式为 "YYYYMMDD"，为空表示当前日期 fields: str = "" # 字段列表，用逗号分隔，为空表示所有字段
• src/xtquantai/server.py:256-289 (registration)

  MCP tool registration in handle_list_tools(), defining name, description, and input schema for the tool.
  types.Tool( name="get_history_market_data", description="获取历史行情数据", inputSchema={ "type": "object", "properties": { "codes": { "type": "string", "description": "股票代码列表，用逗号分隔，例如 \"000001.SZ,600519.SH\"" }, "period": { "type": "string", "description": "周期，例如 \"1d\", \"1m\", \"5m\" 等", "default": "1d" }, "start_date": { "type": "string", "description": "开始日期，格式为 \"YYYYMMDD\"", "default": "" }, "end_date": { "type": "string", "description": "结束日期，格式为 \"YYYYMMDD\"，为空表示当前日期", "default": "" }, "fields": { "type": "string", "description": "字段列表，用逗号分隔，为空表示所有字段", "default": "" } }, "required": ["codes"] } ),
• src/xtquantai/server.py:453-471 (registration)

  Tool dispatch logic in the MCP server's @server.call_tool() handler, which validates arguments, creates input model, calls the handler function, and formats response.
  elif name == "get_history_market_data": if not arguments or "codes" not in arguments: return [types.TextContent(type="text", text="错误: 缺少必要参数 'codes'")] # 处理字符串格式的codes参数 codes_str = arguments["codes"] codes = [code.strip() for code in codes_str.split(",") if code.strip()] period = arguments.get("period", "1d") start_date = arguments.get("start_date", "") end_date = arguments.get("end_date", "") # 处理字符串格式的fields参数 fields_str = arguments.get("fields", "") fields = [field.strip() for field in fields_str.split(",") if field.strip()] input_model = GetMarketDataInput(codes=codes_str, period=period, start_date=start_date, end_date=end_date, fields=fields_str) result = await get_history_market_data(input_model) return [types.TextContent(type="text", text=json.dumps(result, ensure_ascii=False, indent=2))]