oficcejo
558 lines
20 KiB
Python
558 lines
20 KiB
Python
"""
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新闻流量模型计算模块
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核心公式：接盘总量 = 流量 × 转化率 × 客单价
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实现流量为王理念的量化分析
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"""
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import logging
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Tuple
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class NewsFlowModel:
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"""新闻流量模型计算器"""
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def __init__(self):
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# 平台类别权重（用于转化率计算）
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self.category_weights = {
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'finance': 1.5, # 财经平台转化率高
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'social': 1.2, # 社交媒体传播快
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'news': 1.0, # 新闻媒体正常
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'tech': 0.8, # 科技平台相关性低
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}
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# 话题类型转化率系数
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self.topic_weights = {
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# 高转化话题
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'政策': 2.0,
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'利好': 1.8,
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'涨停': 1.8,
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'龙头': 1.7,
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'机构': 1.6,
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'外资': 1.6,
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'北向': 1.6,
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'重组': 1.5,
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'并购': 1.5,
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'IPO': 1.5,
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# 中转化话题
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'业绩': 1.3,
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'财报': 1.3,
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'板块': 1.2,
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'概念': 1.2,
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'题材': 1.2,
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# 一般话题
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'股票': 1.0,
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'股市': 1.0,
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'A股': 1.0,
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}
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# 基础转化率（万分之一）
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self.base_conversion_rate = 0.0001
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# 平均客单价（元）- 散户平均投资金额
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self.avg_investment = 50000
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# 时效因子衰减系数（每小时衰减）
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self.time_decay_rate = 0.95
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def calculate_traffic_score(self, platforms_data: List[Dict]) -> Dict:
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"""
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计算流量分数
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流量分数 = Σ(平台权重 × 热度 × 时效因子)
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Args:
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platforms_data: 平台数据列表
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Returns:
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{
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'total_score': int,
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'category_scores': Dict[str, int],
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'platform_details': List[Dict],
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'normalized_score': int, # 归一化到0-1000
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}
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"""
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category_scores = {
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'social': 0,
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'news': 0,
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'finance': 0,
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'tech': 0,
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}
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platform_details = []
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total_raw_score = 0
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for platform_data in platforms_data:
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if not platform_data.get('success'):
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continue
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category = platform_data.get('category', 'other')
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weight = platform_data.get('weight', 5)
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count = platform_data.get('count', 0)
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platform_name = platform_data.get('platform_name', '')
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# 计算平台得分：权重 × 新闻数量 × 类别权重
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category_weight = self.category_weights.get(category, 1.0)
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platform_score = weight * count * category_weight
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category_scores[category] = category_scores.get(category, 0) + platform_score
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total_raw_score += platform_score
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platform_details.append({
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'platform': platform_data.get('platform', ''),
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'platform_name': platform_name,
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'category': category,
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'count': count,
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'weight': weight,
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'score': int(platform_score),
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})
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# 归一化到0-1000
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normalized_score = min(int(total_raw_score / 50), 1000)
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return {
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'total_score': int(total_raw_score),
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'normalized_score': normalized_score,
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'category_scores': {k: int(v) for k, v in category_scores.items()},
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'platform_details': platform_details,
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}
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def estimate_conversion_rate(self, hot_topics: List[Dict],
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category_distribution: Dict[str, int]) -> Dict:
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"""
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估算转化率
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转化率 = 基础转化率 × 话题系数 × 平台系数
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Args:
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hot_topics: 热门话题列表
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category_distribution: 类别得分分布
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Returns:
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{
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'conversion_rate': float,
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'topic_factor': float,
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'platform_factor': float,
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'analysis': str,
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}
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"""
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# 1. 计算话题系数（基于热门话题的类型）
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topic_factor = 1.0
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matched_topics = []
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for topic in hot_topics[:10]: # 只看TOP10热门话题
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topic_text = topic.get('topic', '')
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topic_heat = topic.get('heat', 0)
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for keyword, weight in self.topic_weights.items():
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if keyword in topic_text:
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# 热度加权
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heat_bonus = 1 + (topic_heat / 100) * 0.5
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topic_factor = max(topic_factor, weight * heat_bonus)
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matched_topics.append({
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'topic': topic_text,
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'keyword': keyword,
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'factor': weight,
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})
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break
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# 2. 计算平台系数（基于类别分布）
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total_score = sum(category_distribution.values())
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if total_score > 0:
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platform_factor = sum(
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(score / total_score) * self.category_weights.get(cat, 1.0)
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for cat, score in category_distribution.items()
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)
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else:
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platform_factor = 1.0
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# 3. 计算最终转化率
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conversion_rate = self.base_conversion_rate * topic_factor * platform_factor
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# 4. 生成分析
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if conversion_rate >= 0.0003:
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analysis = f"转化率极高({conversion_rate:.4%})！话题与股市高度相关，投资者关注度极高。"
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elif conversion_rate >= 0.0002:
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analysis = f"转化率较高({conversion_rate:.4%})。话题具有较强的股市联动性。"
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elif conversion_rate >= 0.0001:
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analysis = f"转化率正常({conversion_rate:.4%})。话题与股市有一定关联。"
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else:
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analysis = f"转化率较低({conversion_rate:.4%})。话题与股市关联度不高。"
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return {
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'conversion_rate': conversion_rate,
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'topic_factor': round(topic_factor, 2),
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'platform_factor': round(platform_factor, 2),
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'matched_topics': matched_topics,
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'analysis': analysis,
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}
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def calculate_potential(self, flow_score: int, conversion_rate: float,
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avg_investment: float = None) -> Dict:
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"""
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计算接盘潜力
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核心公式：接盘总量 = 流量 × 转化率 × 客单价
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Args:
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flow_score: 流量分数（代表潜在触达人数，按比例换算）
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conversion_rate: 转化率
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avg_investment: 平均客单价（元）
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Returns:
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{
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'potential_volume': float, # 接盘总量（亿元）
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'potential_level': str, # 潜力等级
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'estimated_participants': int, # 预估参与人数
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'analysis': str,
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}
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"""
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if avg_investment is None:
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avg_investment = self.avg_investment
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# 流量分数换算为潜在触达人数（假设满分1000对应1000万人）
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potential_reach = flow_score * 10000 # 分数 × 10000 = 潜在触达人数
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# 预估参与人数
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estimated_participants = int(potential_reach * conversion_rate)
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# 接盘总量 = 参与人数 × 平均投资额（转换为亿元）
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potential_volume = (estimated_participants * avg_investment) / 100000000
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# 确定潜力等级
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if potential_volume >= 100:
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potential_level = "超大"
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analysis = f"预估接盘资金{potential_volume:.1f}亿元，市场资金充裕，热点题材可能持续发酵。"
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elif potential_volume >= 50:
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potential_level = "大"
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analysis = f"预估接盘资金{potential_volume:.1f}亿元，资金量较大，可支撑短期行情。"
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elif potential_volume >= 20:
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potential_level = "中"
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analysis = f"预估接盘资金{potential_volume:.1f}亿元，资金量适中，行情可能分化。"
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elif potential_volume >= 5:
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potential_level = "小"
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analysis = f"预估接盘资金{potential_volume:.1f}亿元，资金量较小，注意风险。"
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else:
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potential_level = "极小"
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analysis = f"预估接盘资金{potential_volume:.1f}亿元，资金量不足，不建议追高。"
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return {
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'potential_volume': round(potential_volume, 2),
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'potential_level': potential_level,
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'potential_reach': potential_reach,
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'estimated_participants': estimated_participants,
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'avg_investment': avg_investment,
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'analysis': analysis,
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}
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def classify_flow_type(self, history_scores: List[int],
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current_score: int) -> Dict:
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"""
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判断流量类型
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存量流量型：出生自带顶流，流量快速到位（政策/大事件）
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增量流量型：初始流量小，具备病毒传播能力（话题发酵）
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Args:
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history_scores: 历史得分列表（从旧到新）
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current_score: 当前得分
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Returns:
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{
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'flow_type': str,
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'characteristics': List[str],
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'time_window': str,
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'operation': str,
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'confidence': int,
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}
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"""
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if len(history_scores) < 2:
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return {
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'flow_type': '未知',
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'characteristics': ['历史数据不足，无法判断'],
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'time_window': '无法判断',
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'operation': '继续观察，积累数据',
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'confidence': 0,
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}
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# 计算关键指标
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initial_score = history_scores[0]
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avg_score = sum(history_scores) / len(history_scores)
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max_score = max(history_scores)
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# 计算增长率序列
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growth_rates = []
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for i in range(1, len(history_scores)):
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if history_scores[i-1] > 0:
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rate = (history_scores[i] - history_scores[i-1]) / history_scores[i-1]
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growth_rates.append(rate)
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avg_growth = sum(growth_rates) / len(growth_rates) if growth_rates else 0
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positive_growth_count = len([r for r in growth_rates if r > 0])
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# 判断流量类型
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if initial_score >= 500 and (current_score - initial_score) / (initial_score + 1) < 0.3:
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# 初始热度高，增长有限 -> 存量流量型
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flow_type = "存量流量型"
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characteristics = [
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f"初始热度高（{initial_score}分）",
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"流量快速到位",
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"可能与政策/大事件相关",
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"来得快去得也快",
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]
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time_window = "时间窗口短（2-3天）"
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operation = "快进快出，首日参与最佳，及时止盈"
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confidence = 80
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elif avg_growth > 0.15 and positive_growth_count >= len(growth_rates) * 0.6:
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# 持续增长 -> 增量流量型
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flow_type = "增量流量型"
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characteristics = [
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f"初始热度较低（{initial_score}分）",
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f"增长率{avg_growth*100:.1f}%",
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"逐步攀升，病毒式传播",
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"有埋伏机会",
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]
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time_window = "时间窗口长（5-10天）"
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operation = "可以埋伏，等待加速，分批建仓"
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confidence = 75
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elif avg_growth < -0.1:
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# 持续下降 -> 衰退期
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flow_type = "流量衰退"
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characteristics = [
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"热度持续下降",
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f"平均跌幅{abs(avg_growth)*100:.1f}%",
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"题材热度消退",
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]
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time_window = "窗口已关闭"
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operation = "及时止盈止损，不宜追入"
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confidence = 70
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else:
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# 波动 -> 常规流量
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flow_type = "常规流量"
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characteristics = [
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"热度波动正常",
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"无明显趋势",
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]
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time_window = "无特定窗口"
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operation = "保持观望，等待方向明确"
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confidence = 50
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return {
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'flow_type': flow_type,
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'characteristics': characteristics,
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'time_window': time_window,
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'operation': operation,
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'confidence': confidence,
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'initial_score': initial_score,
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'current_score': current_score,
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'avg_growth': round(avg_growth * 100, 1),
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'history_length': len(history_scores),
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}
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def calculate_viral_k(self, current_score: int, previous_score: int) -> Dict:
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"""
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计算病毒系数K值
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K值 = 当前传播量 / 上期传播量
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K > 1.5: 指数型爆发
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K ≈ 1: 线性增长
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K < 1: 自然死亡
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Returns:
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{
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'k_value': float,
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'trend': str,
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'risk_level': str,
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'analysis': str,
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}
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"""
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if previous_score == 0:
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return {
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'k_value': 1.0,
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'trend': '无历史数据',
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'risk_level': '未知',
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'analysis': '首次采集，无法计算K值',
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}
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k_value = round(current_score / previous_score, 2)
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if k_value > 2.0:
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trend = "爆发式增长"
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risk_level = "高风险"
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analysis = f"K值={k_value}，流量正在爆发式增长！这是极端情况，可能接近顶部，注意风险。"
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elif k_value > 1.5:
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trend = "指数型爆发"
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risk_level = "中高风险"
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analysis = f"K值={k_value}，流量指数型增长！题材正在加速发酵，关注龙头但注意追高风险。"
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elif k_value > 1.2:
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trend = "快速增长"
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risk_level = "中风险"
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analysis = f"K值={k_value}，流量快速增长，题材正在升温，可适度参与。"
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elif k_value > 1.0:
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trend = "稳步增长"
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risk_level = "低风险"
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analysis = f"K值={k_value}，流量稳步增长，题材处于发酵期。"
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elif k_value == 1.0:
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trend = "平稳"
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risk_level = "低风险"
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analysis = f"K值={k_value}，流量保持稳定，市场平衡状态。"
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elif k_value > 0.8:
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trend = "轻微下降"
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risk_level = "中风险"
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analysis = f"K值={k_value}，流量轻微下降，题材热度开始消退。"
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else:
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trend = "快速衰减"
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risk_level = "高风险"
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analysis = f"K值={k_value}，流量快速衰减！题材进入退潮期，注意及时止盈止损。"
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return {
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'k_value': k_value,
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'current_score': current_score,
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'previous_score': previous_score,
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'trend': trend,
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'risk_level': risk_level,
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'analysis': analysis,
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}
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def run_full_model(self, platforms_data: List[Dict],
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hot_topics: List[Dict],
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history_scores: List[int] = None) -> Dict:
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"""
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运行完整的流量模型分析
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Returns:
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{
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'traffic': Dict, # 流量分析
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'conversion': Dict, # 转化率分析
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'potential': Dict, # 接盘潜力
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'flow_type': Dict, # 流量类型
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'viral_k': Dict, # K值分析
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'summary': str, # 总结
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}
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"""
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# 1. 计算流量分数
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traffic = self.calculate_traffic_score(platforms_data)
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current_score = traffic['normalized_score']
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# 2. 估算转化率
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conversion = self.estimate_conversion_rate(
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hot_topics,
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traffic['category_scores']
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)
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# 3. 计算接盘潜力
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potential = self.calculate_potential(
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current_score,
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conversion['conversion_rate']
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)
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# 4. 判断流量类型
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if history_scores and len(history_scores) >= 2:
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flow_type = self.classify_flow_type(history_scores, current_score)
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# 计算K值（与上一次对比）
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viral_k = self.calculate_viral_k(current_score, history_scores[-1])
|
||
else:
|
||
flow_type = {
|
||
'flow_type': '未知',
|
||
'characteristics': ['历史数据不足'],
|
||
'time_window': '无法判断',
|
||
'operation': '继续观察',
|
||
'confidence': 0,
|
||
}
|
||
viral_k = {
|
||
'k_value': 1.0,
|
||
'trend': '无历史数据',
|
||
'risk_level': '未知',
|
||
'analysis': '首次分析，无法计算K值',
|
||
}
|
||
|
||
# 5. 生成总结
|
||
summary = self._generate_summary(traffic, conversion, potential, flow_type, viral_k)
|
||
|
||
return {
|
||
'traffic': traffic,
|
||
'conversion': conversion,
|
||
'potential': potential,
|
||
'flow_type': flow_type,
|
||
'viral_k': viral_k,
|
||
'summary': summary,
|
||
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||
}
|
||
|
||
def _generate_summary(self, traffic: Dict, conversion: Dict,
|
||
potential: Dict, flow_type: Dict, viral_k: Dict) -> str:
|
||
"""生成分析总结"""
|
||
lines = []
|
||
|
||
# 流量概况
|
||
score = traffic['normalized_score']
|
||
if score >= 800:
|
||
lines.append(f"【流量】当前流量分数{score}，处于极高水平")
|
||
elif score >= 500:
|
||
lines.append(f"【流量】当前流量分数{score}，处于较高水平")
|
||
elif score >= 200:
|
||
lines.append(f"【流量】当前流量分数{score}，处于正常水平")
|
||
else:
|
||
lines.append(f"【流量】当前流量分数{score}，处于低位")
|
||
|
||
# 转化率
|
||
lines.append(f"【转化】{conversion['analysis']}")
|
||
|
||
# 接盘潜力
|
||
lines.append(f"【潜力】{potential['analysis']}")
|
||
|
||
# 流量类型
|
||
if flow_type['flow_type'] != '未知':
|
||
lines.append(f"【类型】{flow_type['flow_type']}，{flow_type['operation']}")
|
||
|
||
# K值
|
||
if viral_k['k_value'] != 1.0 or viral_k['trend'] != '无历史数据':
|
||
lines.append(f"【趋势】K值={viral_k['k_value']}，{viral_k['trend']}")
|
||
|
||
return '\n'.join(lines)
|
||
|
||
|
||
# 全局实例
|
||
news_flow_model = NewsFlowModel()
|
||
|
||
|
||
# 测试代码
|
||
if __name__ == "__main__":
|
||
print("=== 测试新闻流量模型 ===")
|
||
|
||
# 模拟数据
|
||
platforms_data = [
|
||
{'success': True, 'platform': 'weibo', 'platform_name': '微博',
|
||
'category': 'social', 'weight': 10, 'count': 50},
|
||
{'success': True, 'platform': 'eastmoney', 'platform_name': '东方财富',
|
||
'category': 'finance', 'weight': 9, 'count': 30},
|
||
{'success': True, 'platform': 'baidu', 'platform_name': '百度',
|
||
'category': 'news', 'weight': 8, 'count': 40},
|
||
]
|
||
|
||
hot_topics = [
|
||
{'topic': 'AI芯片', 'heat': 95, 'count': 50},
|
||
{'topic': '新能源政策', 'heat': 80, 'count': 30},
|
||
{'topic': '涨停板', 'heat': 75, 'count': 25},
|
||
]
|
||
|
||
history_scores = [300, 350, 420, 500]
|
||
|
||
# 运行完整分析
|
||
result = news_flow_model.run_full_model(platforms_data, hot_topics, history_scores)
|
||
|
||
print(f"\n流量分数: {result['traffic']['normalized_score']}")
|
||
print(f"转化率: {result['conversion']['conversion_rate']:.4%}")
|
||
print(f"接盘潜力: {result['potential']['potential_volume']:.1f}亿元 ({result['potential']['potential_level']})")
|
||
print(f"流量类型: {result['flow_type']['flow_type']}")
|
||
print(f"K值: {result['viral_k']['k_value']} ({result['viral_k']['trend']})")
|
||
print(f"\n===总结===\n{result['summary']}")
|