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# 换月预警模块
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Tuple
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class RolloverDetector:
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"""换月预警检测器"""
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def __init__(self):
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pass
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def analyze_rollover(self, symbol: str, data: pd.DataFrame, contract_info: Optional[Dict] = None) -> Dict:
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"""分析换月情况"""
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result = {}
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# 检测交割日
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delivery_info = self._detect_delivery_date(symbol, contract_info)
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result.update(delivery_info)
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# 分析流动性
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liquidity_analysis = self._analyze_liquidity(data)
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result.update(liquidity_analysis)
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# 分析价差
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spread_analysis = self._analyze_spread(symbol)
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result.update(spread_analysis)
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# 生成换月预警
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rollover_warning = self._generate_rollover_warning(delivery_info, liquidity_analysis)
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result['rollover_warning'] = rollover_warning
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# 生成减仓建议
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position_adjustment = self._generate_position_adjustment(delivery_info, liquidity_analysis)
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result['position_adjustment'] = position_adjustment
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return result
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def _detect_delivery_date(self, symbol: str, contract_info: Optional[Dict] = None) -> Dict:
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"""检测交割日"""
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if contract_info and 'expire_datetime' in contract_info:
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# 使用合约信息中的交割日
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expire_timestamp = contract_info['expire_datetime']
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if isinstance(expire_timestamp, int):
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# 处理纳秒时间戳
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if expire_timestamp > 1e15:
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expire_date = datetime.fromtimestamp(expire_timestamp / 1e9)
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else:
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expire_date = datetime.fromtimestamp(expire_timestamp)
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else:
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expire_date = pd.to_datetime(expire_timestamp)
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else:
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# 基于合约代码推断交割日
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expire_date = self._infer_delivery_date(symbol)
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# 计算距离交割日的天数
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today = datetime.now()
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days_to_delivery = (expire_date - today).days
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# 确定换月预警级别
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warning_level = self._calculate_warning_level(days_to_delivery)
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return {
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'expire_date': expire_date.strftime('%Y-%m-%d'),
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'days_to_delivery': days_to_delivery,
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'warning_level': warning_level
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}
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def _infer_delivery_date(self, symbol: str) -> datetime:
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"""基于合约代码推断交割日"""
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# 简化的合约代码解析
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# 假设合约代码格式为：品种+年份+月份，如 'CU2309'
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try:
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# 提取年份和月份
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year_str = symbol[-4:-2]
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month_str = symbol[-2:]
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# 构建年份（加上世纪）
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year = 2000 + int(year_str)
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month = int(month_str)
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# 假设交割日为合约月份的15日
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# 实际交割日可能因品种而异，这里使用简化处理
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expire_date = datetime(year, month, 15)
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return expire_date
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except Exception:
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# 如果解析失败，返回60天后的日期
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return datetime.now() + timedelta(days=60)
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def _calculate_warning_level(self, days_to_delivery: int) -> str:
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"""计算换月预警级别"""
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if days_to_delivery <= 3:
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return 'critical'
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elif days_to_delivery <= 7:
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return 'high'
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elif days_to_delivery <= 15:
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return 'medium'
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elif days_to_delivery <= 30:
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return 'low'
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else:
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return 'none'
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def _analyze_liquidity(self, data: pd.DataFrame) -> Dict:
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"""分析流动性"""
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# 计算成交量指标
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avg_volume = data['volume'].tail(20).mean()
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volume_trend = self._analyze_volume_trend(data['volume'])
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# 计算持仓量指标
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avg_open_interest = data['open_interest'].tail(20).mean()
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oi_trend = self._analyze_oi_trend(data['open_interest'])
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# 计算买卖价差（简化处理）
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# 实际应该使用Tick数据计算
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bid_ask_spread = self._estimate_bid_ask_spread(data)
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# 计算流动性评分
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liquidity_score = self._calculate_liquidity_score(avg_volume, volume_trend, avg_open_interest, oi_trend, bid_ask_spread)
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# 确定流动性风险级别
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liquidity_risk = self._calculate_liquidity_risk(liquidity_score)
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return {
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'avg_volume': avg_volume,
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'volume_trend': volume_trend,
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'avg_open_interest': avg_open_interest,
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'oi_trend': oi_trend,
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'bid_ask_spread': bid_ask_spread,
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'liquidity_score': liquidity_score,
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'liquidity_risk': liquidity_risk
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}
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def _analyze_volume_trend(self, volume_series: pd.Series) -> str:
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"""分析成交量趋势"""
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if len(volume_series) < 10:
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return 'stable'
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# 计算短期和长期移动平均线
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short_ma = volume_series.tail(10).mean()
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long_ma = volume_series.tail(30).mean()
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if short_ma > long_ma * 1.1:
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return 'increasing'
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elif short_ma < long_ma * 0.9:
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return 'decreasing'
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else:
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return 'stable'
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def _analyze_oi_trend(self, oi_series: pd.Series) -> str:
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"""分析持仓量趋势"""
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if len(oi_series) < 10:
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return 'stable'
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# 计算短期和长期移动平均线
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short_ma = oi_series.tail(10).mean()
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long_ma = oi_series.tail(30).mean()
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if short_ma > long_ma * 1.1:
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return 'increasing'
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elif short_ma < long_ma * 0.9:
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return 'decreasing'
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else:
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return 'stable'
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def _estimate_bid_ask_spread(self, data: pd.DataFrame) -> float:
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"""估算买卖价差"""
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# 简化处理，使用收盘价的波动来估算
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price_volatility = data['close'].tail(20).std()
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# 假设价差为波动率的10%
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return price_volatility * 0.1
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def _calculate_liquidity_score(self, avg_volume: float, volume_trend: str,
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avg_open_interest: float, oi_trend: str,
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bid_ask_spread: float) -> float:
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"""计算流动性评分"""
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# 基础分数
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base_score = 100
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# 成交量因素
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if avg_volume < 1000:
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base_score -= 30
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elif avg_volume < 5000:
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base_score -= 15
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# 成交量趋势因素
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if volume_trend == 'decreasing':
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base_score -= 20
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elif volume_trend == 'increasing':
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base_score += 10
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# 持仓量因素
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if avg_open_interest < 5000:
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base_score -= 20
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elif avg_open_interest < 20000:
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base_score -= 10
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# 持仓量趋势因素
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if oi_trend == 'decreasing':
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base_score -= 15
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elif oi_trend == 'increasing':
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base_score += 5
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# 买卖价差因素
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if bid_ask_spread > 0.5:
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base_score -= 25
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elif bid_ask_spread > 0.2:
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base_score -= 10
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# 确保分数在0-100之间
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return max(0, min(100, base_score))
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def _calculate_liquidity_risk(self, liquidity_score: float) -> str:
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"""计算流动性风险"""
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if liquidity_score < 30:
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return 'high'
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elif liquidity_score < 60:
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return 'medium'
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else:
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return 'low'
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def _analyze_spread(self, symbol: str) -> Dict:
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"""分析价差"""
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# 简化处理，实际应该比较当前合约和下一个合约的价差
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# 这里返回模拟数据
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return {
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'current_next_spread': 5.2,
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'spread_trend': 'stable',
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'spread_ratio': 0.0015
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}
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def _generate_rollover_warning(self, delivery_info: Dict, liquidity_info: Dict) -> Dict:
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"""生成换月预警"""
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warning_level = delivery_info['warning_level']
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liquidity_risk = liquidity_info['liquidity_risk']
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# 综合预警
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overall_warning = 'none'
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if warning_level in ['critical', 'high'] or liquidity_risk == 'high':
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overall_warning = 'high'
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elif warning_level == 'medium' or liquidity_risk == 'medium':
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overall_warning = 'medium'
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elif warning_level == 'low':
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overall_warning = 'low'
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# 预警信息
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warning_message = self._generate_warning_message(warning_level, liquidity_risk)
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# 建议操作
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recommended_actions = self._generate_recommended_actions(warning_level, liquidity_risk)
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return {
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'overall_warning': overall_warning,
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'warning_message': warning_message,
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'recommended_actions': recommended_actions
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}
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def _generate_warning_message(self, warning_level: str, liquidity_risk: str) -> str:
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"""生成预警信息"""
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messages = []
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if warning_level == 'critical':
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messages.append('合约即将到期，距离交割日不足3天')
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elif warning_level == 'high':
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messages.append('合约接近到期，距离交割日不足7天')
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elif warning_level == 'medium':
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messages.append('合约距离交割日不足15天，建议开始关注换月')
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if liquidity_risk == 'high':
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messages.append('流动性风险较高，可能影响交易执行')
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elif liquidity_risk == 'medium':
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messages.append('流动性风险中等，建议谨慎交易')
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if not messages:
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return '合约状态正常，无需特殊关注'
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return '; '.join(messages)
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def _generate_recommended_actions(self, warning_level: str, liquidity_risk: str) -> List[str]:
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"""生成建议操作"""
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actions = []
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if warning_level in ['critical', 'high']:
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actions.append('立即开始换月操作')
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actions.append('逐步减仓当前合约')
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actions.append('在新合约建立相应仓位')
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elif warning_level == 'medium':
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actions.append('开始评估换月时机')
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actions.append('关注新合约流动性')
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if liquidity_risk == 'high':
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actions.append('减小单笔交易规模')
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actions.append('使用限价单而非市价单')
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actions.append('考虑提前换月')
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return actions
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def _generate_position_adjustment(self, delivery_info: Dict, liquidity_info: Dict) -> Dict:
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"""生成仓位调整建议"""
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days_to_delivery = delivery_info['days_to_delivery']
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warning_level = delivery_info['warning_level']
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liquidity_risk = liquidity_info['liquidity_risk']
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# 计算减仓比例
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reduction_ratio = self._calculate_reduction_ratio(days_to_delivery, warning_level, liquidity_risk)
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# 计算建议的减仓时间表
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reduction_schedule = self._generate_reduction_schedule(days_to_delivery, reduction_ratio)
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# 计算新合约建仓建议
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new_contract_adjustment = self._generate_new_contract_adjustment(reduction_ratio)
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return {
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'reduction_ratio': reduction_ratio,
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'reduction_schedule': reduction_schedule,
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'new_contract_adjustment': new_contract_adjustment
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}
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def _calculate_reduction_ratio(self, days_to_delivery: int, warning_level: str, liquidity_risk: str) -> float:
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"""计算减仓比例"""
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# 基础减仓比例
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base_ratio = 0.0
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if warning_level == 'critical':
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base_ratio = 0.9 # 减仓90%
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elif warning_level == 'high':
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base_ratio = 0.7 # 减仓70%
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elif warning_level == 'medium':
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base_ratio = 0.4 # 减仓40%
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elif warning_level == 'low':
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base_ratio = 0.2 # 减仓20%
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# 流动性风险调整
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if liquidity_risk == 'high':
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base_ratio = min(1.0, base_ratio + 0.2)
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elif liquidity_risk == 'medium':
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base_ratio = min(1.0, base_ratio + 0.1)
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return base_ratio
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def _generate_reduction_schedule(self, days_to_delivery: int, reduction_ratio: float) -> List[Dict]:
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"""生成减仓时间表"""
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schedule = []
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if days_to_delivery <= 3:
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# 紧急减仓
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schedule.append({
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'timeframe': '今日',
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'reduction_ratio': reduction_ratio
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})
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elif days_to_delivery <= 7:
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# 快速减仓
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daily_ratio = reduction_ratio / 3
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for i in range(3):
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schedule.append({
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'timeframe': f'{i+1}天内',
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'reduction_ratio': daily_ratio
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})
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elif days_to_delivery <= 15:
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# 逐步减仓
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daily_ratio = reduction_ratio / 5
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for i in range(5):
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schedule.append({
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'timeframe': f'{i+1}天内',
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'reduction_ratio': daily_ratio
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})
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elif days_to_delivery <= 30:
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# 缓慢减仓
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weekly_ratio = reduction_ratio / 2
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schedule.append({
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'timeframe': '第一周',
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'reduction_ratio': weekly_ratio
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})
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schedule.append({
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'timeframe': '第二周',
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'reduction_ratio': weekly_ratio
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})
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return schedule
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def _generate_new_contract_adjustment(self, reduction_ratio: float) -> Dict:
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"""生成新合约建仓建议"""
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# 建议在新合约建立与原合约相同方向的仓位
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# 建仓比例应与减仓比例对应
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return {
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'direction': 'same_as_current',
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'target_ratio': reduction_ratio,
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'execution_strategy': 'gradual',
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'considerations': [
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'关注新合约流动性',
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'注意合约间价差',
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'避免在换月高峰期交易'
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]
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}
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def monitor_rollover_risk(self, symbol: str, data: pd.DataFrame, position_size: float) -> Dict:
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"""监控换月风险"""
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# 分析换月情况
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rollover_analysis = self.analyze_rollover(symbol, data)
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# 计算风险暴露
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risk_exposure = self._calculate_risk_exposure(position_size, rollover_analysis)
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# 生成风险报告
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risk_report = {
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'symbol': symbol,
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'position_size': position_size,
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'rollover_analysis': rollover_analysis,
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'risk_exposure': risk_exposure,
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'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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}
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return risk_report
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def _calculate_risk_exposure(self, position_size: float, rollover_analysis: Dict) -> Dict:
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"""计算风险暴露"""
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warning_level = rollover_analysis['warning_level']
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liquidity_risk = rollover_analysis.get('liquidity_risk', 'low')
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# 基础风险分数
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base_risk = 0
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|
|
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if warning_level == 'critical':
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|
base_risk = 90
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elif warning_level == 'high':
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|
base_risk = 70
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elif warning_level == 'medium':
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|
base_risk = 40
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elif warning_level == 'low':
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base_risk = 20
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|
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# 流动性风险调整
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if liquidity_risk == 'high':
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|
base_risk += 20
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elif liquidity_risk == 'medium':
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base_risk += 10
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|
|
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# 仓位大小调整
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|
if position_size > 10:
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base_risk += 15
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elif position_size > 5:
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|
base_risk += 5
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|
|
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# 确保风险分数在0-100之间
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|
risk_score = max(0, min(100, base_risk))
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# 风险等级
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|
risk_level = 'low'
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if risk_score >= 80:
|
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|
risk_level = 'critical'
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|
|
elif risk_score >= 60:
|
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|
risk_level = 'high'
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|
|
elif risk_score >= 30:
|
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|
risk_level = 'medium'
|
|
|
|
|
|
return {
|
|
|
'risk_score': risk_score,
|
|
|
'risk_level': risk_level,
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|
|
'recommendations': self._generate_risk_recommendations(risk_level)
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}
|
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|
|
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def _generate_risk_recommendations(self, risk_level: str) -> List[str]:
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|
"""生成风险建议"""
|
|
|
recommendations = []
|
|
|
|
|
|
if risk_level == 'critical':
|
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|
recommendations.append('立即减仓至最小仓位')
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|
recommendations.append('优先处理换月操作')
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|
|
recommendations.append('密切监控市场流动性')
|
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|
elif risk_level == 'high':
|
|
|
recommendations.append('大幅减仓当前合约')
|
|
|
recommendations.append('加速换月进程')
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|
|
recommendations.append('使用限价单控制交易成本')
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|
|
elif risk_level == 'medium':
|
|
|
recommendations.append('开始有序减仓')
|
|
|
recommendations.append('评估换月时机')
|
|
|
recommendations.append('关注新合约表现')
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else:
|
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|
recommendations.append('保持正常交易')
|
|
|
recommendations.append('定期监控合约到期情况')
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|
|
|
|
|
return recommendations
|
