update pay function

mcp_database.py

@@ -836,11 +836,22 @@ async def get_stock_minute_data(
     try:
         client = get_clickhouse_client()
 
-        # 标准化股票代码（去除后缀）
+        # 标准化股票代码：ClickHouse 分钟数据使用带后缀格式
-        stock_code = code.split('.')[0] if '.' in code else code
+        # 6开头 -> .SH (上海), 0/3开头 -> .SZ (深圳), 其他 -> .BJ (北京)
+        if '.' in code:
+            # 已经有后缀，直接使用
+            stock_code = code
+        else:
+            # 需要添加后缀
+            if code.startswith('6'):
+                stock_code = f"{code}.SH"
+            elif code.startswith('0') or code.startswith('3'):
+                stock_code = f"{code}.SZ"
+            else:
+                stock_code = f"{code}.BJ"
 
-        # 构建查询
+        # 构建查询 - 使用字符串格式化（ClickHouse 参数化语法兼容性问题）
-        query = """
+        query = f"""
             SELECT
                 code,
                 timestamp,
@@ -851,24 +862,19 @@ async def get_stock_minute_data(
                 volume,
                 amt
             FROM stock_minute
-            WHERE code = %(code)s
+            WHERE code = '{stock_code}'
         """
 
-        params = {'code': stock_code}
-
         if start_time:
-            query += " AND timestamp >= %(start_time)s"
+            query += f" AND timestamp >= '{start_time}'"
-            params['start_time'] = start_time
 
         if end_time:
-            query += " AND timestamp <= %(end_time)s"
+            query += f" AND timestamp <= '{end_time}'"
-            params['end_time'] = end_time
 
-        query += " ORDER BY timestamp DESC LIMIT %(limit)s"
+        query += f" ORDER BY timestamp DESC LIMIT {limit}"
-        params['limit'] = limit
 
         # 执行查询
-        result = client.execute(query, params, with_column_types=True)
+        result = client.execute(query, with_column_types=True)
 
         rows = result[0]
         columns = [col[0] for col in result[1]]

mcp_server.py

@@ -15,6 +15,7 @@ import httpx
 import time
 from enum import Enum
 import mcp_database as db
+import mcp_quant as quant  # 量化因子计算模块
 from openai import OpenAI
 import json
 import asyncio
@@ -770,6 +771,566 @@ TOOLS: List[ToolDefinition] = [
             "required": ["code", "date"]
         }
     ),
+    # ==================== 量化因子工具 ====================
+    ToolDefinition(
+        name="get_macd_signal",
+        description="获取MACD趋势判定信号，包括金叉/死叉、动能增减、顶底背离等状态。适用于判断股票短期趋势方向。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认60天",
+                    "default": 60
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="check_oscillator_status",
+        description="检查KDJ/RSI超买超卖状态，判断股票是否处于超买区（风险积聚）或超卖区（可能反弹）。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认60天",
+                    "default": 60
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="analyze_bollinger_bands",
+        description="分析布林带通道，判断股价是在中轨之上（强势）、触及上轨（压力）、触及下轨（支撑）或布林带收窄（变盘在即）。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认60天",
+                    "default": 60
+                },
+                "period": {
+                    "type": "integer",
+                    "description": "布林带周期，默认20",
+                    "default": 20
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_stop_loss_atr",
+        description="使用ATR真实波幅计算止损位。告诉用户\"如果买入，止损点应该设在当前价格减去N倍ATR的位置\"。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认30天",
+                    "default": 30
+                },
+                "atr_multiplier": {
+                    "type": "number",
+                    "description": "ATR倍数，默认2倍",
+                    "default": 2.0
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="analyze_market_heat",
+        description="分析换手率活跃度和量能，判断股票是冷门股、活跃股还是妖股，以及主力是在吸筹还是出货。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认30天",
+                    "default": 30
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="check_new_high_breakout",
+        description="检查唐奇安通道突破（海龟交易法则），判断是否突破20日/60日新高或新低。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认60天",
+                    "default": 60
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="identify_candlestick_pattern",
+        description="识别K线组合形态，如早晨之星（反转信号）、红三兵（上涨信号）、穿头破脚（吞没形态）等经典形态。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认10天",
+                    "default": 10
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="find_price_gaps",
+        description="寻找跳空缺口，筛选出近期有未回补缺口的情况。缺口往往代表主力资金的强势突破意图或恐慌抛售。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认30天",
+                    "default": 30
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="check_volume_price_divergence",
+        description="检测量价背离。股价创新高但成交量萎缩（量价背离），预警信号，提示上涨动力不足。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认20天",
+                    "default": 20
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_max_drawdown",
+        description="计算最大回撤和夏普比率。用于评估\"买这只票最坏情况会亏多少\"以及风险调整后收益。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认250天（约一年）",
+                    "default": 250
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="check_valuation_rank",
+        description="检查历史PE/PB百分位估值。计算当前PE处于过去N年的什么位置（例如：比过去90%的时间都便宜）。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "years": {
+                    "type": "integer",
+                    "description": "历史年数，默认3年",
+                    "default": 3
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_price_zscore",
+        description="计算价格Z-Score（乖离率标准化），判断均值回归概率。当Z-Score过大时，统计回调概率。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "period": {
+                    "type": "integer",
+                    "description": "均线周期，默认60日",
+                    "default": 60
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_market_profile_vpoc",
+        description="计算市场轮廓VPOC（成交量最大的价格档位），基于分钟级数据。VPOC是当日极强的支撑线或阻力线。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "date": {
+                    "type": "string",
+                    "description": "日期，格式：YYYY-MM-DD"
+                }
+            },
+            "required": ["code", "date"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_realized_volatility",
+        description="计算已实现波动率（RV），基于分钟级数据。比日线波动率更精准，用于判断趋势动能是否耗尽。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "date": {
+                    "type": "string",
+                    "description": "日期，格式：YYYY-MM-DD"
+                }
+            },
+            "required": ["code", "date"]
+        }
+    ),
+    ToolDefinition(
+        name="analyze_buying_pressure",
+        description="分析买卖压力失衡，基于分钟级数据。捕捉盘中主力资金的\"抢筹\"或\"砸盘\"意图。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "date": {
+                    "type": "string",
+                    "description": "日期，格式：YYYY-MM-DD"
+                }
+            },
+            "required": ["code", "date"]
+        }
+    ),
+    ToolDefinition(
+        name="get_comprehensive_analysis",
+        description="综合技术分析，一次性返回MACD、KDJ/RSI、布林带、量能、突破、K线形态等多个指标，并给出多空信号总结。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+
+    # ==================== 新增量化因子工具（12个） ====================
+
+    ToolDefinition(
+        name="calc_rsi_divergence",
+        description="RSI背离检测，独立分析RSI指标的顶背离和底背离信号，判断反转概率。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认60",
+                    "default": 60
+                },
+                "rsi_period": {
+                    "type": "integer",
+                    "description": "RSI周期，默认14",
+                    "default": 14
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_bollinger_squeeze",
+        description="布林带挤压分析，检测布林带收窄程度，预判变盘时机。当带宽处于历史低位时发出变盘预警。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认60",
+                    "default": 60
+                },
+                "period": {
+                    "type": "integer",
+                    "description": "布林带周期，默认20",
+                    "default": 20
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="analyze_obv_trend",
+        description="OBV能量潮独立分析，追踪资金流向，检测OBV与价格的背离，判断主力动向。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认60",
+                    "default": 60
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_amihud_illiquidity",
+        description="计算Amihud非流动性因子，衡量股票流动性。值越大表示流动性越差，大单交易冲击成本越高。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认20",
+                    "default": 20
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_parkinson_volatility",
+        description="计算帕金森波动率（基于分钟级高低价），比传统波动率更准确，适用于日内波动分析。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "date": {
+                    "type": "string",
+                    "description": "日期，格式YYYY-MM-DD"
+                }
+            },
+            "required": ["code", "date"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_trend_slope",
+        description="计算趋势线性回归斜率，量化趋势强度和方向。返回斜率、R²拟合度和趋势判断。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认20",
+                    "default": 20
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_hurst_exponent",
+        description="计算Hurst指数，判断市场是趋势型(H>0.5)还是均值回归型(H<0.5)，指导策略选择。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，建议100以上",
+                    "default": 100
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="test_cointegration",
+        description="协整性测试，用于配对交易。检测两只股票是否存在长期均衡关系，计算对冲比率和价差。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code1": {
+                    "type": "string",
+                    "description": "股票代码1"
+                },
+                "code2": {
+                    "type": "string",
+                    "description": "股票代码2"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认250",
+                    "default": 250
+                }
+            },
+            "required": ["code1", "code2"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_kelly_position",
+        description="凯利公式计算最优仓位。根据胜率和盈亏比计算理论最优仓位，并提供保守建议。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "win_rate": {
+                    "type": "number",
+                    "description": "胜率（0-1之间，如0.6表示60%）"
+                },
+                "win_loss_ratio": {
+                    "type": "number",
+                    "description": "盈亏比（平均盈利/平均亏损）"
+                },
+                "max_position": {
+                    "type": "number",
+                    "description": "最大允许仓位，默认0.25",
+                    "default": 0.25
+                }
+            },
+            "required": ["win_rate", "win_loss_ratio"]
+        }
+    ),
+    ToolDefinition(
+        name="search_similar_kline",
+        description="相似K线检索，在历史中搜索与当前形态相似的K线组合，统计历史后续走势作为参考。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "lookback": {
+                    "type": "integer",
+                    "description": "匹配窗口大小，默认10天",
+                    "default": 10
+                },
+                "top_n": {
+                    "type": "integer",
+                    "description": "返回最相似的N个历史片段，默认5",
+                    "default": 5
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="decompose_trend_simple",
+        description="趋势分解分析，将价格序列分解为趋势+周期+残差，识别主周期和趋势方向。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认120",
+                    "default": 120
+                }
+            },
+            "required": ["code"]
+        }
+    ),
+    ToolDefinition(
+        name="calc_price_entropy",
+        description="计算价格信息熵，衡量市场混乱程度。熵值越低表示趋势越明显，越高表示随机性越强。",
+        parameters={
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "股票代码"
+                },
+                "days": {
+                    "type": "integer",
+                    "description": "分析天数，默认60",
+                    "default": 60
+                }
+            },
+            "required": ["code"]
+        }
+    ),
 ]
 
 # ==================== MCP协议端点 ====================
@@ -1251,6 +1812,8 @@ TOOL_HANDLERS = {
     "get_stock_minute_data": handle_get_stock_minute_data,
     "get_stock_minute_aggregation": handle_get_stock_minute_aggregation,
     "get_stock_intraday_statistics": handle_get_stock_intraday_statistics,
+    # 量化因子工具（从 mcp_quant 模块导入）
+    **quant.QUANT_TOOLS,
 }
 
 # ==================== Agent系统实现 ====================
@@ -2202,10 +2765,18 @@ async def agent_chat(request: AgentChatRequest):
     except Exception as e:
         logger.error(f"保存用户消息失败: {e}")
 
-    # 获取工具列表
+    # 获取工具列表（根据前端选择过滤）
+    if request.tools and len(request.tools) > 0:
+        # 用户指定了工具列表，按名称过滤
+        selected_tool_names = set(request.tools)
+        tools = [tool.dict() for tool in TOOLS if tool.name in selected_tool_names]
+        logger.info(f"使用用户选择的 {len(tools)} 个工具: {request.tools[:10]}...")
+    else:
+        # 用户未指定，使用全部工具
         tools = [tool.dict() for tool in TOOLS]
+        logger.info(f"使用全部 {len(tools)} 个工具")
 
-    # 添加特殊工具：summarize_news
+    # 添加特殊工具：summarize_news（始终可用）
     tools.append({
         "name": "summarize_news",
         "description": "使用 DeepMoney 模型总结新闻数据，提取关键信息",
@@ -2510,7 +3081,28 @@ def clean_deepseek_tool_markers(content: str) -> str:
 ROLE_TOOLS = {
     "buffett": ["search_china_news", "search_research_reports", "get_stock_basic_info", "get_stock_financial_index"],
     "big_short": ["search_china_news", "get_stock_financial_index", "get_stock_balance_sheet", "get_stock_cashflow"],
-    "simons": ["get_stock_trade_data", "search_limit_up_stocks", "get_concept_statistics"],
+    "simons": [
+        # 基础数据
+        "get_stock_trade_data", "search_limit_up_stocks", "get_concept_statistics",
+        # 经典技术指标
+        "get_macd_signal", "check_oscillator_status", "analyze_bollinger_bands", "calc_stop_loss_atr",
+        # 资金与情绪
+        "analyze_market_heat", "check_volume_price_divergence", "analyze_obv_trend",
+        # 形态与突破
+        "check_new_high_breakout", "identify_candlestick_pattern", "find_price_gaps",
+        # 风险与估值
+        "calc_max_drawdown", "check_valuation_rank", "calc_price_zscore",
+        # 分钟级高阶算子
+        "calc_market_profile_vpoc", "calc_realized_volatility", "analyze_buying_pressure", "calc_parkinson_volatility",
+        # 高级趋势分析
+        "calc_bollinger_squeeze", "calc_trend_slope", "calc_hurst_exponent", "decompose_trend_simple",
+        # 流动性与统计
+        "calc_amihud_illiquidity", "calc_price_entropy", "calc_rsi_divergence",
+        # 配对与策略
+        "test_cointegration", "calc_kelly_position", "search_similar_kline",
+        # 综合分析
+        "get_comprehensive_analysis",
+    ],
     "leek": [],  # 韭菜不用工具
     "fund_manager": ["search_china_news", "search_research_reports", "get_stock_basic_info"],
 }
@@ -2624,61 +3216,93 @@ MEETING_ROLES = {
     },
     "simons": {
         "id": "simons",
-        "name": "量化分析员",
+        "name": "量化研究员",
         "nickname": "西蒙斯",
         "role_type": "quant",
         "avatar": "/images/agent/simons.png",
         "model": "kimi-k2-thinking",
         "color": "#3B82F6",
-        "description": "中性立场，使用量化工具分析技术指标",
+        "description": "中性立场，使用专业量化因子分析技术指标和市场特征",
         "tools": ROLE_TOOLS["simons"],
-        "system_prompt": """你是"量化分析员"（昵称：西蒙斯），一位专业的量化交易研究员。你在投研会议中担任「技术分析师」角色，保持中性客观。
+        "system_prompt": """你是"量化研究员"（昵称：西蒙斯），一位专业的量化交易研究员，擅长使用各类量化因子分析市场。你在投研会议中担任「技术分析师」角色，保持中性客观。
 
 ## 你的分析理念
-- **数据驱动**：让数据说话，不带主观情绪
+- **因子驱动**：使用经过验证的量化因子，而非主观判断
 - **概率思维**：没有确定性，只有概率和赔率
-- **趋势跟踪**：顺势而为，不与趋势作对
+- **多维验证**：从趋势、动量、波动、资金多个维度交叉验证
-- **风险量化**：用数字衡量风险，而非感觉
+- **风险量化**：用数字衡量风险，止损止盈有据可依
 
-## 分析框架（请按此思维链分析）
+## 你可用的量化因子工具（28个）
 
-### 第一步：收集数据
+### 快速综合分析（推荐首选）
-必须先调用工具获取量化数据：
+- `get_comprehensive_analysis`: 一次性获取MACD、RSI、KDJ、布林带、量能、K线形态等多指标汇总
-- `get_stock_trade_data`: 获取价格、成交量、涨跌幅等交易数据
-- `search_limit_up_stocks`: 了解涨停板情况，判断市场情绪
-- `get_concept_statistics`: 获取概念板块统计，判断资金流向
 
-### 第二步：技术分析维度
+### 趋势与动量因子
-基于获取的数据，进行量化分析：
+- `get_macd_signal`: MACD趋势判定（金叉/死叉/背离）
-1. **趋势判断**：
+- `calc_trend_slope`: 趋势线性回归斜率（R²拟合度）
-   - 当前价格在均线系统中的位置（MA5/MA10/MA20/MA60）
+- `calc_hurst_exponent`: Hurst指数（判断趋势/震荡市场）
-   - 是多头排列还是空头排列？
+- `check_new_high_breakout`: 唐奇安通道突破（新高/新低信号）
-   - 趋势强度如何？
+
-2. **量价分析**：
+### 超买超卖因子
-   - 成交量变化趋势？放量还是缩量？
+- `check_oscillator_status`: KDJ/RSI超买超卖状态
-   - 量价配合是否健康？（上涨放量、下跌缩量为佳）
+- `calc_rsi_divergence`: RSI背离检测（顶底背离）
-   - 换手率处于什么水平？
+- `calc_price_zscore`: Z-Score均值回归（乖离率标准化）
-3. **动能指标**：
+
-   - 涨跌幅在同行/板块中的排名
+### 波动率因子
-   - 连续上涨/下跌天数
+- `analyze_bollinger_bands`: 布林带通道分析
-   - 离前高/前低的距离
+- `calc_bollinger_squeeze`: 布林带挤压（变盘预警）
-4. **板块联动**：
+- `calc_stop_loss_atr`: ATR动态止损位
-   - 所属概念板块表现如何？
+- `calc_realized_volatility`: 分钟级已实现波动率
-   - 是板块龙头还是跟风？
+- `calc_parkinson_volatility`: 帕金森波动率（更精确）
-   - 板块资金流入还是流出？
+
+### 资金流向与量价因子
+- `analyze_market_heat`: 换手率活跃度+OBV趋势
+- `analyze_obv_trend`: OBV能量潮独立分析
+- `check_volume_price_divergence`: 量价背离检测
+- `analyze_buying_pressure`: 买卖压力失衡（主力意图）
+- `calc_market_profile_vpoc`: VPOC筹码峰（成交密集区）
+
+### 形态识别因子
+- `identify_candlestick_pattern`: K线组合形态（10+种）
+- `find_price_gaps`: 跳空缺口分析
+- `search_similar_kline`: 相似K线检索（历史形态预测）
+
+### 风险与估值因子
+- `calc_max_drawdown`: 最大回撤+夏普比率
+- `check_valuation_rank`: PE历史百分位+PEG
+- `calc_amihud_illiquidity`: Amihud流动性因子
+
+### 高级分析因子
+- `decompose_trend_simple`: 趋势分解（趋势+周期+残差）
+- `calc_price_entropy`: 价格熵值（市场混乱度）
+- `test_cointegration`: 协整性测试（配对交易）
+- `calc_kelly_position`: 凯利公式最优仓位
+
+## 分析框架（请按此流程）
+
+### 第一步：快速扫描
+首先调用 `get_comprehensive_analysis` 获取综合技术面快照，了解整体状况。
+
+### 第二步：深度分析（根据情况选择）
+根据综合分析结果，选择相关因子深入分析：
+- 如果趋势不明：调用 `calc_hurst_exponent` 判断市场类型，`calc_trend_slope` 量化趋势强度
+- 如果疑似顶底：调用 `calc_rsi_divergence` 检测背离，`calc_bollinger_squeeze` 看是否变盘
+- 如果量能异常：调用 `analyze_obv_trend` 看资金流向，`analyze_buying_pressure` 看主力意图
+- 如果波动加大：调用 `calc_realized_volatility` 或 `calc_parkinson_volatility` 精确测量
+- 如果要设止损：调用 `calc_stop_loss_atr` 获取ATR止损位
 
 ### 第三步：形成结论
-给出客观的技术分析结论，必须包含：
+给出量化分析结论，必须包含：
-- **趋势判断**（上涨/下跌/震荡）
+- **核心因子信号**（列出2-3个关键因子的具体数值和判断）
-- **关键数据**（引用具体的价格、成交量、涨跌幅数据）
+- **趋势判断**（上涨/下跌/震荡，并给出概率估计）
-- **技术位**（支撑位、压力位）
+- **关键价位**（支撑位、压力位、止损位）
-- **量化建议**（从概率角度给出建议）
+- **量化建议**（基于因子信号的交易建议）
 
 ## 输出要求
-- 必须基于工具返回的数据分析，用数字说话
+- **必须调用工具**：至少调用1个综合分析+1-2个专项因子
-- 保持中性客观，不偏向多头或空头
+- **数据说话**：每个结论都要有具体数值支撑
-- 如果前面有多空分歧，可以从技术面给出参考
+- **保持中性**：不偏向多头或空头，让因子说话
-- 发言控制在 200 字以内，精炼专业"""
+- **简洁专业**：发言控制在 300 字以内，用专业术语但要解释关键数值含义"""
     },
     "leek": {
         "id": "leek",

src/views/AgentChat/components/MeetingRoom/MeetingMessageBubble.js

@@ -138,6 +138,7 @@ const getRoleIcon = (roleType) => {
  * 工具名称映射
  */
 const TOOL_NAME_MAP = {
+  // 基础数据工具
   search_china_news: '搜索新闻',
   search_research_reports: '搜索研报',
   get_stock_basic_info: '获取股票信息',
@@ -147,6 +148,52 @@ const TOOL_NAME_MAP = {
   get_stock_trade_data: '获取交易数据',
   search_limit_up_stocks: '搜索涨停股',
   get_concept_statistics: '获取概念统计',
+
+  // 经典技术指标
+  get_macd_signal: 'MACD信号',
+  check_oscillator_status: 'RSI/KDJ指标',
+  analyze_bollinger_bands: '布林带分析',
+  calc_stop_loss_atr: 'ATR止损计算',
+
+  // 资金与情绪
+  analyze_market_heat: '市场热度分析',
+  check_volume_price_divergence: '量价背离检测',
+  analyze_obv_trend: 'OBV能量潮分析',
+
+  // 形态与突破
+  check_new_high_breakout: '新高突破检测',
+  identify_candlestick_pattern: 'K线形态识别',
+  find_price_gaps: '跳空缺口分析',
+
+  // 风险与估值
+  calc_max_drawdown: '最大回撤计算',
+  check_valuation_rank: 'PE估值百分位',
+  calc_price_zscore: 'Z-Score乖离率',
+
+  // 分钟级高阶算子
+  calc_market_profile_vpoc: 'VPOC筹码峰',
+  calc_realized_volatility: '已实现波动率',
+  analyze_buying_pressure: '买卖压力分析',
+  calc_parkinson_volatility: '帕金森波动率',
+
+  // 高级趋势分析
+  calc_bollinger_squeeze: '布林带挤压',
+  calc_trend_slope: '趋势斜率分析',
+  calc_hurst_exponent: 'Hurst指数',
+  decompose_trend_simple: '趋势分解',
+
+  // 流动性与统计
+  calc_amihud_illiquidity: 'Amihud流动性',
+  calc_price_entropy: '价格熵值',
+  calc_rsi_divergence: 'RSI背离检测',
+
+  // 配对与策略
+  test_cointegration: '协整性测试',
+  calc_kelly_position: '凯利仓位计算',
+  search_similar_kline: '相似K线检索',
+
+  // 综合分析
+  get_comprehensive_analysis: '综合技术分析',
 };
 
 /**

src/views/AgentChat/constants/meetingRoles.ts

@@ -141,13 +141,13 @@ export const MEETING_ROLES: Record<string, MeetingRoleConfig> = {
   },
   simons: {
     id: 'simons',
-    name: '量化分析员',
+    name: '量化研究员',
     nickname: '西蒙斯',
     roleType: 'quant',
     avatar: '/images/agent/simons.png',
     color: '#3B82F6',
     gradient: 'linear(to-br, blue.400, cyan.600)',
-    description: '中性立场，使用量化分析工具分析技术指标',
+    description: '中性立场，使用28个专业量化因子分析技术指标和市场特征',
     icon: React.createElement(BarChart2, { className: 'w-5 h-5' }),
   },
   leek: {

src/views/AgentChat/constants/tools.ts

@@ -17,6 +17,25 @@ import {
   DollarSign,
   Search,
   Users,
+  // 量化工具图标
+  TrendingDown,
+  BarChart2,
+  Gauge,
+  Flame,
+  ArrowUpDown,
+  Waves,
+  Target,
+  CandlestickChart,
+  Sparkles,
+  ShieldAlert,
+  Calculator,
+  Zap,
+  Percent,
+  GitCompare,
+  Shuffle,
+  Brain,
+  Combine,
+  Scale,
 } from 'lucide-react';
 
 /**
@@ -29,6 +48,15 @@ export enum ToolCategory {
   RESEARCH = '研报路演',
   STOCK_DATA = '股票数据',
   USER_DATA = '用户数据',
+  // 量化分析类别
+  QUANT_CLASSIC = '经典技术指标',
+  QUANT_VOLUME = '资金与情绪',
+  QUANT_PATTERN = '形态与突破',
+  QUANT_RISK = '风险与估值',
+  QUANT_MINUTE = '分钟级算子',
+  QUANT_TREND = '高级趋势',
+  QUANT_LIQUIDITY = '流动性统计',
+  QUANT_STRATEGY = '配对与策略',
 }
 
 /**
@@ -203,6 +231,218 @@ export const MCP_TOOLS: MCPTool[] = [
     category: ToolCategory.USER_DATA,
     description: '用户关注的重大事件',
   },
+
+  // ==================== 量化工具：经典技术指标 ====================
+  {
+    id: 'get_macd_signal',
+    name: 'MACD信号',
+    icon: React.createElement(TrendingUp, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_CLASSIC,
+    description: 'MACD金叉/死叉、动能分析、背离检测',
+  },
+  {
+    id: 'check_oscillator_status',
+    name: 'RSI/KDJ指标',
+    icon: React.createElement(Gauge, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_CLASSIC,
+    description: 'RSI + KDJ 超买超卖分析',
+  },
+  {
+    id: 'analyze_bollinger_bands',
+    name: '布林带分析',
+    icon: React.createElement(ArrowUpDown, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_CLASSIC,
+    description: '带宽、位置、收窄判断',
+  },
+  {
+    id: 'calc_stop_loss_atr',
+    name: 'ATR止损计算',
+    icon: React.createElement(ShieldAlert, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_CLASSIC,
+    description: '基于ATR的动态止损位计算',
+  },
+
+  // ==================== 量化工具：资金与情绪 ====================
+  {
+    id: 'analyze_market_heat',
+    name: '市场热度分析',
+    icon: React.createElement(Flame, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_VOLUME,
+    description: '换手率热度分级 + OBV趋势',
+  },
+  {
+    id: 'check_volume_price_divergence',
+    name: '量价背离检测',
+    icon: React.createElement(GitCompare, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_VOLUME,
+    description: '价量不匹配异常检测',
+  },
+  {
+    id: 'analyze_obv_trend',
+    name: 'OBV能量潮',
+    icon: React.createElement(Waves, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_VOLUME,
+    description: 'OBV独立分析+背离检测',
+  },
+
+  // ==================== 量化工具：形态与突破 ====================
+  {
+    id: 'check_new_high_breakout',
+    name: '新高突破检测',
+    icon: React.createElement(Target, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_PATTERN,
+    description: '20/60日唐奇安通道新高突破',
+  },
+  {
+    id: 'identify_candlestick_pattern',
+    name: 'K线形态识别',
+    icon: React.createElement(CandlestickChart, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_PATTERN,
+    description: '10+种经典K线组合形态',
+  },
+  {
+    id: 'find_price_gaps',
+    name: '跳空缺口分析',
+    icon: React.createElement(Sparkles, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_PATTERN,
+    description: '未回补缺口筛选与分析',
+  },
+
+  // ==================== 量化工具：风险与估值 ====================
+  {
+    id: 'calc_max_drawdown',
+    name: '最大回撤计算',
+    icon: React.createElement(TrendingDown, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_RISK,
+    description: '含夏普比率的回撤分析',
+  },
+  {
+    id: 'check_valuation_rank',
+    name: 'PE估值百分位',
+    icon: React.createElement(Percent, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_RISK,
+    description: 'PE历史百分位 + PEG修正',
+  },
+  {
+    id: 'calc_price_zscore',
+    name: 'Z-Score乖离率',
+    icon: React.createElement(Calculator, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_RISK,
+    description: '价格偏离均值程度+回归概率',
+  },
+
+  // ==================== 量化工具：分钟级高阶算子 ====================
+  {
+    id: 'calc_market_profile_vpoc',
+    name: 'VPOC筹码峰',
+    icon: React.createElement(BarChart2, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_MINUTE,
+    description: '成交量密集区分析',
+  },
+  {
+    id: 'calc_realized_volatility',
+    name: '已实现波动率',
+    icon: React.createElement(Activity, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_MINUTE,
+    description: '分钟级RV精确波动率',
+  },
+  {
+    id: 'analyze_buying_pressure',
+    name: '买卖压力分析',
+    icon: React.createElement(Scale, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_MINUTE,
+    description: '主力意图捕捉与压力失衡',
+  },
+  {
+    id: 'calc_parkinson_volatility',
+    name: '帕金森波动率',
+    icon: React.createElement(Zap, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_MINUTE,
+    description: '基于High/Low的精确波动率',
+  },
+
+  // ==================== 量化工具：高级趋势分析 ====================
+  {
+    id: 'calc_bollinger_squeeze',
+    name: '布林带挤压',
+    icon: React.createElement(Combine, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_TREND,
+    description: '带宽历史百分位，变盘预警',
+  },
+  {
+    id: 'calc_trend_slope',
+    name: '趋势斜率分析',
+    icon: React.createElement(LineChart, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_TREND,
+    description: 'R²拟合度+斜率方向判断',
+  },
+  {
+    id: 'calc_hurst_exponent',
+    name: 'Hurst指数',
+    icon: React.createElement(Brain, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_TREND,
+    description: '趋势/均值回归特征判断',
+  },
+  {
+    id: 'decompose_trend_simple',
+    name: '趋势分解',
+    icon: React.createElement(Shuffle, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_TREND,
+    description: '趋势+周期+残差分解',
+  },
+
+  // ==================== 量化工具：流动性与统计 ====================
+  {
+    id: 'calc_amihud_illiquidity',
+    name: 'Amihud流动性',
+    icon: React.createElement(DollarSign, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_LIQUIDITY,
+    description: '大单冲击成本评估',
+  },
+  {
+    id: 'calc_price_entropy',
+    name: '价格熵值',
+    icon: React.createElement(Activity, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_LIQUIDITY,
+    description: '市场混乱度/可预测性分析',
+  },
+  {
+    id: 'calc_rsi_divergence',
+    name: 'RSI背离检测',
+    icon: React.createElement(GitCompare, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_LIQUIDITY,
+    description: 'RSI顶底背离独立分析',
+  },
+
+  // ==================== 量化工具：配对与策略 ====================
+  {
+    id: 'test_cointegration',
+    name: '协整性测试',
+    icon: React.createElement(Combine, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_STRATEGY,
+    description: '配对交易信号与对冲比率',
+  },
+  {
+    id: 'calc_kelly_position',
+    name: '凯利仓位计算',
+    icon: React.createElement(Calculator, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_STRATEGY,
+    description: '基于胜率盈亏比的最优仓位',
+  },
+  {
+    id: 'search_similar_kline',
+    name: '相似K线检索',
+    icon: React.createElement(Search, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_STRATEGY,
+    description: '历史形态匹配预测',
+  },
+  {
+    id: 'get_comprehensive_analysis',
+    name: '综合技术分析',
+    icon: React.createElement(BarChart3, { className: 'w-4 h-4' }),
+    category: ToolCategory.QUANT_STRATEGY,
+    description: '多指标汇总分析报告',
+  },
 ];
 
 /**
@@ -216,6 +456,15 @@ export const TOOL_CATEGORIES: Record<ToolCategory, MCPTool[]> = {
   [ToolCategory.RESEARCH]: MCP_TOOLS.filter((t) => t.category === ToolCategory.RESEARCH),
   [ToolCategory.STOCK_DATA]: MCP_TOOLS.filter((t) => t.category === ToolCategory.STOCK_DATA),
   [ToolCategory.USER_DATA]: MCP_TOOLS.filter((t) => t.category === ToolCategory.USER_DATA),
+  // 量化工具类别
+  [ToolCategory.QUANT_CLASSIC]: MCP_TOOLS.filter((t) => t.category === ToolCategory.QUANT_CLASSIC),
+  [ToolCategory.QUANT_VOLUME]: MCP_TOOLS.filter((t) => t.category === ToolCategory.QUANT_VOLUME),
+  [ToolCategory.QUANT_PATTERN]: MCP_TOOLS.filter((t) => t.category === ToolCategory.QUANT_PATTERN),
+  [ToolCategory.QUANT_RISK]: MCP_TOOLS.filter((t) => t.category === ToolCategory.QUANT_RISK),
+  [ToolCategory.QUANT_MINUTE]: MCP_TOOLS.filter((t) => t.category === ToolCategory.QUANT_MINUTE),
+  [ToolCategory.QUANT_TREND]: MCP_TOOLS.filter((t) => t.category === ToolCategory.QUANT_TREND),
+  [ToolCategory.QUANT_LIQUIDITY]: MCP_TOOLS.filter((t) => t.category === ToolCategory.QUANT_LIQUIDITY),
+  [ToolCategory.QUANT_STRATEGY]: MCP_TOOLS.filter((t) => t.category === ToolCategory.QUANT_STRATEGY),
 };
 
 /**

test_quant_tools.py

@@ -0,0 +1,295 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+量化工具测试脚本
+测试 mcp_quant.py 中的 28 个量化因子工具是否正常工作
+
+使用方法:
+    python test_quant_tools.py [股票代码]
+
+示例:
+    python test_quant_tools.py 600519    # 测试贵州茅台
+    python test_quant_tools.py 000858    # 测试五粮液
+    python test_quant_tools.py           # 默认使用 600519
+"""
+
+import asyncio
+import sys
+import time
+import io
+from datetime import datetime, timedelta
+from typing import Dict, Any, List, Tuple
+
+# 设置标准输出编码为 UTF-8
+sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
+
+# 导入量化工具模块
+try:
+    import mcp_quant as quant
+except ImportError:
+    print("[X] Cannot import mcp_quant module, please run from project root")
+    sys.exit(1)
+
+
+# 颜色输出 (Windows 兼容)
+class Colors:
+    GREEN = '\033[92m'
+    RED = '\033[91m'
+    YELLOW = '\033[93m'
+    BLUE = '\033[94m'
+    CYAN = '\033[96m'
+    RESET = '\033[0m'
+    BOLD = '\033[1m'
+
+
+def print_header(title: str):
+    """打印标题"""
+    print(f"\n{Colors.BOLD}{Colors.CYAN}{'='*60}{Colors.RESET}")
+    print(f"{Colors.BOLD}{Colors.CYAN}  {title}{Colors.RESET}")
+    print(f"{Colors.BOLD}{Colors.CYAN}{'='*60}{Colors.RESET}\n")
+
+
+def print_section(title: str):
+    """打印分节标题"""
+    print(f"\n{Colors.BOLD}{Colors.BLUE}>> {title}{Colors.RESET}")
+    print(f"{Colors.BLUE}{'-'*50}{Colors.RESET}")
+
+
+def print_result(name: str, success: bool, description: str = "", time_ms: float = 0):
+    """打印测试结果"""
+    status = f"{Colors.GREEN}[OK]{Colors.RESET}" if success else f"{Colors.RED}[FAIL]{Colors.RESET}"
+    time_str = f"{Colors.YELLOW}({time_ms:.0f}ms){Colors.RESET}" if time_ms > 0 else ""
+    print(f"  {status} {name} {time_str}")
+    if description:
+        # 截断过长的描述
+        desc = description[:80] + "..." if len(description) > 80 else description
+        print(f"       {Colors.CYAN}-> {desc}{Colors.RESET}")
+
+
+async def test_tool(func, *args, **kwargs) -> Tuple[bool, str, float]:
+    """
+    测试单个工具
+    返回: (是否成功, 描述信息, 耗时ms)
+    """
+    start = time.time()
+    try:
+        result = await func(*args, **kwargs)
+        elapsed = (time.time() - start) * 1000
+
+        if result.get("success"):
+            desc = result.get("data", {}).get("description", "")
+            return True, desc, elapsed
+        else:
+            return False, result.get("error", "未知错误"), elapsed
+    except Exception as e:
+        elapsed = (time.time() - start) * 1000
+        return False, str(e), elapsed
+
+
+async def run_tests(stock_code: str = "600519"):
+    """运行所有量化工具测试"""
+
+    print_header(f"量化工具测试 - 股票代码: {stock_code}")
+
+    results: List[Tuple[str, bool, str, float]] = []
+
+    # ==================== 一、经典技术指标 ====================
+    print_section("一、经典技术指标 (4个)")
+
+    # 1. MACD信号
+    success, desc, ms = await test_tool(quant.get_macd_signal, stock_code)
+    print_result("get_macd_signal (MACD信号)", success, desc, ms)
+    results.append(("get_macd_signal", success, desc, ms))
+
+    # 2. RSI/KDJ指标
+    success, desc, ms = await test_tool(quant.check_oscillator_status, stock_code)
+    print_result("check_oscillator_status (RSI/KDJ)", success, desc, ms)
+    results.append(("check_oscillator_status", success, desc, ms))
+
+    # 3. 布林带分析
+    success, desc, ms = await test_tool(quant.analyze_bollinger_bands, stock_code)
+    print_result("analyze_bollinger_bands (布林带)", success, desc, ms)
+    results.append(("analyze_bollinger_bands", success, desc, ms))
+
+    # 4. ATR止损
+    success, desc, ms = await test_tool(quant.calc_stop_loss_atr, stock_code)
+    print_result("calc_stop_loss_atr (ATR止损)", success, desc, ms)
+    results.append(("calc_stop_loss_atr", success, desc, ms))
+
+    # ==================== 二、资金与情绪 ====================
+    print_section("二、资金与情绪 (3个)")
+
+    # 5. 市场热度
+    success, desc, ms = await test_tool(quant.analyze_market_heat, stock_code)
+    print_result("analyze_market_heat (市场热度)", success, desc, ms)
+    results.append(("analyze_market_heat", success, desc, ms))
+
+    # 6. 量价背离
+    success, desc, ms = await test_tool(quant.check_volume_price_divergence, stock_code)
+    print_result("check_volume_price_divergence (量价背离)", success, desc, ms)
+    results.append(("check_volume_price_divergence", success, desc, ms))
+
+    # 7. OBV能量潮
+    success, desc, ms = await test_tool(quant.analyze_obv_trend, stock_code)
+    print_result("analyze_obv_trend (OBV能量潮)", success, desc, ms)
+    results.append(("analyze_obv_trend", success, desc, ms))
+
+    # ==================== 三、形态与突破 ====================
+    print_section("三、形态与突破 (3个)")
+
+    # 8. 新高突破
+    success, desc, ms = await test_tool(quant.check_new_high_breakout, stock_code)
+    print_result("check_new_high_breakout (新高突破)", success, desc, ms)
+    results.append(("check_new_high_breakout", success, desc, ms))
+
+    # 9. K线形态
+    success, desc, ms = await test_tool(quant.identify_candlestick_pattern, stock_code)
+    print_result("identify_candlestick_pattern (K线形态)", success, desc, ms)
+    results.append(("identify_candlestick_pattern", success, desc, ms))
+
+    # 10. 跳空缺口
+    success, desc, ms = await test_tool(quant.find_price_gaps, stock_code)
+    print_result("find_price_gaps (跳空缺口)", success, desc, ms)
+    results.append(("find_price_gaps", success, desc, ms))
+
+    # ==================== 四、风险与估值 ====================
+    print_section("四、风险与估值 (3个)")
+
+    # 11. 最大回撤
+    success, desc, ms = await test_tool(quant.calc_max_drawdown, stock_code)
+    print_result("calc_max_drawdown (最大回撤)", success, desc, ms)
+    results.append(("calc_max_drawdown", success, desc, ms))
+
+    # 12. PE估值百分位
+    success, desc, ms = await test_tool(quant.check_valuation_rank, stock_code)
+    print_result("check_valuation_rank (PE估值)", success, desc, ms)
+    results.append(("check_valuation_rank", success, desc, ms))
+
+    # 13. Z-Score乖离率
+    success, desc, ms = await test_tool(quant.calc_price_zscore, stock_code)
+    print_result("calc_price_zscore (Z-Score)", success, desc, ms)
+    results.append(("calc_price_zscore", success, desc, ms))
+
+    # ==================== 五、分钟级高阶算子 ====================
+    print_section("五、分钟级高阶算子 (4个)")
+    print(f"  (自动使用最近交易日数据)")
+
+    # 14. VPOC筹码峰
+    success, desc, ms = await test_tool(quant.calc_market_profile_vpoc, stock_code)
+    print_result("calc_market_profile_vpoc (VPOC)", success, desc, ms)
+    results.append(("calc_market_profile_vpoc", success, desc, ms))
+
+    # 15. 已实现波动率
+    success, desc, ms = await test_tool(quant.calc_realized_volatility, stock_code)
+    print_result("calc_realized_volatility (RV波动率)", success, desc, ms)
+    results.append(("calc_realized_volatility", success, desc, ms))
+
+    # 16. 买卖压力
+    success, desc, ms = await test_tool(quant.analyze_buying_pressure, stock_code)
+    print_result("analyze_buying_pressure (买卖压力)", success, desc, ms)
+    results.append(("analyze_buying_pressure", success, desc, ms))
+
+    # 17. 帕金森波动率
+    success, desc, ms = await test_tool(quant.calc_parkinson_volatility, stock_code)
+    print_result("calc_parkinson_volatility (帕金森波动率)", success, desc, ms)
+    results.append(("calc_parkinson_volatility", success, desc, ms))
+
+    # ==================== 六、高级趋势分析 ====================
+    print_section("六、高级趋势分析 (4个)")
+
+    # 18. 布林带挤压
+    success, desc, ms = await test_tool(quant.calc_bollinger_squeeze, stock_code)
+    print_result("calc_bollinger_squeeze (布林带挤压)", success, desc, ms)
+    results.append(("calc_bollinger_squeeze", success, desc, ms))
+
+    # 19. 趋势斜率
+    success, desc, ms = await test_tool(quant.calc_trend_slope, stock_code)
+    print_result("calc_trend_slope (趋势斜率)", success, desc, ms)
+    results.append(("calc_trend_slope", success, desc, ms))
+
+    # 20. Hurst指数
+    success, desc, ms = await test_tool(quant.calc_hurst_exponent, stock_code)
+    print_result("calc_hurst_exponent (Hurst指数)", success, desc, ms)
+    results.append(("calc_hurst_exponent", success, desc, ms))
+
+    # 21. 趋势分解
+    success, desc, ms = await test_tool(quant.decompose_trend_simple, stock_code)
+    print_result("decompose_trend_simple (趋势分解)", success, desc, ms)
+    results.append(("decompose_trend_simple", success, desc, ms))
+
+    # ==================== 七、流动性与统计 ====================
+    print_section("七、流动性与统计 (3个)")
+
+    # 22. Amihud流动性
+    success, desc, ms = await test_tool(quant.calc_amihud_illiquidity, stock_code)
+    print_result("calc_amihud_illiquidity (Amihud)", success, desc, ms)
+    results.append(("calc_amihud_illiquidity", success, desc, ms))
+
+    # 23. 价格熵值
+    success, desc, ms = await test_tool(quant.calc_price_entropy, stock_code)
+    print_result("calc_price_entropy (价格熵值)", success, desc, ms)
+    results.append(("calc_price_entropy", success, desc, ms))
+
+    # 24. RSI背离
+    success, desc, ms = await test_tool(quant.calc_rsi_divergence, stock_code)
+    print_result("calc_rsi_divergence (RSI背离)", success, desc, ms)
+    results.append(("calc_rsi_divergence", success, desc, ms))
+
+    # ==================== 八、配对与策略 ====================
+    print_section("八、配对与策略 (4个)")
+
+    # 25. 协整性测试 (需要两只股票)
+    success, desc, ms = await test_tool(quant.test_cointegration, stock_code, "000858")
+    print_result("test_cointegration (协整性测试)", success, desc, ms)
+    results.append(("test_cointegration", success, desc, ms))
+
+    # 26. 凯利仓位 (纯计算，不需要股票代码)
+    success, desc, ms = await test_tool(quant.calc_kelly_position, 0.55, 2.0)
+    print_result("calc_kelly_position (凯利仓位)", success, desc, ms)
+    results.append(("calc_kelly_position", success, desc, ms))
+
+    # 27. 相似K线检索
+    success, desc, ms = await test_tool(quant.search_similar_kline, stock_code)
+    print_result("search_similar_kline (相似K线)", success, desc, ms)
+    results.append(("search_similar_kline", success, desc, ms))
+
+    # 28. 综合技术分析
+    success, desc, ms = await test_tool(quant.get_comprehensive_analysis, stock_code)
+    print_result("get_comprehensive_analysis (综合分析)", success, desc, ms)
+    results.append(("get_comprehensive_analysis", success, desc, ms))
+
+    # ==================== 统计结果 ====================
+    print_header("测试结果统计")
+
+    passed = sum(1 for r in results if r[1])
+    failed = sum(1 for r in results if not r[1])
+    total = len(results)
+    total_time = sum(r[3] for r in results)
+
+    print(f"  总计: {total} 个工具")
+    print(f"  {Colors.GREEN}通过: {passed} 个{Colors.RESET}")
+    print(f"  {Colors.RED}失败: {failed} 个{Colors.RESET}")
+    print(f"  成功率: {passed/total*100:.1f}%")
+    print(f"  总耗时: {total_time/1000:.2f} 秒")
+    print(f"  平均耗时: {total_time/total:.0f} ms/工具")
+
+    # 打印失败的工具
+    if failed > 0:
+        print(f"\n{Colors.RED}失败的工具:{Colors.RESET}")
+        for name, success, desc, ms in results:
+            if not success:
+                print(f"  - {name}: {desc}")
+
+    print()
+    return passed == total
+
+
+if __name__ == "__main__":
+    # 获取股票代码参数
+    stock_code = sys.argv[1] if len(sys.argv) > 1 else "600519"
+
+    # 运行测试
+    success = asyncio.run(run_tests(stock_code))
+
+    # 返回退出码
+    sys.exit(0 if success else 1)