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