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core/__init__.py

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+"""
+MND-IA Core Module
+==================
+共享内核 - 系统数据中心和逻辑基石
+"""
+
+from .world_book import WorldBook, Narrative, MacroCycle, create_narrative_id
+
+__all__ = [
+    'WorldBook',
+    'Narrative',
+    'MacroCycle',
+    'create_narrative_id'
+]
+
+__version__ = '1.0.0'

core/asset_map.json

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+{
+  "_meta": {
+    "version": "4.0.0",
+    "description": "Agent专用资产映射表。包含资产代码、关键词触发器及宏观因子敏感度矩阵。",
+    "instruction": "当计算Trust Index时，使用sensitivity中的数值作为基础权重(x)，结合Narrative强度(y)进行最终打分。"
+  },
+
+  "assets": {
+    "core_A500": {
+      "name": "中证A500",
+      "etfs": ["512050", "512020", "563360"],
+      "keywords": ["核心资产", "大盘", "指数增强", "国家队", "长线资金"],
+      "sensitivity": {
+        "liquidity_easing": 0.6,
+        "policy_support": 0.8,
+        "cpi_rebound": 0.5,
+        "risk_on": 0.9
+      }
+    },
+    "tech_hardware": {
+      "name": "半导体与硬科技",
+      "etfs": ["512480", "588000"],
+      "keywords": ["芯片", "光刻机", "集成电路", "卡脖子", "科创板"],
+      "sensitivity": {
+        "interest_rate_down": 0.9,
+        "policy_tech_self_reliance": 1.0,
+        "geopolitics_tension": 0.7,
+        "risk_on": 1.0
+      }
+    },
+    "tech_software": {
+      "name": "软件与数字经济",
+      "etfs": ["515230", "159869"],
+      "keywords": ["信创", "国产软件", "操作系统", "数据要素", "AI应用", "网络安全"],
+      "sensitivity": {
+        "policy_digital_economy": 1.0,
+        "govt_spending": 0.8,
+        "risk_on": 0.9,
+        "interest_rate_down": 0.7
+      }
+    },
+    "tech_infra": {
+      "name": "5G与新基建",
+      "etfs": ["515050", "515880"],
+      "keywords": ["5G", "6G", "光模块", "通信基站", "算力底座", "东数西算"],
+      "sensitivity": {
+        "policy_new_infra": 1.0,
+        "tech_cycle_up": 0.8,
+        "export_growth": 0.6
+      }
+    },
+    "finance_broker": {
+      "name": "券商/牛市旗手",
+      "etfs": ["512880", "512900"],
+      "keywords": ["成交量", "资本市场", "印花税", "T+0", "金融强国"],
+      "sensitivity": {
+        "market_volume_spike": 1.0,
+        "policy_capital_market": 1.0,
+        "liquidity_easing": 0.8
+      }
+    },
+    "finance_bank": {
+      "name": "银行/高股息",
+      "etfs": ["512800", "159887"],
+      "keywords": ["高股息", "中特估", "红利", "避险", "存款利率"],
+      "sensitivity": {
+        "interest_rate_down": -0.5,
+        "risk_off": 0.8,
+        "policy_soe_reform": 0.7
+      }
+    },
+    "new_energy_vehicle": {
+      "name": "新能源车与电池",
+      "etfs": ["515030", "159840"],
+      "keywords": ["电动车", "锂电池", "宁德时代", "汽车下乡", "碳中和"],
+      "sensitivity": {
+        "consumption_stimulus": 0.8,
+        "export_growth": 0.9,
+        "oil_price_up": 0.5
+      }
+    },
+    "consumption_staples": {
+      "name": "主要消费(白酒)",
+      "etfs": ["512690"],
+      "keywords": ["白酒", "内需", "CPI", "春节", "高端消费"],
+      "sensitivity": {
+        "cpi_rebound": 0.9,
+        "liquidity_easing": 0.6,
+        "foreign_inflow": 0.8
+      }
+    },
+    "healthcare": {
+      "name": "医药医疗",
+      "etfs": ["512010", "159898"],
+      "keywords": ["创新药", "医疗器械", "老龄化", "医保", "CXO"],
+      "sensitivity": {
+        "interest_rate_down": 0.8,
+        "policy_public_health": 0.7,
+        "demographic_trend": 0.5
+      }
+    },
+    "cross_border_tech": {
+      "name": "跨境科技(T+0)",
+      "etfs": ["513330", "513300", "513500"],
+      "keywords": ["港股", "美股", "纳斯达克", "中概股", "平台经济"],
+      "sensitivity": {
+        "fed_rate_cut": 0.9,
+        "currency_rmb_depreciation": 0.8,
+        "policy_platform_economy": 0.7
+      }
+    },
+    "agriculture": {
+      "name": "农业与粮食安全",
+      "etfs": ["159825"],
+      "keywords": ["粮食安全", "一号文件", "猪周期", "种业", "食品通胀"],
+      "sensitivity": {
+        "cpi_rebound": 0.8,
+        "policy_food_security": 1.0,
+        "geopolitics_tension": 0.6
+      }
+    },
+    "resources_gold": {
+      "name": "资源与黄金",
+      "etfs": ["518880", "512400"],
+      "keywords": ["黄金", "有色", "通胀", "避险", "美元下跌"],
+      "sensitivity": {
+        "dollar_index_down": 0.9,
+        "geopolitics_tension": 1.0,
+        "inflation_expectations": 0.8
+      }
+    },
+    "defense_low_altitude": {
+      "name": "军工与低空",
+      "etfs": ["512660"],
+      "keywords": ["低空经济", "国防", "军费", "无人机", "地缘"],
+      "sensitivity": {
+        "policy_low_altitude": 1.0,
+        "geopolitics_tension": 0.9,
+        "govt_spending": 0.7
+      }
+    }
+  },
+
+  "macro_factors_definition": {
+    "liquidity_easing": "央行降准降息，或市场资金利率下行",
+    "interest_rate_down": "无风险利率下行（利好成长股）",
+    "cpi_rebound": "通胀回升，消费复苏",
+    "risk_on": "市场情绪高涨，追逐高风险高收益",
+    "risk_off": "市场恐慌，资金流向避险资产",
+    "policy_tech_self_reliance": "政策强调自主可控、国产替代",
+    "geopolitics_tension": "外部冲突、制裁或地缘紧张",
+    "currency_rmb_depreciation": "人民币贬值（利好出口及跨境资产）",
+    "fed_rate_cut": "美联储降息（利好港股/美股/黄金）"
+  }
+}

core/config.py

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+"""
+配置管理模块
+提供统一的配置加载和访问接口，支持大模型配置、系统配置等
+"""
+import json
+import os
+from pathlib import Path
+from typing import Dict, Any, Optional
+
+
+class Config:
+    """配置管理类，单例模式"""
+    
+    _instance = None
+    _config_data: Dict[str, Any] = {}
+    
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+    
+    def __init__(self):
+        if not self._config_data:
+            self.load_config()
+    
+    def load_config(self, config_path: Optional[str] = None):
+        """
+        加载配置文件
+        
+        Args:
+            config_path: 配置文件路径，默认为项目根目录下的 config.json
+        """
+        if config_path is None:
+            # 获取项目根目录（core 目录的上级目录）
+            project_root = Path(__file__).parent.parent
+            config_path = project_root / "config.json"
+        
+        if not os.path.exists(config_path):
+            raise FileNotFoundError(f"配置文件不存在: {config_path}")
+        
+        with open(config_path, 'r', encoding='utf-8') as f:
+            self._config_data = json.load(f)
+    
+    def get(self, key: str, default: Any = None) -> Any:
+        """
+        获取配置项（支持点号分隔的多级key）
+        
+        Args:
+            key: 配置项键，如 "llm.model_name" 或 "llm"
+            default: 默认值
+            
+        Returns:
+            配置值
+        """
+        keys = key.split('.')
+        value = self._config_data
+        
+        for k in keys:
+            if isinstance(value, dict):
+                value = value.get(k)
+                if value is None:
+                    return default
+            else:
+                return default
+        
+        return value
+    
+    @property
+    def llm_config(self) -> Dict[str, Any]:
+        """获取大模型配置"""
+        return self.get('llm', {})
+    
+    @property
+    def model_name(self) -> str:
+        """获取模型名称"""
+        return self.get('llm.model_name', 'gpt-4')
+    
+    @property
+    def api_base(self) -> str:
+        """获取API地址"""
+        return self.get('llm.api_base', 'https://api.openai.com/v1')
+    
+    @property
+    def api_key(self) -> str:
+        """获取API Key"""
+        return self.get('llm.api_key', '')
+    
+    @property
+    def temperature(self) -> float:
+        """获取温度参数"""
+        return self.get('llm.temperature', 0.7)
+    
+    @property
+    def max_tokens(self) -> int:
+        """获取最大token数"""
+        return self.get('llm.max_tokens', 2000)
+    
+    @property
+    def timeout(self) -> int:
+        """获取超时时间"""
+        return self.get('llm.timeout', 60)
+    
+    def update(self, key: str, value: Any):
+        """
+        更新配置项
+        
+        Args:
+            key: 配置项键，支持点号分隔的多级key
+            value: 新值
+        """
+        keys = key.split('.')
+        config = self._config_data
+        
+        for k in keys[:-1]:
+            if k not in config:
+                config[k] = {}
+            config = config[k]
+        
+        config[keys[-1]] = value
+    
+    def save_config(self, config_path: Optional[str] = None):
+        """
+        保存配置到文件
+        
+        Args:
+            config_path: 配置文件路径，默认为项目根目录下的 config.json
+        """
+        if config_path is None:
+            project_root = Path(__file__).parent.parent
+            config_path = project_root / "config.json"
+        
+        with open(config_path, 'w', encoding='utf-8') as f:
+            json.dump(self._config_data, f, ensure_ascii=False, indent=2)
+
+
+# 全局配置实例
+config = Config()
+
+
+def get_llm_client():
+    """
+    获取配置好的LLM客户端（OpenAI SDK）
+    
+    Returns:
+        OpenAI客户端实例
+    """
+    try:
+        from openai import OpenAI
+        
+        client = OpenAI(
+            api_key=config.api_key,
+            base_url=config.api_base,
+            timeout=config.timeout
+        )
+        return client
+    except ImportError:
+        print("警告: openai 库未安装，无法创建LLM客户端")
+        return None
+    except Exception as e:
+        print(f"创建LLM客户端失败: {e}")
+        return None
+
+
+def llm_call(messages: list, **kwargs) -> Optional[str]:
+    """
+    统一的LLM调用接口
+    
+    Args:
+        messages: 消息列表，格式为 [{"role": "user", "content": "..."}]
+        **kwargs: 其他参数（会覆盖配置文件中的默认值）
+        
+    Returns:
+        LLM返回的文本内容，失败返回None
+    """
+    client = get_llm_client()
+    if client is None:
+        return None
+    
+    try:
+        # 合并配置和传入的参数
+        params = {
+            'model': config.model_name,
+            'temperature': config.temperature,
+            'max_tokens': config.max_tokens,
+        }
+        params.update(kwargs)
+        
+        response = client.chat.completions.create(
+            messages=messages,
+            **params
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        print(f"LLM调用失败: {e}")
+        return None
+
+
+if __name__ == "__main__":
+    # 测试配置加载
+    print("=== 配置管理模块测试 ===")
+    print(f"模型名称: {config.model_name}")
+    print(f"API地址: {config.api_base}")
+    print(f"API Key: {config.api_key[:10]}..." if config.api_key else "API Key: 未配置")
+    print(f"温度: {config.temperature}")
+    print(f"最大tokens: {config.max_tokens}")
+    
+    print("\n完整LLM配置:")
+    print(json.dumps(config.llm_config, indent=2, ensure_ascii=False))
+    
+    # 测试配置更新
+    print("\n测试配置更新...")
+    config.update('llm.temperature', 0.5)
+    print(f"更新后的温度: {config.temperature}")
+    print()
+    # 测试获取不存在的配置
+    print(f"\n获取不存在的配置: {config.get('nonexistent.key', 'default_value')}")
+    # 测试调用
+    respodse = llm_call([{"role": "user", "content": "你好，用100字介绍一下你自己。"}])
+    print(f"\nLLM调用结果:\n{respodse}")

core/macro_matrix.json

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+{
+  "_comment": "宏观逻辑矩阵 - 定义宏观因子对各 ETF 板块的基础理论影响值",
+  "_version": "1.0.0",
+  "_last_updated": "2025-12-30",
+  "_scoring_rules": {
+    "range": "影响值范围: -10 (极度负面) 到 +10 (极度正面)",
+    "neutral": "0 表示无明显影响",
+    "interpretation": "数值越大表示正面影响越强，数值越小表示负面影响越强"
+  },
+  
+  "macro_factors": {
+    "rate_cut": {
+      "description": "降准降息 - 货币宽松政策",
+      "impact": {
+        "tech_etf": 8,
+        "ai_etf": 8,
+        "semiconductor_etf": 7,
+        "new_energy_vehicle_etf": 7,
+        "solar_etf": 6,
+        "pharma_etf": 5,
+        "consumer_etf": 7,
+        "bank_etf": -5,
+        "insurance_etf": -4,
+        "broker_etf": 6,
+        "realestate_etf": 9,
+        "military_etf": 4,
+        "metal_etf": 5,
+        "coal_etf": 2
+      }
+    },
+    "rate_hike": {
+      "description": "加息收紧 - 货币紧缩政策",
+      "impact": {
+        "tech_etf": -7,
+        "ai_etf": -7,
+        "semiconductor_etf": -6,
+        "new_energy_vehicle_etf": -6,
+        "solar_etf": -5,
+        "pharma_etf": -3,
+        "consumer_etf": -6,
+        "bank_etf": 5,
+        "insurance_etf": 4,
+        "broker_etf": -4,
+        "realestate_etf": -9,
+        "military_etf": -2,
+        "metal_etf": -4,
+        "coal_etf": -1
+      }
+    },
+    "currency_depreciation": {
+      "description": "人民币贬值 - 有利出口型企业",
+      "impact": {
+        "tech_etf": 3,
+        "ai_etf": 1,
+        "semiconductor_etf": 2,
+        "new_energy_vehicle_etf": 5,
+        "solar_etf": 6,
+        "pharma_etf": 2,
+        "consumer_etf": -3,
+        "bank_etf": -2,
+        "insurance_etf": -1,
+        "broker_etf": -1,
+        "realestate_etf": -4,
+        "military_etf": 3,
+        "metal_etf": 4,
+        "coal_etf": 1
+      }
+    },
+    "currency_appreciation": {
+      "description": "人民币升值 - 有利进口型企业",
+      "impact": {
+        "tech_etf": -2,
+        "ai_etf": -1,
+        "semiconductor_etf": -1,
+        "new_energy_vehicle_etf": -4,
+        "solar_etf": -5,
+        "pharma_etf": -1,
+        "consumer_etf": 4,
+        "bank_etf": 3,
+        "insurance_etf": 2,
+        "broker_etf": 2,
+        "realestate_etf": 5,
+        "military_etf": -2,
+        "metal_etf": -3,
+        "coal_etf": -1
+      }
+    },
+    "fiscal_stimulus": {
+      "description": "财政刺激政策 - 基建、消费刺激",
+      "impact": {
+        "tech_etf": 6,
+        "ai_etf": 7,
+        "semiconductor_etf": 6,
+        "new_energy_vehicle_etf": 8,
+        "solar_etf": 7,
+        "pharma_etf": 4,
+        "consumer_etf": 9,
+        "bank_etf": 5,
+        "insurance_etf": 4,
+        "broker_etf": 7,
+        "realestate_etf": 8,
+        "military_etf": 6,
+        "metal_etf": 8,
+        "coal_etf": 6
+      }
+    },
+    "regulation_tightening": {
+      "description": "监管收紧 - 行业整顿",
+      "impact": {
+        "tech_etf": -6,
+        "ai_etf": -4,
+        "semiconductor_etf": -3,
+        "new_energy_vehicle_etf": -4,
+        "solar_etf": -3,
+        "pharma_etf": -7,
+        "consumer_etf": -5,
+        "bank_etf": -2,
+        "insurance_etf": -2,
+        "broker_etf": -5,
+        "realestate_etf": -8,
+        "military_etf": -1,
+        "metal_etf": -2,
+        "coal_etf": -3
+      }
+    },
+    "export_strong": {
+      "description": "出口数据强劲",
+      "impact": {
+        "tech_etf": 5,
+        "ai_etf": 3,
+        "semiconductor_etf": 4,
+        "new_energy_vehicle_etf": 7,
+        "solar_etf": 8,
+        "pharma_etf": 3,
+        "consumer_etf": 2,
+        "bank_etf": 3,
+        "insurance_etf": 2,
+        "broker_etf": 4,
+        "realestate_etf": 1,
+        "military_etf": 4,
+        "metal_etf": 6,
+        "coal_etf": 3
+      }
+    },
+    "pmi_expansion": {
+      "description": "PMI 扩张 (>50) - 制造业景气",
+      "impact": {
+        "tech_etf": 7,
+        "ai_etf": 6,
+        "semiconductor_etf": 7,
+        "new_energy_vehicle_etf": 8,
+        "solar_etf": 7,
+        "pharma_etf": 4,
+        "consumer_etf": 6,
+        "bank_etf": 5,
+        "insurance_etf": 4,
+        "broker_etf": 6,
+        "realestate_etf": 4,
+        "military_etf": 6,
+        "metal_etf": 8,
+        "coal_etf": 7
+      }
+    },
+    "oil_price_surge": {
+      "description": "原油价格大涨",
+      "impact": {
+        "tech_etf": -3,
+        "ai_etf": -2,
+        "semiconductor_etf": -2,
+        "new_energy_vehicle_etf": 5,
+        "solar_etf": 6,
+        "pharma_etf": -1,
+        "consumer_etf": -4,
+        "bank_etf": -1,
+        "insurance_etf": -1,
+        "broker_etf": 0,
+        "realestate_etf": -3,
+        "military_etf": -1,
+        "metal_etf": 3,
+        "coal_etf": 8
+      }
+    },
+    "us_tech_weakness": {
+      "description": "美国科技股大跌",
+      "impact": {
+        "tech_etf": -6,
+        "ai_etf": -7,
+        "semiconductor_etf": -8,
+        "new_energy_vehicle_etf": -4,
+        "solar_etf": -3,
+        "pharma_etf": -2,
+        "consumer_etf": -3,
+        "bank_etf": 2,
+        "insurance_etf": 1,
+        "broker_etf": -2,
+        "realestate_etf": 1,
+        "military_etf": 0,
+        "metal_etf": 0,
+        "coal_etf": 1
+      }
+    }
+  },
+  
+  "etf_mapping": {
+    "tech_etf": ["515980", "159813"],
+    "ai_etf": ["515980", "159813"],
+    "semiconductor_etf": ["512480", "159995"],
+    "new_energy_vehicle_etf": ["515030", "159806"],
+    "solar_etf": ["515790", "159857"],
+    "pharma_etf": ["512010", "159938"],
+    "consumer_etf": ["159928", "510630"],
+    "bank_etf": ["510230"],
+    "insurance_etf": ["512880"],
+    "broker_etf": ["512000"],
+    "realestate_etf": ["512200", "159707"],
+    "military_etf": ["512660", "512810"],
+    "metal_etf": ["512400", "159881"],
+    "coal_etf": ["515220", "159678"]
+  },
+  
+  "composite_scenarios": {
+    "bull_market_start": {
+      "description": "牛市启动期 - 货币宽松 + 政策利好",
+      "factors": ["rate_cut", "fiscal_stimulus"],
+      "multiplier": 1.2
+    },
+    "bear_market_bottom": {
+      "description": "熊市底部 - 政策转向前",
+      "factors": ["rate_hike", "regulation_tightening"],
+      "multiplier": 0.8
+    },
+    "economic_recovery": {
+      "description": "经济复苏期",
+      "factors": ["pmi_expansion", "export_strong", "fiscal_stimulus"],
+      "multiplier": 1.15
+    },
+    "risk_off": {
+      "description": "风险规避模式",
+      "factors": ["us_tech_weakness", "currency_depreciation"],
+      "multiplier": 0.9
+    }
+  }
+}

core/world_book.py

@@ -0,0 +1,400 @@
+"""
+MND-IA Core: World Book (世界书)
+====================================
+这是系统的数据中心和逻辑基石，存储宏观状态、活跃叙事对象和历史经验。
+所有 Agent 必须通过此模块访问和更新系统状态。
+"""
+
+import json
+import os
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional, Any
+from pathlib import Path
+from copy import deepcopy
+
+
+class Narrative:
+    """单个叙事对象 (Narrative Object)"""
+    
+    def __init__(
+        self,
+        id: str,
+        topic: str,
+        related_etfs: List[str],
+        lifecycle_stage: str = "incubation",
+        base_score: float = 50.0,
+        decay_factor: float = 0.95,
+        current_weight: float = None
+    ):
+        self.id = id
+        self.topic = topic
+        self.related_etfs = related_etfs
+        self.lifecycle_stage = lifecycle_stage  # incubation | fermentation | realization | decay
+        self.base_score = base_score
+        self.decay_factor = decay_factor
+        self.current_weight = current_weight if current_weight is not None else base_score
+        self.last_updated = datetime.now().strftime("%Y-%m-%d")
+    
+    def decay(self) -> None:
+        """应用时间衰减"""
+        self.current_weight *= self.decay_factor
+        self.last_updated = datetime.now().strftime("%Y-%m-%d")
+    
+    def boost(self, new_score: float) -> None:
+        """新闻强化叙事权重"""
+        # 使用加权平均，新信息占40%，历史占60%
+        self.current_weight = self.current_weight * 0.6 + new_score * 0.4
+        self.last_updated = datetime.now().strftime("%Y-%m-%d")
+    
+    def update_stage(self, new_stage: str) -> None:
+        """更新生命周期阶段"""
+        valid_stages = ["incubation", "fermentation", "realization", "decay"]
+        if new_stage in valid_stages:
+            self.lifecycle_stage = new_stage
+            self.last_updated = datetime.now().strftime("%Y-%m-%d")
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典格式"""
+        return {
+            "id": self.id,
+            "topic": self.topic,
+            "related_etfs": self.related_etfs,
+            "lifecycle_stage": self.lifecycle_stage,
+            "base_score": self.base_score,
+            "decay_factor": self.decay_factor,
+            "current_weight": round(self.current_weight, 2),
+            "last_updated": self.last_updated
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> 'Narrative':
+        """从字典创建对象"""
+        return cls(
+            id=data["id"],
+            topic=data["topic"],
+            related_etfs=data["related_etfs"],
+            lifecycle_stage=data.get("lifecycle_stage", "incubation"),
+            base_score=data.get("base_score", 50.0),
+            decay_factor=data.get("decay_factor", 0.95),
+            current_weight=data.get("current_weight")
+        )
+
+
+class MacroCycle:
+    """宏观周期状态"""
+    
+    def __init__(
+        self,
+        status: str = "neutral",  # upward | downward | neutral
+        liquidity: str = "neutral",  # loose | neutral | tight
+        policy_wind: str = "wait_and_see"  # stimulus | regulation | wait_and_see
+    ):
+        self.status = status
+        self.liquidity = liquidity
+        self.policy_wind = policy_wind
+    
+    def to_dict(self) -> Dict[str, str]:
+        return {
+            "status": self.status,
+            "liquidity": self.liquidity,
+            "policy_wind": self.policy_wind
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, str]) -> 'MacroCycle':
+        return cls(
+            status=data.get("status", "neutral"),
+            liquidity=data.get("liquidity", "neutral"),
+            policy_wind=data.get("policy_wind", "wait_and_see")
+        )
+
+
+class WorldBook:
+    """
+    MCP World Book - 系统核心数据中心
+    ======================================
+    动态更新的状态管理器，存储：
+    1. 宏观周期状态
+    2. 活跃叙事对象列表
+    3. 宏观因子影响矩阵快照
+    """
+    
+    def __init__(self, data_dir: str = "data"):
+        self.data_dir = Path(data_dir)
+        self.data_dir.mkdir(exist_ok=True)
+        
+        self.world_book_path = self.data_dir / "world_book.json"
+        
+        # 初始化数据结构
+        self.timestamp = datetime.now().isoformat()
+        self.macro_cycle = MacroCycle()
+        self.active_narratives: Dict[str, Narrative] = {}
+        self.static_matrix_snapshot: Dict[str, Dict[str, float]] = {}
+        
+        # 新增: 宏观因子向量 (用于向量点积计算)
+        self.macro_factor_vector: Dict[str, float] = {}
+        
+        # 尝试加载已有数据
+        self.load()
+    
+    def load(self) -> bool:
+        """从磁盘加载 World Book"""
+        if not self.world_book_path.exists():
+            print(f"[WorldBook] 未找到已有数据，初始化新的 World Book")
+            self._load_static_matrix()
+            return False
+        
+        try:
+            with open(self.world_book_path, 'r', encoding='utf-8') as f:
+                data = json.load(f)
+            
+            self.timestamp = data.get("timestamp", datetime.now().isoformat())
+            self.macro_cycle = MacroCycle.from_dict(data.get("macro_cycle", {}))
+            
+            # 加载叙事对象
+            self.active_narratives = {}
+            for narrative_data in data.get("active_narratives", []):
+                narrative = Narrative.from_dict(narrative_data)
+                self.active_narratives[narrative.id] = narrative
+            
+            # 加载矩阵快照
+            self.static_matrix_snapshot = data.get("static_matrix_snapshot", {})
+            
+            # 加载宏观因子向量
+            self.macro_factor_vector = data.get("macro_factor_vector", {})
+            
+            print(f"[WorldBook] 成功加载数据，包含 {len(self.active_narratives)} 个活跃叙事")
+            return True
+            
+        except Exception as e:
+            print(f"[WorldBook] 加载数据失败: {e}")
+            self._load_static_matrix()
+            return False
+    
+    def save(self) -> bool:
+        """保存 World Book 到磁盘"""
+        try:
+            self.timestamp = datetime.now().isoformat()
+            
+            data = {
+                "timestamp": self.timestamp,
+                "macro_cycle": self.macro_cycle.to_dict(),
+                "active_narratives": [
+                    narrative.to_dict() 
+                    for narrative in self.active_narratives.values()
+                ],
+                "static_matrix_snapshot": self.static_matrix_snapshot,
+                "macro_factor_vector": self.macro_factor_vector
+            }
+            
+            with open(self.world_book_path, 'w', encoding='utf-8') as f:
+                json.dump(data, f, ensure_ascii=False, indent=2)
+            
+            print(f"[WorldBook] 成功保存数据到 {self.world_book_path}")
+            return True
+            
+        except Exception as e:
+            print(f"[WorldBook] 保存数据失败: {e}")
+            return False
+    
+    def _load_static_matrix(self) -> None:
+        """加载静态宏观矩阵"""
+        matrix_path = Path("core") / "macro_matrix.json"
+        if matrix_path.exists():
+            try:
+                with open(matrix_path, 'r', encoding='utf-8') as f:
+                    self.static_matrix_snapshot = json.load(f)
+                print(f"[WorldBook] 加载宏观矩阵成功")
+            except Exception as e:
+                print(f"[WorldBook] 加载宏观矩阵失败: {e}")
+                self.static_matrix_snapshot = {}
+    
+    def add_narrative(self, narrative: Narrative) -> None:
+        """添加新叙事"""
+        self.active_narratives[narrative.id] = narrative
+        print(f"[WorldBook] 添加新叙事: {narrative.topic} (ID: {narrative.id})")
+    
+    def update_narrative(self, narrative_id: str, **kwargs) -> bool:
+        """更新叙事属性"""
+        if narrative_id not in self.active_narratives:
+            print(f"[WorldBook] 叙事不存在: {narrative_id}")
+            return False
+        
+        narrative = self.active_narratives[narrative_id]
+        
+        if 'new_score' in kwargs:
+            narrative.boost(kwargs['new_score'])
+        if 'stage' in kwargs:
+            narrative.update_stage(kwargs['stage'])
+        
+        return True
+    
+    def decay_all_narratives(self) -> None:
+        """对所有叙事应用时间衰减"""
+        for narrative in self.active_narratives.values():
+            narrative.decay()
+        print(f"[WorldBook] 对 {len(self.active_narratives)} 个叙事应用了时间衰减")
+    
+    def remove_weak_narratives(self, threshold: float = 10.0) -> List[str]:
+        """移除权重过低的叙事"""
+        to_remove = [
+            nid for nid, narrative in self.active_narratives.items()
+            if narrative.current_weight < threshold
+        ]
+        
+        for nid in to_remove:
+            topic = self.active_narratives[nid].topic
+            del self.active_narratives[nid]
+            print(f"[WorldBook] 移除衰退叙事: {topic} (ID: {nid})")
+        
+        return to_remove
+    
+    def get_narrative_by_topic(self, topic: str) -> Optional[Narrative]:
+        """根据主题获取叙事"""
+        for narrative in self.active_narratives.values():
+            if narrative.topic == topic:
+                return narrative
+        return None
+    
+    def get_narratives_by_etf(self, etf_code: str) -> List[Narrative]:
+        """获取与特定 ETF 相关的所有叙事"""
+        return [
+            narrative for narrative in self.active_narratives.values()
+            if etf_code in narrative.related_etfs
+        ]
+    
+    def update_macro_cycle(self, **kwargs) -> None:
+        """更新宏观周期状态"""
+        if 'status' in kwargs:
+            self.macro_cycle.status = kwargs['status']
+        if 'liquidity' in kwargs:
+            self.macro_cycle.liquidity = kwargs['liquidity']
+        if 'policy_wind' in kwargs:
+            self.macro_cycle.policy_wind = kwargs['policy_wind']
+        
+        print(f"[WorldBook] 宏观周期更新: {self.macro_cycle.to_dict()}")
+    
+    def update_macro_factor_vector(self, factor_updates: Dict[str, float]) -> None:
+        """
+        更新宏观因子向量
+        
+        Args:
+            factor_updates: 宏观因子及其强度，例如:
+                {
+                    "interest_rate_down": 1.0,  # 强烈降息预期
+                    "geopolitics_tension": 0.5,  # 中等地缘紧张
+                    "policy_digital_economy": 1.0  # 数字经济政策头条
+                }
+        """
+        self.macro_factor_vector.update(factor_updates)
+        print(f"[WorldBook] 宏观因子向量更新: {self.macro_factor_vector}")
+    
+    def get_macro_factor_value(self, factor_name: str) -> float:
+        """获取特定宏观因子的当前值"""
+        return self.macro_factor_vector.get(factor_name, 0.0)
+    
+    def clear_macro_factor_vector(self) -> None:
+        """清空宏观因子向量（通常在每日开盘前重置）"""
+        self.macro_factor_vector = {}
+        print("[WorldBook] 宏观因子向量已清空")
+    
+    def get_top_narratives(self, top_n: int = 5) -> List[Narrative]:
+        """获取权重最高的 N 个叙事"""
+        sorted_narratives = sorted(
+            self.active_narratives.values(),
+            key=lambda n: n.current_weight,
+            reverse=True
+        )
+        return sorted_narratives[:top_n]
+    
+    def export_snapshot(self) -> Dict[str, Any]:
+        """导出完整快照（供其他 Agent 读取）"""
+        return {
+            "timestamp": self.timestamp,
+            "macro_cycle": self.macro_cycle.to_dict(),
+            "macro_factor_vector": self.macro_factor_vector,
+            "active_narratives": [n.to_dict() for n in self.active_narratives.values()],
+            "static_matrix_snapshot": self.static_matrix_snapshot,
+            "summary": {
+                "total_narratives": len(self.active_narratives),
+                "top_3_topics": [n.topic for n in self.get_top_narratives(3)]
+            }
+        }
+    
+    def __repr__(self) -> str:
+        return (
+            f"<WorldBook: {len(self.active_narratives)} narratives, "
+            f"macro={self.macro_cycle.status}, "
+            f"updated={self.timestamp}>"
+        )
+
+
+# ==================== 工具函数 ====================
+
+def create_narrative_id(topic: str, date: Optional[str] = None) -> str:
+    """生成叙事 ID"""
+    if date is None:
+        date = datetime.now().strftime("%Y%m")
+    
+    # 简化主题名称作为 ID 的一部分
+    topic_slug = topic.replace(" ", "_").replace("，", "_").replace("、", "_")
+    return f"narrative_{topic_slug}_{date}"
+
+
+if __name__ == "__main__":
+    # 测试代码
+    print("=" * 50)
+    print("World Book 核心模块测试")
+    print("=" * 50)
+    
+    # 创建 World Book 实例
+    wb = WorldBook(data_dir="data")
+    
+    # 添加测试叙事
+    narrative1 = Narrative(
+        id=create_narrative_id("低空经济"),
+        topic="低空经济",
+        related_etfs=["512980", "159969"],
+        lifecycle_stage="fermentation",
+        base_score=85.0,
+        decay_factor=0.95
+    )
+    
+    narrative2 = Narrative(
+        id=create_narrative_id("AI算力"),
+        topic="AI算力",
+        related_etfs=["515980", "159813"],
+        lifecycle_stage="realization",
+        base_score=92.0,
+        decay_factor=0.93
+    )
+    
+    wb.add_narrative(narrative1)
+    wb.add_narrative(narrative2)
+    
+    # 更新宏观周期
+    wb.update_macro_cycle(
+        status="upward",
+        liquidity="loose",
+        policy_wind="stimulus"
+    )
+    
+    # 保存数据
+    wb.save()
+    
+    # 测试导出快照
+    snapshot = wb.export_snapshot()
+    print("\n当前状态快照:")
+    print(json.dumps(snapshot, ensure_ascii=False, indent=2))
+    
+    # 测试衰减
+    print("\n应用时间衰减...")
+    wb.decay_all_narratives()
+    
+    # 测试获取 Top 叙事
+    top = wb.get_top_narratives(2)
+    print(f"\nTop 2 叙事:")
+    for n in top:
+        print(f"  - {n.topic}: {n.current_weight:.2f}")
+    
+    print("\n✅ World Book 核心模块测试完成")