cfdaily
386 lines
14 KiB
Python
386 lines
14 KiB
Python
"""
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资金流向监控系统
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功能：
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1. 北向资金每日流向监控
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2. 北向资金板块流向统计
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3. 主力资金流向监控
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4. 资金流量化因子构建
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5. 资金背离信号预警（指数涨资金跌/指数跌资金涨）
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Author: 关羽（云长）
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Date: 2026-03-27
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"""
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from dataclasses import dataclass
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from typing import List, Dict, Optional, Tuple
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import pandas as pd
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import numpy as np
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@dataclass
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class CapitalFlowData:
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"""单日资金流向数据"""
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date: str
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# 北向资金
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northbound_total: float # 当日北向净流入（亿）
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northbound_5d_avg: float # 5日平均净流入
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northbound_20d_avg: float # 20日平均净流入
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# 主力资金
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main_force_net: float # 当日主力净流入（亿）
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main_force_5d_avg: float
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main_force_20d_avg: float
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# 板块数据
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sector_northbound_flow: Dict[str, float] # 板块北向净流入
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sector_main_flow: Dict[str, float] # 板块主力净流入
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@dataclass
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class CapitalFlowStock:
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"""个股资金流向"""
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code: str
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name: str
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sector: str
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# 资金数据
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northbound_hold_change: float # 北向持仓变化（周%）
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main_force_flow_5d: float # 5日主力净流入（亿）
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main_force_flow_20d: float # 20日主力净流入
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turnover_rate: float # 换手率
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# 价格数据
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pct_change_5d: float # 5日涨跌幅%
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class CapitalFlowConfig:
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"""资金监控参数配置"""
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def __init__(self,
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northbound_bull_threshold: float = 20.0, # 单日北向净流入超20亿算强势
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northbound_bear_threshold: float = -20.0, # 单日净流出超20亿算弱势
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consecutive_bull_days: int = 3, # 连续3天净流入算趋势转强
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consecutive_bear_days: int = 3, # 连续3天净流出算趋势转弱
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main_flow_deviation_threshold: float = -0.5, # 资金背离阈值：价涨资金跌
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hot_sector_flow_pct_threshold: float = 0.3): # 板块资金占比超30%算热点
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self.northbound_bull_threshold = northbound_bull_threshold
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self.northbound_bear_threshold = northbound_bear_threshold
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self.consecutive_bull_days = consecutive_bull_days
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self.consecutive_bear_days = consecutive_bear_days
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self.main_flow_deviation_threshold = main_flow_deviation_threshold
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self.hot_sector_flow_pct_threshold = hot_sector_flow_pct_threshold
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default_config = CapitalFlowConfig()
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class NorthboundMonitor:
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"""北向资金监控"""
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def __init__(self, config: CapitalFlowConfig = None):
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self.config = config or default_config
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self.history: List[CapitalFlowData] = []
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def add_daily_data(self, data: CapitalFlowData):
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"""添加每日数据"""
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self.history.append(data)
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# 按日期排序
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self.history.sort(key=lambda x: x.date)
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def get_trend(self) -> str:
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"""判断北向资金整体趋势"""
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if len(self.history) < self.config.consecutive_bull_days:
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return "neutral"
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recent = self.history[-self.config.consecutive_bull_days:]
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bull_days = sum(1 for d in recent if d.northbound_total > self.config.northbound_bull_threshold)
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bear_days = sum(1 for d in recent if d.northbound_total < self.config.northbound_bear_threshold)
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if bull_days >= self.config.consecutive_bull_days:
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return "strong_bull"
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elif bear_days >= self.config.consecutive_bear_days:
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return "strong_bear"
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elif np.mean([d.northbound_total for d in recent]) > 0:
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return "weak_bull"
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else:
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return "weak_bear"
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def get_sector_rank(self) -> List[Tuple[str, float]]:
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"""获取板块北向资金流入排名"""
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if not self.history:
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return []
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latest = self.history[-1]
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sectors = latest.sector_northbound_flow
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sorted_sectors = sorted(sectors.items(), key=lambda x: x[1], reverse=True)
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return sorted_sectors
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def get_hot_sectors(self) -> List[str]:
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"""获取当前热点板块（北向资金集中流入）"""
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ranked = self.get_sector_rank()
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if not ranked:
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return []
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total_inflow = sum(s[1] for s in ranked if s[1] > 0)
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if total_inflow <= 0:
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return []
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hot = []
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cumulative = 0
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for name, flow in ranked:
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if flow <= 0:
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break
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pct = flow / total_inflow
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cumulative += pct
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hot.append(name)
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if cumulative >= self.config.hot_sector_flow_pct_threshold:
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break
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return hot
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def get_trend_signal(self) -> Tuple[str, str]:
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"""获取趋势信号和说明"""
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trend = self.get_trend()
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signal_map = {
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"strong_bull": ("🔼 强烈看多", "北向连续三日净流入超20亿，趋势转强"),
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"weak_bull": ("▶️ 偏多", "北向整体净流入，趋势偏多"),
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"neutral": ("➖ 中性", "北向资金没有明确趋势"),
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"weak_bear": ("◀️ 偏空", "北向整体净流出，趋势偏空"),
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"strong_bear": ("🔽 强烈看空", "北向连续三日净流出超20亿，趋势转弱")
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}
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return signal_map[trend]
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class MainForceMonitor:
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"""主力资金监控"""
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def __init__(self, config: CapitalFlowConfig = None):
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self.config = config or default_config
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def check_deviation(self, index_pct_5d: float, main_flow_5d: float) -> bool:
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"""
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检查资金背离
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指数涨但主力资金净流出 → 顶背离，风险预警
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"""
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if index_pct_5d > 5 and main_flow_5d < self.config.main_flow_deviation_threshold * abs(index_pct_5d):
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return True # 背离，预警
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return False
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def rank_stocks_by_main_flow(self, stocks: List[CapitalFlowStock]) -> Tuple[List[str], List[str]]:
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"""对个股按主力资金流排名，返回强流入和强流出列表"""
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sorted_stocks = sorted(stocks, key=lambda x: x.main_force_flow_5d, reverse=True)
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# 前20%强流入
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top_count = max(len(sorted_stocks) // 5, 1)
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top_codes = [s.code for s in sorted_stocks[:top_count]]
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# 后20%强流出
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bottom_codes = [s.code for s in sorted_stocks[-top_count:]]
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return top_codes, bottom_codes
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def check_sector_main_trend(self, flow_data: Dict[str, float]) -> List[Tuple[str, str]]:
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"""检查板块主力资金趋势"""
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result = []
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for sector, flow in flow_data.items():
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if flow > 10:
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result.append((sector, "bull"))
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elif flow < -10:
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result.append((sector, "bear"))
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return result
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class CapitalFlowFactorBuilder:
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"""资金流量化因子构建"""
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@staticmethod
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def build_northbound_factor(stock: CapitalFlowStock) -> float:
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"""
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北向资金因子
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周度北向持仓变化，归一化到 0~1，越高越好
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"""
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change = stock.northbound_hold_change
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# 归一化：-5% → 0，+5% → 1
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factor = (change + 5.0) / 10.0
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return max(0.0, min(1.0, factor))
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@staticmethod
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def build_main_force_factor(stock: CapitalFlowStock) -> float:
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"""
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主力资金因子
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5日主力净流入/流通市值，归一化到 0~1
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"""
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# 假设流通市值大概100亿，5日净流入超5亿算满分
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flow = stock.main_force_flow_5d
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factor = (flow + 5.0) / 10.0
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return max(0.0, min(1.0, factor))
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@staticmethod
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def build_turnover_factor(stock: CapitalFlowStock) -> float:
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"""
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换手率因子
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换手率适中最好，过低没流动性，过高太疯狂
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最优区间：2%~8%
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"""
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tr = stock.turnover_rate
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if tr < 1:
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return 0.2 + tr * 0.3 # 0 → 0.2，1 → 0.5
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elif tr <= 8:
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return 0.8 - abs(tr - 5) * 0.05 # 5% → 0.8
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else:
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return max(0.2, 1.0 - (tr - 8) * 0.05)
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@staticmethod
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def build_combined_factor(stock: CapitalFlowStock) -> float:
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"""综合资金因子"""
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nf = CapitalFlowFactorBuilder.build_northbound_factor(stock)
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mf = CapitalFlowFactorBuilder.build_main_force_factor(stock)
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tf = CapitalFlowFactorBuilder.build_turnover_factor(stock)
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# 加权：北向40%，主力40%，换手率20%
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combined = nf * 0.4 + mf * 0.4 + tf * 0.2
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return combined
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class CapitalFlowRiskMonitor:
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"""资金流向风险预警"""
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def __init__(self, config: CapitalFlowConfig = None):
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self.config = config or default_config
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self.northbound_monitor = NorthboundMonitor(config)
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def check_systemic_risk(self, index_pct_5d: float, total_main_flow_5d: float) -> Tuple[bool, str]:
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"""检查系统性风险"""
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# 指数涨但主力资金大规模流出 → 风险
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if self.northbound_monitor.get_trend() == "strong_bear":
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return True, "北向资金连续大幅流出，系统性风险上升"
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# 指数涨主力跌 → 背离
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if index_pct_5d > 5 and total_main_flow_5d < 0:
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return True, f"指数5日涨{index_pct_5d:.1f}%但主力资金净流出，顶背离风险"
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return False, ""
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class CapitalFlowMonitor:
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"""总资金流向监控器"""
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def __init__(self, config: CapitalFlowConfig = None):
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self.config = config or default_config
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self.northbound = NorthboundMonitor(config)
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self.main_force = MainForceMonitor(config)
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self.risk = CapitalFlowRiskMonitor(config)
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self.factor_builder = CapitalFlowFactorBuilder()
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def get_daily_report(self,
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data: CapitalFlowData,
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stocks: List[CapitalFlowStock],
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index_pct_5d: float) -> str:
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"""生成每日资金流向报告"""
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self.northbound.add_daily_data(data)
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lines = []
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lines.append("=" * 60)
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lines.append("资金流向监控日报")
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lines.append("=" * 60)
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# 北向趋势
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trend, desc = self.northbound.get_trend_signal()
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lines.append(f"北向资金趋势: {trend} → {desc}")
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lines.append(f"最近一日净流入: {data.northbound_total:.1f}亿")
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lines.append(f"5日均: {data.northbound_5d_avg:.1f}亿 20日均: {data.northbound_20d_avg:.1f}亿")
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lines.append("")
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# 北向板块排名
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hot_sectors = self.northbound.get_hot_sectors()
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if hot_sectors:
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lines.append(f"🔼 北向资金集中流入板块: {', '.join(hot_sectors)}")
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lines.append("")
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# 主力资金板块
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sector_trend = self.main_force.check_sector_main_trend(data.sector_main_flow)
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bull_sectors = [s[0] for s in sector_trend if s[1] == "bull"]
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bear_sectors = [s[0] for s in sector_trend if s[1] == "bear"]
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if bull_sectors:
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lines.append(f"✅ 主力资金净流入板块: {', '.join(bull_sectors)}")
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if bear_sectors:
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lines.append(f"⚠️ 主力资金净流出板块: {', '.join(bear_sectors)}")
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lines.append("")
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# 风险检查
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has_risk, reason = self.risk.check_systemic_risk(index_pct_5d, data.main_force_5d_avg)
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if has_risk:
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lines.append(f"⚠️ 风险预警: {reason}")
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else:
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lines.append("✅ 无明显系统性风险")
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# 个股资金因子示例
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if stocks:
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top_stocks = sorted(stocks, key=lambda x: self.factor_builder.build_combined_factor(x), reverse=True)[:5]
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lines.append("")
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lines.append("💯 综合资金因子前五名:")
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for s in top_stocks:
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f = self.factor_builder.build_combined_factor(s)
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lines.append(f" {s.code} {s.name} 因子得分: {f:.2f}")
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lines.append("=" * 60)
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return "\n".join(lines)
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if __name__ == "__main__":
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print("=== 测试资金流向监控模块 ===\n")
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# 测试北向监控
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from datetime import datetime, timedelta
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monitor = CapitalFlowMonitor()
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# 添加最近几天数据
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dates = ["2026-03-23", "2026-03-24", "2026-03-25", "2026-03-26", "2026-03-27"]
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flows = [25, 32, 18, -10, -28]
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for date, flow in zip(dates, flows):
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data = CapitalFlowData(
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date=date,
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northbound_total=flow,
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northbound_5d_avg=np.mean(flows[-5:]),
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northbound_20d_avg=np.mean(flows[-20:]) if len(flows)>=20 else np.mean(flows),
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main_force_net=flow * 2,
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main_force_5d_avg=np.mean([f*2 for f in flows[-5:]]),
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main_force_20d_avg=np.mean([f*2 for f in flows]),
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sector_northbound_flow={"AI": 45, "新能源": -15, "消费": 8, "金融": -5},
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sector_main_flow={"AI": 60, "新能源": -20, "消费": 12}
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)
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monitor.northbound.add_daily_data(data)
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# 测试个股
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stocks = [
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CapitalFlowStock(
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code="600000", name="浦发银行", sector="银行",
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northbound_hold_change=1.2,
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main_force_flow_5d=2.5,
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main_force_flow_20d=8.0,
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turnover_rate=3.5,
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pct_change_5d=2.1
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),
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CapitalFlowStock(
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code="002XXX", name="AI龙头", sector="AI",
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northbound_hold_change=3.5,
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main_force_flow_5d=8.5,
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main_force_flow_20d=25.0,
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turnover_rate=5.2,
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pct_change_5d=8.5
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),
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CapitalFlowStock(
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code="601XXX", name="周期股", sector="周期",
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northbound_hold_change=-2.1,
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main_force_flow_5d=-5.2,
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main_force_flow_20d=-15.0,
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turnover_rate=12.5,
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pct_change_5d=-6.2
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)
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]
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# 生成报告
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print(monitor.get_daily_report(data, stocks, 2.5))
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# 测试因子构建
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print("\n=== 测试资金因子构建 ===")
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for s in stocks:
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f = monitor.factor_builder.build_combined_factor(s)
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print(f"{s.name}: 综合因子得分 {f:.2f}")
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