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# 资金监控模块
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import pandas as pd
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import numpy as np
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from typing import Dict, Optional, List
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from qihuo_analyzer.core.models import StrategyConfig
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class FundFlowMonitor:
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"""资金流向监控器"""
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def __init__(self, config: Optional[StrategyConfig] = None):
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self.config = config or StrategyConfig()
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def analyze_fund_flow(self, data: pd.DataFrame) -> Dict:
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"""分析资金流向"""
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result = {}
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# 分析持仓量变化
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oi_analysis = self._analyze_open_interest(data)
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result.update(oi_analysis)
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# 分析量价关系
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volume_price_analysis = self._analyze_volume_price_relationship(data)
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result.update(volume_price_analysis)
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# 分析资金流向强度
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fund_flow_strength = self._calculate_fund_flow_strength(data)
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result['fund_flow_strength'] = fund_flow_strength
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# 分析资金集中度
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fund_concentration = self._analyze_fund_concentration(data)
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result.update(fund_concentration)
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# 综合资金面信号
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fund_signal = self._generate_fund_signal(result)
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result['fund_signal'] = fund_signal
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return result
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def _analyze_open_interest(self, data: pd.DataFrame) -> Dict:
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"""分析持仓量变化"""
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# 计算持仓量变化
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data['oi_change'] = data['open_interest'].diff()
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data['oi_change_pct'] = data['oi_change'] / data['open_interest'].shift(1) * 100
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# 最近N天持仓量变化
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recent_oi_change = data['oi_change'].tail(5).sum()
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recent_oi_change_pct = data['oi_change_pct'].tail(5).mean()
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# 持仓量趋势
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oi_trend = self._judge_oi_trend(data['open_interest'])
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# 持仓量与价格关系
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oi_price_relationship = self._judge_oi_price_relationship(data)
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return {
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'recent_oi_change': recent_oi_change,
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'recent_oi_change_pct': recent_oi_change_pct,
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'oi_trend': oi_trend,
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'oi_price_relationship': oi_price_relationship
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}
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def _analyze_volume_price_relationship(self, data: pd.DataFrame) -> Dict:
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"""分析量价关系"""
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# 计算价格变化
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data['price_change'] = data['close'].diff()
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data['price_change_pct'] = data['price_change'] / data['close'].shift(1) * 100
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# 计算成交量变化
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data['volume_change'] = data['volume'].diff()
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data['volume_change_pct'] = data['volume_change'] / data['volume'].shift(1) * 100
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# 量价配合度
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volume_price_fit = self._calculate_volume_price_fit(data)
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# 量价背离检测
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divergence = self._detect_volume_price_divergence(data)
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# 成交量趋势
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volume_trend = self._judge_volume_trend(data['volume'])
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return {
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'volume_price_fit': volume_price_fit,
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'divergence': divergence,
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'volume_trend': volume_trend
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}
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def _calculate_fund_flow_strength(self, data: pd.DataFrame) -> float:
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"""计算资金流向强度"""
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# 计算资金流向
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# 简化计算：(收盘价 - 开盘价) * 成交量
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fund_flow = ((data['close'] - data['open']) * data['volume']).tail(20).sum()
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# 归一化到-100到100
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if fund_flow > 0:
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strength = min(100, (fund_flow / data['volume'].tail(20).sum()) * 1000)
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else:
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strength = max(-100, (fund_flow / data['volume'].tail(20).sum()) * 1000)
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return strength
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def _analyze_fund_concentration(self, data: pd.DataFrame) -> Dict:
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"""分析资金集中度"""
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# 计算成交量集中度（前5天成交量占比）
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recent_volume = data['volume'].tail(5).sum()
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total_volume = data['volume'].tail(30).sum()
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volume_concentration = recent_volume / total_volume if total_volume > 0 else 0
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# 计算持仓量集中度（最近持仓量变化占比）
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recent_oi_change = abs(data['oi_change'].tail(5).sum())
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total_oi = data['open_interest'].iloc[-1]
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oi_concentration = recent_oi_change / total_oi if total_oi > 0 else 0
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return {
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'volume_concentration': volume_concentration,
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'oi_concentration': oi_concentration
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}
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def _judge_oi_trend(self, oi_series: pd.Series) -> str:
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"""判断持仓量趋势"""
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# 使用简单移动平均线判断趋势
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ma5 = oi_series.rolling(window=5).mean().iloc[-1]
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ma20 = oi_series.rolling(window=20).mean().iloc[-1]
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if ma5 > ma20 * 1.02:
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return 'strong_increasing'
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elif ma5 > ma20:
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return 'increasing'
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elif ma5 < ma20 * 0.98:
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return 'strong_decreasing'
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elif ma5 < ma20:
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return 'decreasing'
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else:
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return 'stable'
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def _judge_oi_price_relationship(self, data: pd.DataFrame) -> Dict:
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"""判断持仓量与价格关系"""
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recent_data = data.tail(10)
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# 计算价格变化
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if 'price_change' not in recent_data.columns:
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recent_data['price_change'] = recent_data['close'].diff()
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# 计算持仓量变化
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if 'oi_change' not in recent_data.columns:
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recent_data['oi_change'] = recent_data['open_interest'].diff()
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# 计算平均价格变化和平均持仓量变化
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avg_price_change = recent_data['price_change'].mean()
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avg_oi_change = recent_data['oi_change'].mean()
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if avg_price_change > 0 and avg_oi_change > 0:
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return 'price_up_oi_up' # 价涨量增
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elif avg_price_change > 0 and avg_oi_change < 0:
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return 'price_up_oi_down' # 价涨量减
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elif avg_price_change < 0 and avg_oi_change > 0:
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return 'price_down_oi_up' # 价跌量增
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elif avg_price_change < 0 and avg_oi_change < 0:
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return 'price_down_oi_down' # 价跌量减
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else:
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return 'stable'
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def _calculate_volume_price_fit(self, data: pd.DataFrame) -> float:
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"""计算量价配合度"""
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recent_data = data.tail(20)
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# 计算量价配合的次数
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fit_count = 0
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total_count = len(recent_data) - 1
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for i in range(1, len(recent_data)):
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price_change = recent_data['price_change'].iloc[i]
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volume_change = recent_data['volume_change'].iloc[i]
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# 量价配合：价格上涨成交量增加，价格下跌成交量减少
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if (price_change > 0 and volume_change > 0) or (price_change < 0 and volume_change < 0):
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fit_count += 1
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fit_ratio = fit_count / total_count if total_count > 0 else 0
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return fit_ratio * 100
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def _detect_volume_price_divergence(self, data: pd.DataFrame) -> str:
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"""检测量价背离"""
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recent_data = data.tail(10)
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# 计算价格趋势（斜率）
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price_slope = np.polyfit(range(len(recent_data)), recent_data['close'], 1)[0]
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# 计算成交量趋势（斜率）
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volume_slope = np.polyfit(range(len(recent_data)), recent_data['volume'], 1)[0]
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# 判断背离
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if price_slope > 0 and volume_slope < 0:
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return 'bearish_divergence' # 价格上涨，成交量下降，看跌背离
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elif price_slope < 0 and volume_slope > 0:
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return 'bullish_divergence' # 价格下降，成交量上升，看涨背离
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else:
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return 'no_divergence'
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def _judge_volume_trend(self, volume_series: pd.Series) -> str:
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"""判断成交量趋势"""
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ma5 = volume_series.rolling(window=5).mean().iloc[-1]
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ma20 = volume_series.rolling(window=20).mean().iloc[-1]
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if ma5 > ma20 * 1.1:
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return 'strong_increasing'
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elif ma5 > ma20:
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return 'increasing'
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elif ma5 < ma20 * 0.9:
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return 'strong_decreasing'
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elif ma5 < ma20:
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return 'decreasing'
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else:
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return 'stable'
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def _generate_fund_signal(self, fund_analysis: Dict) -> str:
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"""生成资金面信号"""
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signals = []
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# 持仓量信号
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if fund_analysis.get('oi_trend') in ['strong_increasing', 'increasing']:
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if fund_analysis.get('oi_price_relationship') == 'price_up_oi_up':
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signals.append('bullish')
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elif fund_analysis.get('oi_price_relationship') == 'price_down_oi_up':
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signals.append('bearish')
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# 量价关系信号
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if fund_analysis.get('volume_price_fit') > 60:
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if fund_analysis.get('volume_trend') in ['strong_increasing', 'increasing']:
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signals.append('bullish')
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# 量价背离信号
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if fund_analysis.get('divergence') == 'bullish_divergence':
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signals.append('bullish')
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elif fund_analysis.get('divergence') == 'bearish_divergence':
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signals.append('bearish')
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# 资金流向强度信号
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fund_flow_strength = fund_analysis.get('fund_flow_strength', 0)
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if fund_flow_strength > 30:
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signals.append('bullish')
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elif fund_flow_strength < -30:
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signals.append('bearish')
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# 综合信号
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if signals.count('bullish') > signals.count('bearish'):
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return 'bullish'
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elif signals.count('bearish') > signals.count('bullish'):
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return 'bearish'
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else:
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return 'neutral'
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def detect_volume_spikes(self, data: pd.DataFrame, threshold: float = 2.0) -> List[int]:
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"""检测成交量异动"""
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# 计算成交量移动平均线和标准差
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data['volume_ma'] = data['volume'].rolling(window=20).mean()
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data['volume_std'] = data['volume'].rolling(window=20).std()
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# 计算成交量偏离度
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data['volume_zscore'] = (data['volume'] - data['volume_ma']) / data['volume_std']
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# 找出成交量异动的位置
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spikes = data[data['volume_zscore'] > threshold].index
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return list(spikes)
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def analyze_institutional_activity(self, data: pd.DataFrame) -> Dict:
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"""分析机构活动"""
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# 基于持仓量和成交量的变化分析机构活动
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# 机构通常会引起较大的持仓量变化
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# 计算大资金活动指标
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data['institutional_activity'] = data['oi_change'] * abs(data['price_change'])
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# 最近机构活动强度
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recent_institutional_activity = data['institutional_activity'].tail(5).sum()
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# 机构活动趋势
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institutional_trend = 'increasing' if recent_institutional_activity > 0 else 'decreasing'
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return {
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'recent_institutional_activity': recent_institutional_activity,
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'institutional_trend': institutional_trend
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}
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